GB2587288A - Tissue analysis by mass spectrometry or ion mobility spectrometry - Google Patents

Abstract

Described is a method of analysis of bacteria in tissue samples using mass spectrometry. The method comprises using a first device to generate aerosol or vapour from one or more regions of a first target of biological material. The device comprises a desorption electrospray ionisation (“DESI”) ion source or a desorption electro-flow focusing (“DEFFI”) ion source. The biological material is a specimen from an animal subject. The aerosol or vapour is analysed by mass spectrometry or ion mobility spectrometry. The spectrometric data obtained is used to identify bacteria or microbes in the sample. Also described are apparatus for carrying out the method.

Description

TISSUE ANALYSIS BY MASS SPECTROMETRY OR ION

MOBILITY SPECTROMETRY

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from and the benefit of United Kingdom patent application No. 1503876.3 filed on 6 March 2015, United Kingdom patent application No. 1503864.9 filed on 6 March 2015, United Kingdom patent application No. 1518369.2 filed on 16 October 2015, United Kingdom patent application No. 1503877.1 filed on 6 March 2015, United Kingdom patent application No. 1503867.2 filed on 6 March 2015, United Kingdom patent application No. 1503863.1 filed on 6 March 2015, United Kingdom patent application No. 1503878.9 filed on 6 March 2015, United Kingdom patent application No. 1503879.7 filed on 6 March 2015 and United Kingdom patent application No. 1516003.9 filed on 9 September 2015. The entire contents of these applications are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to mass spectrometry and/or ion mobility spectrometry, and in particular to methods of in vivo, ex vivo or in vitro specimen and/or tissue 20 analysis.

BACKGROUND

Cancers figure among the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer related deaths in 2012. According to 25 the World Health Organisation, the number of new cases is expected to rise by about 70% over the next 2 decades.

Gastro-intestinal cancers are a leading cause of mortality and account for 23% of cancer-related deaths worldwide.

Mamma carcinoma is a carcinoma of breast tissue. Worldwide it is the most common form of cancer in women, affecting approximately 10% of all females at some stage of their life (in the Western world). Although significant efforts have been made to achieve early detection and effective treatment, about 20% of all women with breast cancer still die from the disease. Mamma carcinoma is the second most common cause of cancer deaths in women.

In order to improve outcomes from cancers and other diseases, novel tissue characterisation methods are needed in order to facilitate accurate diagnosis.

A common treatment option is surgery. Current surgical methods rely on the trained eye of the surgeon, sometimes with the help of an operating microscope and/or imaging from scans performed before the surgery.

The main goal of tumour surgery is to maximize tumour resection while preserving as much of the healthy tissue, and its function, as possible. However, using existing techniques it can be difficult or impossible to delineate tumour boundaries. Similar considerations apply to surgery of necrotic tissue.

Surgical resection therefore typically involves the removal of apparently normal tissue as a "safety margin", but this can increase morbidity and risk of complications. Moreover, there is a risk of the "safety margin" being too small, leaving cancerous or necrotic tissue behind. For example, up to 40 percent of subjects undergoing breast cancer surgery require additional operations because surgeons may fail to remove all the cancerous tissue in the initial operation. There is therefore a need for a tool that will help surgeons better distinguish cancerous tissue from normal tissue, thereby decreasing the risk of the need for repeat operations.

There is also a need for novel methods to facilitate accurate diagnosis and/or treatment of further diseases such as necrosis, or inflammatory conditions.

There is also a need for novel methods to detect infections and/or to analyse microbial interactions with one another and/or with a host.

Mass spectrometry imaging ("MSI") analysis of biological samples is known and allows simultaneous and spatially resolved detection of metabolites, proteins and lipids directly from biological tissue sections.

The technique has gained significant momentum during the course of the last two decades with the introduction of new techniques such as matrix assisted laser desorption/ionization ("MALDI"), secondary ion mass spectrometry ("SIMS") and desorption electrospray ionization ("DESI").

The spatially resolved nature of the resulting data allows its use as a supplemental layer of information for histopathological characterization and classification of tissues including the possibility of cancer biomarker discovery.

Rapid evaporative ionization mass spectrometry ("REIMS") is a technology which has recently been developed for the real time identification of tissues during surgical interventions. Coupling of RUMS technology with handheld sampling devices has resulted in iKnife sampling technology, which can provide intra-operative tissue identification. The iKnife sampling technology allows surgeons to more efficiently resect tumours intra-operatively through minimizing the amount of healthy tissue removed whilst ensuring that all the cancerous tissue is removed.

REIMS analysis of biological tissue has been shown to yield phospholipid profiles showing high histological and histopathological specificity -similar to Matrix Assisted Laser Desorption Ionisation ("MALDI"), Secondary Ion Mass Spectrometry ("SIMS") and Desorption Electrospray Ionisation ("DESI") imaging. A mass spectrometric signal is obtained by subjecting the cellular biomass to alternating electric current at radiofrequency which causes localized Joule-heating and the disruption of cells along with desorption of charged and neutral particles. The resulting aerosol or surgical smoke is then transported to a mass spectrometer for on-line mass spectrometric analysis.

In this process, cellular biomass is held between the tips of the forceps and an electric current is applied causing the cells to undergo thermal disintegration and release a partially charged aerosol that is transported to a mass spectrometer.

REIMS profiling applications typically require a spectral library of reference mass spectra in order to build multivariate classification models which are necessary for pattern-based identification.

The collection of reference mass spectra using iKnife sampling technology is carried out by manual electrosurgical sampling of ex-vivo tissue specimens followed by the histopathological examination of the remaining material. Although the workflow provides satisfactory data, there is a degree of uncertainty involved at the validation step since the tissue part producing the spectral data cannot be investigated since it is evaporated during the course of the analysis. Hence, conventionally all identifications are based on interpolation of the histological environment of the evaporated tissue.

SUMMARY

The invention provides a method of mass and/or ion mobility spectrometry comprising; using a first device to generate aerosol. smoke or vapour from one or more regions of a target; and mass analysing and/or ion mobility analysing said aerosol, smoke or vapour or ions derived therefrom.

The invention also provides a method of analysis using mass and/or ion mobility spectrometry comprising; (a) using a first device to generate to generate aerosol, smoke or vapour from one or more regions of a target; (b) mass analysing and/or ion mobility analysing said aerosol, smoke or vapour or ions derived therefrom in order to obtain spectrometric data; and (c) analysing said spectrometric data in order to analyse said target.

Embodiments of the invention also provide methods of analysis, diagnosis, prognosis, monitoring, stratification, treatment, and/or surgery.

Details of embodiments of the methods are discussed in the detailed description. Optional features of any of these methods are discussed below. Thus, unless otherwise stated, any reference to "a method" or the method" is intended to be a reference to any of the methods of the invention listed herein. It is explicitly intended that any of these features may be present in any combination in any of these methods.

Various embodiments are contemplated wherein analyte ions are generated from the target, aerosol, smoke or vapour, e.g., by an ambient ionisation ion source. The analyte ions, or ions derived therefrom, may be subjected either to: (i) mass analysis by a mass analyser such as a quadrupole mass analyser or a Time of Flight mass analyser; (ii) ion mobility analysis (IMS) and/or differential ion mobility analysis (DMA) and/or Field Asymmetric Ion Mobility Spectrometry (FAIMS) analysis; and/or (iii) a combination of firstly ion mobility analysis (IMS) and/or differential ion mobility analysis (DMA) and/or Field Asymmetric Ion Mobility Spectrometry (FAIMS) analysis followed by secondly mass analysis by a mass analyser such as a quadrupole mass analyser or a Time of Flight mass analyser (or vice versa). Various embodiments also relate to an ion mobility spectrometer and/or mass analyser and a method of ion mobility spectrometry and/or method of mass analysis.

Obtaining the spectrometric data may comprise recording the ion signal intensity of the ions derived from the smoke, aerosol or vapour as a function of one or more physicochemical property (or as a function of a property related thereto). For example, the ion signal intensity may be recorded as a function of mass to charge ratio and/or ion mobility. The location and/or size and/or pattern of peaks in this recorded ion signal may then be used to characterise or identify one or more analytes present in the smoke, aerosol or vapour.

Tandem mass spectrometry may be used to assign an analyte/compound to each of the peaks. For example, parent ions having a physicochemical property (e.g., mass to charge ratio) corresponding to that of a peak may be isolated (e.g., using a mass filter) and then fragmented or reacted so as to produce fragment or product ions. These fragment or product ions may then be analysed (e.g., by mass analysis) and their determined properties used to identify the parent ion giving rise to the peak in the ion signal. Such tandem mass spectrometry may be used, for example, to identify biomarkers in the spectrometric data.

The mass and/or ion mobility spectrometer may obtain data in negative ion mode only, positive ion mode only, or in both positive and negative ion modes. Positive ion mode spectrometric data may be combined or concatenated with negative ion mode spectrometric data. Negative ion mode can provide particularly useful spectra for classifying aerosol, smoke or vapour samples, such as aerosol, smoke or vapour samples from targets comprising lipids.

Ion mobility spectrometric data may be obtained using different ion mobility drift gases, or dopants may be added to the drift gas to induce a change in drift time of one or more species. This data may then be combined or concatenated. Other embodiments are contemplated wherein the first device for generating aerosol, smoke or vapour from the target may comprise an argon plasma coagulation ("AFC") device. An argon plasma coagulation device involves the use of a jet of ionised argon gas (plasma) that is directed through a probe. The probe may be passed through an endoscope. Argon plasma coagulation is essentially a non-contact process as the probe is placed at some distance from the target. Argon gas is emitted from the probe and is then ionized by a high voltage discharge (e.g., 6 kV). High-frequency electric current is then conducted through the jet of gas, resulting in coagulation of the target on the other end of the jet. The depth of coagulation is usually only a few millimetres.

The first device, surgical or electrosurgical tool, device or probe or other sampling device or probe disclosed in any of the aspects or embodiments herein may comprise a non-contact surgical device, such as one or more of a hydrosurgical device, a surgical water jet device, an argon plasma coagulation device, a hybrid argon plasma coagulation device, a water jet device and a laser device.

A non-contact surgical device may be defined as a surgical device arranged and adapted to dissect, fragment, liquefy, aspirate, fulgurate or otherwise disrupt biologic tissue without physically contacting the tissue. Examples include laser devices, hydrosurgical devices, argon plasma coagulation devices and hybrid argon plasma coagulation devices.

As the non-contact device may not make physical contact with the tissue, the procedure may be seen as relatively safe and can be used to treat delicate tissue having low intracellular bonds, such as skin or fat.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will now be described, by way of example only, and with reference to the accompanying drawings in which: Fig. 1A shows an endoscopic experimental setup according to an embodiment wherein smoke, aerosol or vapour generated by an electrosurgical electrode tip is analysed by a mass and/or ion mobility spectrometer, and Fig. 1B shows a resection of a GI polyp according to an embodiment of the invention; Fig. 2 shows an embodiment of the interface between the electrosurgical device and the mass and/or ion mobility spectrometer; Fig. 3 illustrates a method of REIMS wherein an RF voltage is applied to bipolar forceps, resulting in the generation of smoke, aerosol or vapour, which is then analysed by a mass and/or ion mobility spectrometer; Fig. 4 illustrates the technique of Desorption Electrospray Ionisation ("DESI") according to various embodiments; Fig. 5a shows results of Example 1: PCA analysis of Grade II invasive ductal carcinoma (IDC) in negative ion mode; Fig. 5b shows results of Example 1: MMC analysis of Grade II IDC negative ion mode; Fig. 6a shows results of Example 1: PCA analysis of Grade II IDC in positive ion mode; Fig. 6b shows results of Example 1: MMC analysis of Grade II IDC positive ion mode; Fig. 7a and b shows results of Example 1: Leave one out cross validation of different tissue types in a Grade II IDC in negative ion mode (7a) and (7b) in positive ion mode; Fig. 8 shows results of Example 2: Analysis of a combined dataset from multiple samples (negative ion mode). a) PCA of identified regions; b) MMC supervised analysis; c) MMC analysis excluding the samples with outliers identified in b); d) respective leave-oneregion-per-patient-out cross validation; Fig. 9 shows results of Example 2: a) Supervised MMC analysis of healthy ovary, borderline tumours and carcinomas together with b) leave one patient out cross validation; Fig 10 shows results of Example 2: a) Supervised MMC analysis of healthy ovary and different epithelial carcinomas (endometrioid and serous) with the respective b) leave one patient out cross validation; Fig 11 shows results of Example 2 A sample with unknown histology was used to predict the different tissue types. Serous carcinoma, serous carcinoma associated stroma, normal ovarian stroma and background were correctly predicted. Cross validation of this prediction based on the histological annotation was performed and a classification accuracy of almost 100% was achieved; Fig 12 shows data from Example 3: cut mode (normal tissue from 61 patients, 280 spectra, tumour tissue from 37 patients, 80 spectra); Fig. 13 shows data from Example 3: Coagulation mode (normal tissue from 66 patients, 281 spectra, tumour tissue from 31 patients, 59 spectra); Fig. 14 shows an example of a margin test run across a mastectomy sample (Example Fig. 15 shows data from Example 6. Linear discriminant analysis showing separation of tissue that is borderline margin between normal and cancer, and between normal, borderline and ovarian lesions; Fig. 16 shows results from Example 8, which provides more detail on this Figure; Fig. 17 shows results from Example 9, which provides more detail on this Figure; Fig. 18 shows results from Example 10, which provides more detail on this Figure; Fig. 19 shows results of Example 11. DESI-MS image displaying tissue type distribution in a colorectal tissue specimen; In the original picture, tumour tissue was shown in green and stroma tissue in red. In the black and white version, tumour tissue is shown in light grey and stroma tissue in darker grey; B) H&E stained and histopathologically annotated section postDESI; Fig. 20 shows results of Example 11. Full scan mass spectra for colorectal adenocarcinoma, tumour surrounding stroma and necrotic tissue of same tissue section shown in Figure 19. Stars indicate major taxonomic markers; Fig. 21 shows results of Example 11. Single ion images and representative intensity distribution plots for known and confirmed homologous sphingolipid species that showed specificity as taxonomic markers; Fig. 22 shows results of Example 11. Single ion images and intensity selected distribution plots for other taxonomical markers; Fig. 23 (a) and (b) show results of Example 12; Fig. 24 shows results of Example 13; Fig 25 shows a_spectrum observed when analysing stool samples using rapid evaporative ionisation mass spectrometry ("REIMS") analysis; Fig. 26 shows schematically a variety of microbes that are present in the human microbiome; Fig. 27 shows schematically various mucosa or mucosal membranes which are present in the human body; Fig. 28 shows schematically a mucosa or mucosal membrane comprising biological tissue and bacteria; Fig. 29 shows schematically how analytes present in a mucosa may be useful in identifying a number of clinical disorders; Fig. 30 shows schematically how metabolomic profiling of analytes from a mucosal membrane can be useful in identifying clinical disorders such as allergies, inflammation and pre-term delivery; Fig. 31 shows various approaches for microbial analysis together with a real time rapid and direct analysis method using ambient mass spectrometry according to various embodiments; Fig. 32 shows schematically mucosal membrane sampling from selected parts of the human body (e.g., urogenital tract, oral or nose cavity) using medical cotton swabs as a sampling device wherein the surface of the medical swab may then be directly analysed by desorption electrospray ionisation ("DESI") mass spectrometry without prior sample preparation procedures according to various embodiments; Fig. 33A shows averaged negative-ion desorption electrospray ionisation ("DESI") mass spectra from vaginal, oral and nasal mucosa recorded using a Xevo G2-S Q-Tof (RTM) mass spectrometer, and Fig. 33B shows a PGA and MMC score plot for vaginal (n=68, shown as shaded circles), oral (n=15, shown as white-filled circles) and nasal (n=20, shown as black-filled circles) mucosa acquired with desorption electrospray ionisation ("DESI") mass spectrometry; Fig. 34 shows desorption electrospray ionisation ("DESI") mass spectrometry spectra of vaginal, oral and nasal mucosal membranes in a negative ion mode obtained from medical cotton swabs, together with principal component analysis (PCA) and maximum margin criterion analysis providing a separation between different mucosal classes (nasal, oral, vaginal) with a prediction accuracy ranging from 92-100% obtained by leave one out cross validation; Fig. 35 shows a desorption electrospray ionisation ("DESI") mass spectrum of pregnant vaginal mucosal membrane obtained in negative ion mode from a medical cotton swab, wherein the urogenital mucosa was found to produce cholesterol sulphate Em-Hr having a mass to charge ratio of 465.41 as the most abundant lipid species as well as a different glycerophosholipids species such as glycerophosphoethanolamine (PE) [PE(40:7)-1-1]" having a mass to charge ratio of 788.50, glycerophosphoserine (PS) [PS(34:1)-1-l]" having a mass to charge ratio of 760.50 and glycerophosphoinositol (PI) [P1(36:1)-Fl]" having a mass to charge ratio of 863.58; Fig. 36A shows averaged desorption electrospray ionisation ("DESI") mass spectra from a pregnant and a non-pregnant group acquired in negative ion mode in the mass range m/z 150-1000, Fig. 36B shows principal component analysis and discriminatory analysis using recursive maximum margin criterion ("RMMC"), Fig. 36C shows analysis with leave-one-out cross-validation for enhanced separation of group classes with highly accurate identification (>80 %) based on chemical signatures in the vaginal mucosal membrane, Fig. 36D shows box plots indicating significant differences of the abundance for selected peaks between nonpregnant and pregnant vaginal mucosal membranes mainly in the mass to charge ratio ("m/z") range 550-1000, and Fig. 36E shows the leave-one-out cross-validation; Fig. 37A shows desorption electrospray ionisation ("DESI") spectrometric analysis of a bacteria sample on a swab in accordance with various embodiments and shows that bacterial samples can be detected using DESI, and Fig. 37B shows a comparison with rapid evaporative ionisation mass spectrometry ("REIMS") analysis in conjunction with a Time of Flight mass analysis of a bacterial sample directly from an agar plate; Fig. 38A shows averaged desorption electrospray ionisation ("DESI") mass spectra of diverse analysed microorganism species including Candida albicans, Pseudomonas mantel'', Staphylococcus epidermis, Moraxella catarrhalis, Klebsiella pneumonia and Lactobacillus sp as well as pregnant vaginal mucosa, and Figs. 38B and 38C show PCA plots showing a separation between the vaginal mucosa (pregnant and non-pregnant group) from the microorganism species within the first two components, and a separation between the different bacteria and fungi species; Fig. 39 shows schematically desorption electrospray ionisation ("DESI") mass spectrometry analysis, rapid evaporative ionisation mass spectrometry ("REIMS") mass spectrometry analysis and culturing based analysis of a sample on a swab according to various 40 embodiments; Fig. 40 shows a method of analysis that comprises building a classification model according to various embodiments; Fig. 41 shows a set of reference sample spectra obtained from two classes of known reference samples; Fig. 42 shows a multivariate space having three dimensions defined by intensity axes, wherein the multivariate space comprises plural reference points, each reference point corresponding to a set of three peak intensity values derived from a reference sample spectrum; Fig. 43 shows a general relationship between cumulative variance and number of components of a PCA model; Fig. 44 shows a PCA space having two dimensions defined by principal component axes. wherein the PCA space comprises plural transformed reference points or scores. each transformed reference point or score corresponding to a reference point of Fig. 42; Fig. 45 shows a PCA-LDA space having a single dimension or axis, wherein the LDA is performed based on the PCA space of Fig. 44, the PCA-LDA space comprising plural further transformed reference points or class scores, each further transformed reference point or class score corresponding to a transformed reference point or score of Fig. 44.

Fig. 46 shows a method of analysis that comprises using a classification model according to various embodiments; Fig. 47 shows a sample spectrum obtained from an unknown sample; Fig. 48 shows the PCA-LDA space of Fig. 45, wherein the PCA-LDA space further comprises a PCA-LDA projected sample point derived from the peak intensity values of the sample spectrum of Fig. 47; Fig. 49 shows a method of analysis that comprises building a classification library according to various embodiments; Fig. 50 shows a method of analysis that comprises using a classification library according to various embodiments; Fig. 51 shows a sample, H&E and mass spectrometric multivariate images of liver samples with metastatic tumour analysed by rapid evaporative ionization mass spectrometry and DESI wherein it is apparent that both techniques clearly differentiate the tissue types; Fig. 52 shows principal component analysis plots of healthy and cancerous liver tissues for rapid evaporative ionization mass spectrometry imaging cutting and pointing modes as well as for DESI data wherein PC is the principal component and percentage values are explained variance; Fig. 53 shows an univariate intensity comparison of single phospholipid ion species wherein the depicted images of samples are ion-images of the respective ions and DESI and rapid evaporative ionization mass spectrometry show similar relative intensity values for the same ions wherein PE is phosphatidyl-ethanolamine; Fig. 54A shows mass spectra of gastric mucosa, gastric submucosa and adenocarcinoma tissue which was recorded using a modified Xevo G2-S (RTM) Q-Tof mass spectrometer (Waters (RTM)), wherein cancerous and healthy mucosa tissue feature mainly phospholipids in the 600-900 m/z range whilst submucosa feature triglyceride and phosphatidyl-inositol species in the 800-1000 m/z range and Fig. 54B shows a comparison of the abundance of selected peaks showing significant differences between cancerous and healthy tissue in the 600-900 m/z range using Kruskal-Wallis ANOVA wherein all peaks above m/z 800 are significantly different when comparing submucosa to the other two tissue types; Fig. 55A shows a 3-dimensional PCA plot of human colon adenocarcinoma (n=43) and healthy colon mucosal data (n=45) acquired from seven patients using an LTQ Velos (RTM) mass spectrometer wherein the adenomatous polyps (n=5) collected from two patients were sampled ex vivo after their removal and wherein a significant difference can be observed in the FICA space between all three groups and Fig. 55B shows a 3-dimensional RCA plot of healthy gastric mucosa (n=32), gastric submucosa (n=10) and adenocarcinoma of the stomach (n=29) acquired from three patients ex vivo using a Xevo G2-S (RTM) Q-Tof mass spectrometer (Waters (RTM)) wherein the significant differences between submucosa and the other two layers may be used to provide a perforation risk alert system for interventional endoscopy according to an embodiment; and Fig. 56A shows in vivo utilization of a rapid evaporative ionisation mass spectrometry compatible endoscope system and sampling points taken from three patients undergoing colonoscopy and Fig. 56B shows the sampling points depicted on a 3-dimensional PCA plot wherein the spectra acquired in vivo when the polyps were removed localize in a different part of space whilst all other mucosal spectra are quasi uniformly independent from the sampling location.

DETAILED DESCRIPTION

Although the present invention has been described with reference to preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the scope of the invention as set forth in the accompanying claims.

The skilled person will understand that any of the features listed herein may be combined in any combination.

Mass spectrometry ("MS") based identification of tissues is known using imaging techniques, sampling probe/electrospray systems and the direct ambient ionization mass spectrometry investigation of tissues. Direct ambient ionization mass spectrometry, such as REIMS technology, has emerged as a technology allowing in-situ real-time analysis by the utilization of electrosurgical tools as a mass spectrometry ion source. The REIMS fingerprint of human tissues shows high histological specificity with 90-100% concordance with standard histology.

The embodiments of the invention described herein may, for example, be used in or with a real-time, robust tissue characterisation tool which utilises ambient ionisation technologies, such as REIMS technology. Optionally, the tool may be an endoscopic tool.

As will become further apparent, embodiments described herein enables accurate real time spectrometric data to be obtained and utilised, e.g., in order to reduce mis-diagnosis rates and improve complete resection rates.

Various embodiments will now be described in more detail below which in general relate to generating an aerosol, surgical smoke or vapour from one or more regions of a target (details of which are provided elsewhere herein, e.g., in vivo tissue) using an ambient ionisation ion source. The aerosol, surgical smoke or vapour may then be mixed with a matrix and aspirated into a vacuum chamber of a mass and/or ion mobility spectrometer. The mixture may be caused to impact upon a collision surface causing the aerosol, smoke or vapour to be ionised by impact ionisation which results in the generation of analyte ions. The resulting analyte ions (or fragment or product ions derived from the analyte ions) may then be mass and/or ion mobility analysed and the resulting mass and/or ion mobility spectrometric data may be subjected to multivariate analysis or other mathematical treatment in order to determine one or more properties of the target in real time. For example, the multivariate analysis may enable a determination to be made as to whether or not a portion of tissue which is currently being resected is cancerous or not.

Ambient ionisation ion sources In any of the methods of the invention a device may be used to generate an aerosol, smoke or vapour from one or more regions of a target (details of which are provided elsewhere herein, e.g., in vivo tissue). The device may comprise an ambient ionisation ion source which is characterised by the ability to generate analyte aerosol, smoke or vapour from target (details of which are provided elsewhere herein), which may, e.g., be a native or unmodified target. By contrast, other types of ionisation ion sources such as Matrix Assisted Laser Desorption Ionisation ("MALDI") ion sources require a matrix or reagent to be added to the sample prior to ionisation.

It will be apparent that the requirement to add a matrix or a reagent directly to a sample may prevent the ability to perform in vivo analysis of tissue and also, more generally, prevents the ability to provide a rapid simple analysis of target material.

Ambient ionisation techniques are particularly useful since firstly they do not require the addition of a matrix or a reagent to the sample (and hence are suitable for the analysis of in vivo tissue) and since secondly they enable a rapid simple analysis of target material to be performed. Whilst there is no requirement to add a matrix or reagent to a sample in order to perform ambient ionization techniques, the method may optionally include a step of adding a matrix or reagent to the target (e.g., directly to the target) prior to analysis. The matrix or reagent may be added to the target, e.g., to lyse the cells of the target or to enhance the signal therefrom during the analysis.

A number of different ambient ionisation techniques are known and are intended to fall within the scope of the present invention. As a matter of historical record, Desorption Electrospray Ionisation ("DESI") was the first ambient ionisation technique to be developed and was disclosed in 2004. Since 2004, a number of other ambient ionisation techniques have been developed. These ambient ionisation techniques differ in their precise ionisation method but they share the same general capability of generating gas-phase ions directly from (e.g., native, untreated or unmodified) samples. The various ambient ionisation techniques which are intended to fall within the scope of the present invention may not require any prior sample preparation. As a result, the various ambient ionisation techniques enable both in vivo tissue and ex vivo tissue samples to be analysed without the time, expense and problems associated with adding a matrix or reagent to the tissue sample or other target material.

A list of ambient ionisation techniques which are intended to fall within the scope of the present invention are given in the following table: Acronym DESI DeSSI

DAPPI

EASI JeDI

TM-DESI

LMJ-SSP DICE

Nano-DESI

EADESI

Ionisation technique Desorption electrospray ionization Desorption sonic spray ionization Desorption atmospheric pressure photoionization Easy ambient sonic-spray ionization Jet desorption electrospray ionization Transmission mode desorption electrospray ionization Liquid microjunction-surface sampling probe Desorption ionization by charge exchange Nanospray desorption electrospray ionization Electrode-assisted desorption electrospray ionization

APTDOI

V-EASI AFAI LESA PTC-ESI

AFADESI

DEFFI ESTASI

DART ASAP

APTDI

PADI DBDI FAPA HAPGDI

Atmospheric pressure thermal desorption chemical ionization Venturi easy ambient sonic-spray ionization Air flow-assisted ionization Liquid extraction surface analysis Pipette tip column electrospray ionization Air flow-assisted desorption eIectrospray ionization Desorption electro-flow focusing ionization Electrostatic spray ionization Plasma-based ambient sampling ionization transmission Desorption atmospheric pressure chemical ionization Direct analysis in real time Atmospheric pressure solid analysis probe Atmospheric pressure thermal desorption ionization Plasma assisted desorption ionization Dielectric barrier discharge ionization Flowing atmospheric pressure afterglow Helium atmospheric pressure glow discharge ionization APGDDI Atmospheric pressure glow discharge desorption ionization LTP Low temperature plasma LS-APGD Liquid sampling-atmospheric pressure glow discharge

MIPDI

MFGDP RoPPI PLASI

MALDESI

ELDI LDTD LAESI CALDI

LA-FAPA LADESI

LDESI LEMS LSI

IR-LAMICI LDSPI PAMLDI

Microwave induced plasma desorption ionization Microfabricated glow discharge plasma Robotic plasma probe ionization Plasma spray ionization Matrix assisted laser desorption electrospray ionization Electrospray laser desorption ionization Laser diode thermal desorption Laser ablation electrospray ionization Charge assisted laser desorption ionization Laser ablation flowing atmospheric pressure afterglow Laser assisted desorption electrospray ionization Laser desorption electrospray ionization Laser electrospray mass spectrometry Laser spray ionization Infrared laser ablation metastable induced chemical ionization Laser desorption spray post-ionization Plasma assisted multiwavelength laser desorption ionization HALDI High voltage-assisted laser desorption ionization

PALDI ESSI PESI

N D-ESSI PS

TS

Wooden-tip Plasma assisted laser desorption ionization Extractive electrospray ionization Probe electrospray ionization Neutral desorption extractive electrospray ionization Paper spray Direct inlet probe-atmospheric pressure chemical ionization Touch spray Wooden-tip electrospray CBS-SPME Coated blade spray solid phase microextraction TS! Tissue spray ionization RADIO Radiofrequency acoustic desorption ionization LIAD-ESI Laser induced acoustic desorption electrospray ionization SAWN Surface acoustic wave nebulization UASI Ultrasonication-assisted spray ionization SPA-nanoESl Solid probe assisted nanoelectrospray ionization PAUSI DPESI ESA-Py APPIS

RASTIR SAC I DEMI

REIMS SPAM TDAMS

MAII SAI I

SwiFERR

LPTD

Paper assisted ultrasonic spray ionization Direct probe electrospray ionization Electrospray assisted pyrolysis ionization Ambient pressure pyroelectric ion source Remote analyte sampling transport and ionization relay Surface activated chemical ionization Desorption electrospray metastable-induced ionization Rapid evaporative ionization mass spectrometry Single particle aerosol mass spectrometry Thermal desorption-based ambient mass spectrometry Matrix assisted inlet ionization Solvent assisted inlet ionization Switched ferroelectric plasma ionizer Leidenfrost phenomenon assisted thermal desorption According to an embodiment the ambient ionisation ion source may comprise a rapid evaporative ionisation mass spectrometry ("REIMS") ion source wherein a RF voltage is applied to one or more electrodes in order to generate smoke, aerosol or vapour by Joule heating.

However, it will be appreciated that other ambient ion sources including those referred to above may also be utilised. For example, according to another embodiment the ambient ionisation ion source may comprise a laser ionisation ion source. According to an embodiment the laser ionisation ion source may comprise a mid-IR laser ablation ion source. For example, there are several lasers which emit radiation close to or at 2.94 pm which corresponds with the peak in the water absorption spectrum. According to various embodiments the ambient ionisation ion source may comprise a laser ablation ion source having a wavelength close to 2.94 pm on the basis of the high absorption coefficient of water at 2.94 pm. According to an embodiment the laser ablation ion source may comprise a Er:YAG laser which emits radiation at 2.94 pm.

Other embodiments are contemplated wherein a mid-infrared optical parametric oscillator ("OPO") may be used to produce a laser ablation ion source having a longer wavelength than 2.94 pm. For example, an Er:YAG pumped ZGP-OPO may be used to produce laser radiation having a wavelength of e.g. 6.1 pm, 6.45 pm or 6.73 pm. In some situations it may be advantageous to use a laser ablation ion source having a shorter or longer wavelength than 2.94 pm since only the surface layers will be ablated and less thermal damage may result. According to an embodiment a Co:MgF2 laser may be used as a laser ablation ion source wherein the laser may be tuned from 1.75-2.5 pm. According to another embodiment an optical parametric oscillator ("OPO") system pumped by a Nd:YAG laser may be used to produce a laser ablation ion source having a wavelength between 2.9-3.1 pm. According to another embodiment a CO2 laser having a wavelength of 10.6 pm may be used to generate the aerosol, smoke or vapour.

According to other embodiments the ambient ionisation ion source may comprise an ultrasonic ablation ion source, or a hybrid electrosurgical -ultrasonic ablation source that generates a liquid sample which is then aspirated as an aerosol. The ultrasonic ablation ion source may comprise a focused or unfocussed ultrasound.

According to an embodiment the first device for generating aerosol, smoke or vapour from one or more regions of a target may comprise an tool which utilises an RF voltage, such as continuous RF waveform. According to other embodiments a radiofrequency tissue dissection system may be used which is arranged to supply pulsed plasma RF energy to a tool. The tool may comprise, for example, a PlasmaBlade (RTM). Pulsed plasma RF tools operate at lower temperatures than conventional electrosurgical tools (e.g. 40-170 °C c.f. 200-350 °C) thereby reducing thermal injury depth. Pulsed waveforms and duty cycles may be used for both cut and coagulation modes of operation by inducing electrical plasma along the cutting edge(s) of a thin insulated electrode.

According to an embodiment the first device comprises a surgical water/saline jet device such as a resection device, a hybrid of such device with any of the other devices herein, an electrosurgery argon plasma coagulation device, a hybrid argon plasma coagulation and water/saline jet device. According to an embodiment the first device comprises or forms part of an ambient ion or ionisation source; or said first device generates said aerosol, smoke or vapour from the target and contains ions and/or is subsequently ionised by an ambient ion or ionisation source, or other ionisation source.

Optionally, the first device comprises or forms part of a device, or an ion source, selected from the group consisting of: (i) a rapid evaporative ionisation mass spectrometry ("REIMS") ion source; (ii) a desorption electrospray ionisation ("DESI") ion source; (iii) a laser desorption ionisation ("LDI") ion source; (iv) a thermal desorption ion source; (v) a laser diode thermal desorption ("LDTD'') ion source; (vi) a desorption electro-flow focusing ("DEFFI") ion source; (vii) a dielectric barrier discharge ("DBD") plasma ion source; (viii) an Atmospheric Solids Analysis Probe ("ASAP") ion source; (ix) an ultrasonic assisted spray ionisation ion source; (x) an easy ambient sonic-spray ionisation ("EASI") ion source; (xi) a desorption is atmospheric pressure photoionisation ("DAPPI") ion source; (xii) a paperspray ("PS") ion source; (xiii) a jet desorption ionisation ("JeDI") ion source; (xiv) a touch spray ("TS") ion source; (xv) a nano-DESI ion source; (xvi) a laser ablation electrospray ("LAESI") ion source; (xvii) a direct analysis in real time ("DART") ion source; (xviii) a probe electrospray ionisation ("PESI") ion source; (xix) a solid-probe assisted electrospray ionisation ("SPA-ESI") ion source; (xx) a cavitron ultrasonic surgical aspirator ("CUSA") device; (xxi) a hybrid CUSA-diathermy device; (xxii) a focussed or unfocussed ultrasonic ablation device; (xxiii) a hybrid focussed or unfocussed ultrasonic ablation and diathermy device; (xxiv) a microwave resonance device; (xxv) a pulsed plasma RF dissection device; (xxvi) an argon plasma coagulation device; (xxvi) a hybrid pulsed plasma RF dissection and argon plasma coagulation device; (xxvii) a hybrid pulsed plasma RF dissection and JeDI device; (xxviii) a surgical water/saline jet device; (xxix) a hybrid electrosurgery and argon plasma coagulation device; and (xxx) a hybrid argon plasma coagulation and water/saline jet device.

Optionally, the step of using said first device to generate aerosol, smoke or vapour comprises contacting said target with one or more electrodes.

Optionally, said one or more electrodes comprises either: (i) a monopolar device, wherein said method optionally further comprises providing a separate return electrode, (ii) a bipolar device; or (iii) a multi-phase RF device, wherein said method optionally further comprises providing a separate return electrode or electrodes.

Optionally, said one or more electrodes comprise or forms part of a rapid evaporation ionization mass spectrometry ("REIMS") device.

Optionally, said method further comprises applying an AC or RF voltage to said one or more electrodes in order to generate said aerosol, smoke or vapour.

Optionally, the step of applying said AC or RF voltage to said one or more electrodes further comprises applying one or more pulses of said AC or RF voltage to said one or more electrodes.

Optionally, said step of applying said AC or RF voltage to said one or more electrodes causes heat to be dissipated into said target.

Optionally, said step of using said first device to generate aerosol, smoke or vapour from one or more regions of the target further comprises irradiating the target with a laser.

Optionally, said first device generates aerosol from one or more regions of the target by direct evaporation or vaporisation of target material from said target by Joule heating or diathermy.

Optionally, said step of using said first device to generate aerosol, smoke or vapour from one or more regions of the target further comprises directing ultrasonic energy into said target.

Optionally, said aerosol comprises uncharged aqueous droplets, optionally comprising cellular material.

Optionally, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the mass or matter generated by said first device and which forms said aerosol may be in the form of droplets.

The first device may be arranged and adapted to generate aerosol wherein the Sauter mean diameter ("SMD", d32) of said aerosol is in a range: (i) < 5 pm; (ii) 5-10 pm; (iii) 10-15 pm; (iv) 15-20 pm; (v) 20-25 pm; or (vi) > 25 pm.

The aerosol may traverse a flow region with a Reynolds number (Re) in the range: (i) < 2000; (ii) 2000-2500; (iii) 2500-3000; (iv) 3000-3500; (v) 3500-4000; or (vi) > 4000.

Substantially at the point of generating the aerosol, the aerosol may comprise droplets having a Weber number (We) selected from the group consisting of: (i) < 50; (ii) 50-100; (iii) 100-150; (iv) 150-200; (v) 200-250;(vi) 250-300; (vii) 300-350; (viii) 350-400; (ix) 400-450; (x) 450-500; (xi) 500-550; (xii) 550-600; (xiii) 600-650; (xiv) 650-700; (xv) 700-750; (xvi) 750-800; (xvii) 800-850; (xviii) 850-900; (xix) 900-950; (xx) 950-1000; and (xxi) > 1000.

Substantially at the point of generating the aerosol, the aerosol may comprise droplets having a Stokes number (Sk) in the range: (i) 1-5; (ii) 5-10; (iii) 10-15; (iv) 15-20; (v) 20-25; (vi) 25-30; (vii) 30-35; (viii) 35-40; (ix) 40-45; (x) 45-50; and (xi) > 50.

Substantially at the point of generating the aerosol, the aerosol may comprise droplets having a mean axial velocity selected from the group consisting of: (i) < 20 m/s; (N) 20-30 m/s; (iii) 30-40 m/s; (iv) 40-50 m/s; (v) 50-60 m/s; (vi) 60-70 mis; (vii) 70-80 m/s; (viii) 80-90 m/s; (ix) 90-100 m/s; (x) 100-110 m/s; (xi) 110-120 m/s; (xii) 120-130 m/s; (xiii) 130-140 m/s; (xiv) 140150 m/s; and (xv) > 150 m/s.

Optionally, said aerosol comprises uncharged aqueous droplets, which may comprise cellular material.

Optionally, the method comprises ionising at least some of said aerosol, smoke or vapour, or analyte therein, so as to generate analyte ions; wherein said analyte ions are analysed to obtain said spectrometric data.

Optionally, the method comprises directing or aspirating at least some of said aerosol, smoke or vapour into a vacuum chamber of a mass and/or ion mobility spectrometer; and/or ionising at least some said aerosol, smoke or vapour, or the analyte therein, within a, or said, vacuum chamber of said spectrometer so as to generate a plurality of analyte ions.

Optionally, the method comprises causing said aerosol, smoke or vapour, or analyte therein, to impact upon a collision surface, optionally located within a, or the, vacuum chamber of said spectrometer, so as to generate the plurality of analyte ions.

Optionally, the collision surface may be heated. The collision surface may be heated to a temperature selected from the group consisting of: (i) about < 100 00; (ii) about 100-200 00; (iii) about 200-300 °C; (iv) about 300-400 00; (v) about 400-500 °C; (vi) about 500-600 °C; (vii) about 600-700 °C; (viii) about 700-800 °C; (ix) about 800-900 °C; (x) about 900-1000 "C; (xi) about 1000-1100 'C; and (xii) about > 1100 'C.

Optionally, the method comprises adding a matrix to said aerosol, smoke or vapour; optionally wherein said matrix is selected from the group consisting of: (i) a solvent for said aerosol, smoke or vapour or analyte therein; (ii) an organic solvent; (iii) a volatile compound; (iv) polar molecules; (v) water; (vi) one or more alcohols; (vii) methanol; (viii) ethanol; (ix) isopropanol; (x) acetone; (xi) acetonitrile; (xii) 1-butanol; (xiii) tetrahydrofuran; (xiv) ethyl acetate; (xv) ethylene glycol; (xvi) dimethyl sulfoxide; an aldehyde; (xviii) a ketone; (xiv) non-polar molecules; (xx) hexane; (xxi) chloroform; and (xxii) propanol.

Rapid evaporative ionisation mass spectrometry ("REIMS") technology Fig. 1A and Fig. 1B show a REIMS technology endoscope and snare arrangement in accordance with an embodiment of the present invention. According to the embodiment a polypectomy snare may be provided. As shown in Fig. 1B, the snare 116 comprises a wire loop which runs through a length of tubing 113. The wire loop is attached to a manipulator which, as shown in Fig. 1A, may be operated by a user via an endoscopic stack 101. The manipulator allows a user to close the snare 116 around a polyp 117. The wire snare 116 is connected to an RF voltage generator (not shown). The wire snare 116 acts as an electrosurgical tool and may be deployed through a port 112 in an endoscope 107 and used to resect polyps 117 located e.g., in the stomach 111, pylorus 110 or colon etc., e.g., via the oesophagus 109. As the polypectomy snare 116 is deployed and tightened around a polyp 117, the polyp 117 effectively restricts or seals the open end 114 of the tubing 113 which houses the wire snare 116.

When an RF voltage is applied to the wire snare 116, the wire snare 116 acts as an electrosurgical tool and effectively cuts and removes the polyp 117. At the same time, surgical smoke or aerosol 118 is generated which is substantially unable to pass into the end 114 of the tubing 113 which houses the wire snare 116. The tubing 113 which houses the wire snare 116 is additionally provided with fenestrations or one or more aspiration ports 115 which enables the surgical smoke or aerosol 118 to be aspirated into the tubing 113 which houses the wire snare 116. The surgical smoke or aerosol 118 may be sucked towards the tubing by a pump (not shown) connected to the tubing and the direction of smoke suction may be as illustrated by arrow 119, i.e., the surgical smoke or aerosol 118 may be sucked towards the tubing 113 and through the fenestrations or one or more aspiration ports 115. The surgical smoke or aerosol 118 is then aspirated along the length of the tubing 113 and, as shown in Fig. 1A, via a connector 106 is passed to a vacuum chamber of a mass and/or ion mobility spectrometer 102 whereupon the surgical smoke or aerosol 118 is ionised, e.g., upon impacting a collision surface.

The resulting analyte ions are then mass and/or ion mobility analysed and real time information relating to the tissue which is being resected may be provided to a user (who may be, for example, a surgeon or a specialist nurse). In addition to cutting the polyp 117 away from the lining of the stomach 111 or colon, the snare 116 may be also be used to hold on to the polyp 117 so that the polyp 117 can be removed from the stomach 111 or colon, optionally analysed and then disposed of.

The endoscope may emit light 108 and comprise a camera such that a user may appropriately operate the apparatus.

According to other embodiments the electrosurgical tool and associated endoscope may be used in any other body cavities and organs, details of which are provided elsewhere herein, including the lung, nose and urethra.

The snare 116 may comprise a monopolar device and a relatively large pad acting as a return electrode may be placed underneath the patient so that electrical current flows from the snare electrode, through the patient, to the return electrode. Alternatively, the snare electrode may comprise a bipolar device such that electrical current does not flow through the patient's body. A bipolar device may be used, for example, in very sensitive operations such as brain surgery wherein it is clearly undesirable for an electrical current to flow through surrounding tissue.

Other embodiments are also contemplated wherein the electrosurgical tool may comprise a multi-phase or 3-phase device and may comprise, for example, three or more separate electrodes or probes.

Surgical smoke, aerosol or vapour 118 which is aspirated via the electrosurgical tool may be passed via a liquid separator or liquid trap (not shown) in order to remove or reduce the amount of liquid which is onwardly transmitted to the mass and/or ion mobility spectrometer 102.

A matrix may be added or mixed with the smoke, aerosol or vapour, optionally prior to the smoke, aerosol or vapour impacting upon a collision surface. The matrix may dissolve, dilute or form clusters with at least some of the analytes within the smoke, aerosol or vapour.

This may assist in the ionisation of the analytes.

The matrix may be selected from the group consisting of: (i) a solvent for said aerosol, smoke or vapour or analyte therein; (ii) an organic solvent; (iii) a volatile compound; (iv) polar molecules; (v) water; (vi) one or more alcohols; (vii) methanol; (viii) ethanol; (ix) isopropanol; (x) acetone; (xi) acetonitrile; (xii) 1-butanol; (xiii) tetrahydrofuran; (xiv) ethyl acetate; (xv) ethylene glycol; (xvi) dimethyl sulfoxide; an aldehyde; (xviii) a ketone; (xiv) non-polar molecules; (xx) hexane; (xxi) chloroform; and (xxii) 1-propanol. Isopropanol is of particular interest, e.g., in the analyse of lipids and triglycerides.

The matrix and/or aerosol, smoke or vapour may be doped with one or more additives to, for example, enhance the solvation or dilution of analyte with the matrix, or for enhancing the ionisation of the analyte within the aerosol, smoke or vapour.

The doping compound may be an acidic or basic additive such as, for example, formic acid or diethylamine.

The matrix and/or doping compound may cause derivatisation of the analyte in the aerosol, smoke or vapour. For example, the matrix and/or doping compound may cause the derivatisation of cholesterol or steroids in the analyte. This may render the analyte more easily ionised.

The addition of a matrix is particularly advantageous in that diluting the sample to be analysed, dissolving analyte in the matrix or forming said clusters may reduce intermolecular bonding between the analyte molecules. This enhances the ionisation of the analyte. For example, if the analyte is then atomised, e.g., by being collided with a collision surface, the analyte will fragment into smaller droplets or clusters, wherein any given droplet or cluster is likely to contain fewer analyte molecules than it would if the matrix were not present. This in turn leads to a more efficient generation of ions when the matrix in each droplet is evaporated.

Fig. 1A also shows in more detail an embodiment wherein an endoscopic polypectomy snare which was equipped with an additional T-piece connector 106 in order to establish a transfer line between the tissue evaporation point and the atmospheric inlet 103 of a mass and/or ion mobility spectrometer 102. The atmospheric inlet 103 may comprise a grounding 104.

The REIMS endoscopic setup was initially optimized and its reproducibility was assessed using a porcine stomach model. Artificial polyps 117 were created within porcine stomach mucosa and resections were undertaken using a polypectomy snare 116 as shown in Fig. 1B. This set-up allowed for an exact simulation of a standard endoscopic resection. Since the polyp 117 completely blocks the opening or tool deployment opening 114 of the plastic sheath tubing 113 of the snare 116 during resection as can be seen from Fig. 1B, the aerosol 118 produced by the resection is aspirated through fenestrations 115 which are provided on the plastic sheath 113 of the snare 116.

The provision of fenestrations 115 on the plastic sheath 113 of the REIMS snare and which are distal from the tool deployment opening 114 of the snare is particularly advantageous since the fenestrations or aspiration ports 115 allow surgical smoke, aerosol or vapour 118 to be aspirated when the tool deployment opening 114 is at least partially or totally blocked.

The aerosol particles 118 which enter the tubing 113 housing the REIMS snare 116 via the fenestrations or aspiration ports 115 are then may transferred to a mass and/or ion mobility spectrometer 102 via PTFE tubing 105 which may be connected to a port of the snare. The snare 116 may be connected to the proximal end of a REIMS endoscope 107. The tubing may be connected directly to an inlet capillary or ion sampling orifice of the mass and/or ion mobility spectrometer 102. It will be understood that the mass and/or ion mobility spectrometer is distal to the point of evaporation.

Aspiration of the aerosols may be facilitated using a Venturi pump driven by standard medical air.

The mass and/or ion mobility spectrometer may include an atmospheric interface including the collision surface mentioned above, as will be described in relation to Fig. 2.

Fig. 2 shows a schematic of an embodiment of the interface between the electrosurgical tool and the mass and/or ion mobility spectrometer. The instrument may comprise an ion analyser 207 having an inlet 206, a vacuum region 208, said collision surface 209 and ion optics 212 (such as a Stepwave (RTM) ion guide) arranged within the vacuum region 208. The instrument also may comprise a sample transfer tube 202 and a matrix introduction conduit 203.

The sample transfer tube 202 has an inlet for receiving the smoke, aerosol or vapour sample 201 (which may correspond to the plume 118 described in relation to Fig. 1) from a sample being investigated and an outlet that is connected to the inlet 206 of the ion analyser 207. The matrix introduction conduit 203 has an inlet for receiving a matrix compound and an outlet that intersects with the sample transfer tube 202 so as to allow the matrix 204 to be intermixed with the aerosol sample 201 in the sample transfer tube 202. A T-junction component may be provided at the junction between tubes 202, 203 and 206. The tubes 202, 203 and 206 may be removably inserted into the T-junction.

A method of operating the device of Fig. 2 will now be described. A sample, such as biological tissue, may be subjected to the REIMS technique. For example, a diathermic device may be used to evaporate biological tissue from the sample so as to form an aerosol, e.g., as described above in relation to Fig. 1. The aerosol particles 201 are then introduced into the inlet of the sample transfer tube 202. A matrix compound 204 is introduced into the inlet of the matrix introduction conduit 203. The aerosol particles 201 and matrix compound 204 are drawn towards the inlet 206 of the ion analyser 207 by a pressure differential caused by the vacuum chamber 208 being at a lower pressure than the inlets to the tubes 202, 203. The aerosol particles 201 may encounter the molecules of matrix compound 204 in, and downstream of, the region that the sample transfer tube 202 intersects with the matrix introduction conduit 203. The aerosol particles 201 intermix with the matrix 204 so as to form aerosol particles containing matrix molecules 205, in which both the molecular constituents of the aerosol sample 201 and the matrix compound 204 are present. The matrix molecules 204 may be in excess compared to the molecular constituents of aerosol sample 201.

The particles 205 may exit the sample transfer tube 202 and pass into the inlet 206 of the ion analyser 207. The particles 205 then enter into the decreased pressure region 208 and gain substantial linear velocity due to the adiabatic expansion of gas entering the vacuum region 208 from the sample transfer tube 202 and due to the associated free jet formation. The accelerated particles 205 may impact on the collision surface 209, where the impact event fragments the particles 205, leading to the eventual formation of gas phase ions 210 of the molecular constituents of the aerosol sample 201 and the formation of matrix molecules 211. The collision surface 209 may be controlled and maintained at a temperature that is substantially higher than the ambient temperature.

The matrix 204 includes a solvent for the analyte 201, such that the analyte 201 dissolves by the matrix 204, thereby eliminating intermolecular bonding between the analyte molecules 201. As such, when the dissolved analyte 205 is then collided with the collision surface 209, the dissolved analyte 205 will fragment into droplets and any given droplet is likely to contain fewer analyte molecules than it would if the matrix were not present. This in turn leads to a more efficient generation of analyte ions 210 when the matrix in each droplet is evaporated. The matrix may include an organic solvent and/or a volatile compound. The matrix may include polar molecules, water, one or more alcohols, methanol, ethanol, isopropanol, acetone or acetonitrile. Isopropanol is of particular interest.

The matrix molecules 211 may freely diffuse into the vacuum. In contrast, the gas phase ions 210 of the molecular constituents of the aerosol sample 201 may be transferred by the ion optics 212 to an analysis region (not shown) of the ion analyser 207. The ions 210 may be guided to the analysis region by applying voltages to the ion optics 212.

The ion optics 2012 may be a StepWave (RTM) ion guide. The collision surface may be positioned along and adjacent to the central axis of the large opening of a StepWave (RTM) ion guide. As will be understood by those skilled in the art, a StepWave (RTM) ion guide comprises two conjoined ion tunnel ion guides. Each ion guide comprises a plurality of ring or other electrodes wherein ions pass through the central aperture provided by the ring or other electrodes. Ions enter a first of the ion guides, along with any neutrals that may be present, and travel through the first ion guide. Ions are then directed orthogonally into a second of the ion guides and are transmitted therethrough. Transient DC voltages or potentials are applied to the electrodes to drive the ions through them. The StepWave (RTM) ion guide is based on stacked ring ion guide technology and is designed to maximise ion transmission from the source to the mass and/or ion mobility analyser. The device allows for the active removal of neutral contaminants, since the neutrals are not directed orthogonally into the second ion guide, thereby providing an enhancement to overall signal to noise. The design enables the efficient capture of the diffuse ion cloud entering a first lower stage which is then may focused into an upper ion guide for transfer to the ion analyser. The ions are then analysed by the ion analyser, which may comprise a mass spectrometer and/or an ion mobility spectrometer, or a combination of the two. As a result of the analysis, chemical information about the sample 201 may be obtained.

REIMS spectra recorded from the porcine stomach model in the m/z range of 600-1000 features predominantly phospholipids, which have been observed for all mammalian tissue types in previous REIMS experiments.

The REIMS endoscopic setup was tested on ex-vivo human samples including gastric adenocarcinoma, healthy gastric mucosa and healthy gastric submucosa. The samples were acquired from three individual patients, all of whom provided written informed consent. It was also tested on humans in vivo.

Real time and/or delayed information may be provided to a user of the electrosurgical tool that may comprise spectrometric information and/or tissue classification information. A feedback device and/or an alarm and/or an alert may also may be provided to provide a user of the electrosurgical tool with feedback and/or an alarm and/or an alert that analyte from an undesired target region or area is being analysed by the analyser or that the electrosurgical tool is operating in and/or is located in an undesired target region or area.

Electrical power to the electrosurgical tool may be reduced and/or stopped in the event that analyte from an undesired target region or area is being analysed by the analyser and/or the electrosurgical tool is operating in and/or is located in an undesired target region or area.

A liquid trap or separator may be provided between the electrosurgical probe and the analyser which captures or discards undesired liquids that are aspirated by the probe whilst may allowing the aerosol or surgical smoke itself to pass relatively uninhibited to the mass and/or ion mobility spectrometer. This prevents undesired liquid from reaching the analyser without affecting the measurement of the aerosol or surgical smoke. The liquid trap or separator may be arranged to capture the liquid, may using a liquid collector, for later disposal.

Fig. 3 illustrates another REIMS embodiment of the invention wherein bipolar forceps 301 may be brought into contact with in vivo tissue 302 of a patient 303. In the example shown in Fig. 3, the bipolar forceps 301 may be brought into contact with brain tissue 302 of a patient 303 during the course of a surgical operation on the patient's brain. An RF voltage from an RF voltage generator 304 may be applied to the bipolar forceps 301 which causes localised Joule or diathermy heating of the tissue 302. As a result, smoke, aerosol or vapour 305 is generated. The smoke, aerosol or vapour 305 may then be captured or otherwise aspirated through an irrigation port of the bipolar forceps 301. The irrigation port of the bipolar forceps 301 is therefore reutilised as an aspiration port. The smoke, aerosol or vapour 305 may then be passed from the irrigation (aspiration) port of the bipolar forceps 301 to tubing 306 (e.g. 1/8" or 3.2 mm diameter Teflon (RTM) tubing). The tubing 306 is arranged to transfer the smoke, aerosol or vapour 305 to an atmospheric pressure interface 307 of a mass and/or ion mobility spectrometer 308.

Although embodiments have been described in which in vivo tissue is analysed, the invention extends to embodiments wherein ex vivo or in vitro specimens are analysed. Also, the invention extends to embodiments wherein non-tissue specimens are analysed, either in vivo, ex vivo or in vitro. For example, a body fluid sample or faecal sample may be analysed. Although embodiments have been described in which REIMS is used to generate the smoke, aerosol or vapour for analysis, other ambient ionisation techniques may be used such as, for example, Desorption Electrospray Ionisation ("DESI").

Desorption Electrospray Ionisation ("DESI") Desorption Electrospray Ionisation ("DESI") has also been found to be a particularly useful and convenient method for the real time rapid and direct analysis of biological material, such as tissues. DESI techniques allow direct and fast analysis of surfaces without the need for prior sample preparation. The technique will now be described in more detail with reference to Fig. 4.

As shown in Fig. 4, the DESI technique is an ambient ionisation method that involves directing a spray of (primary) electrically charged droplets 401 onto a target. The electrospray mist is pneumatically directed at the target by a sprayer 400 where subsequent splashed (secondary) droplets 405 carry desorbed ionised analytes (e.g. desorbed lipid ions). The sprayer 400 may be supplied with a solvent 406, a gas 407 (such as nitrogen) and a voltage from a high voltage source 408. After ionisation, the ions travel through air into an atmospheric pressure interface 409 of a mass and/or ion mobility spectrometer or mass and/or ion mobility analyser (not shown), e.g. via a transfer capillary 410. The transfer capillary 410 may be heated, e.g., to a temperature up to 500 °C.

The ions may be analysed by the method described in relation to Fig. 2, or by other methods. The DESI technique allows, for example, direct analysis of biological compounds such as lipids, metabolites and peptides in their native state without requiring any advance sample preparation.

General methods of the invention The invention provides a method of analysis using mass spectrometry and/or ion mobility spectrometry comprising: a) using a first device to generate aerosol, smoke or vapour from one or more regions of a first target of biological material; and b) mass analysing and/or ion mobility analysing said aerosol, smoke, or vapour, or ions derived therefrom so as to obtain first spectrometric data, wherein said biological material is a human subject, a non-human animal subject, or a specimen derived from said human or non-human animal subject.

In one aspect, the method may be a method of analysing a disease, a diseased tissue, and/or a biomarker of a disease. Thus, the method may optionally comprise a step of analysing a disease, a diseased tissue, and/or a biomarker of a disease.

The method may be a method of, or of obtaining information relevant to, (i) diagnosing a disease; (ii) monitoring the progression or development of a disease; (iii) disease prognosis; (iv) predicting the likelihood of a disease responding to treatment; (v) monitoring the response of a disease to treatment; (vi) stratifying subjects; (vii) determining the distribution of diseased tissue; and/or (viii) determining the margin between diseased and healthy tissue.

Thus, the method may optionally comprise a step of (i) diagnosing a disease; (ii) monitoring the progression or development of a disease; (iii) disease prognosis; (iv) predicting the likelihood of a disease responding to treatment; (v) monitoring the response of a disease to treatment; (vi) stratifying subjects; (vii) determining the distribution of diseased tissue; and/or (viii) determining the margin between diseased and healthy tissue.

Details of suitable diseases are provided elsewhere herein.

In one aspect, the method may be a method of analysing a microbe, a microbial interaction, a microbial biomarker, and/or a microbiome. Thus, the method may optionally comprise a step of analysing a microbe, a microbial interaction, a microbial biomarker, and/or a microbiome.

In one aspect, the method may be a method of analysing the genotype and/or phenotype of a cell. Thus, the method may optionally comprise a step of analysing the genotype and/or phenotype of a cell.

In one aspect, the method may be a method of treatment. Thus, the method may optionally comprise a step of administering a therapeutically effective amount of a therapeutic agent to a subject in need thereof.

In one aspect, the method may be a method of surgery. Thus, the method may optionally comprise a surgical step of resecting tissue, optionally prior to, during, and/or after the method of analysis. The method may optionally be a method of surgery, comprising using the method to determine what tissue to resect, or comprising resecting tissue that was identified, characterised, and/or confirmed as being diseased by the method.

In one aspect, the method may be a method of analysing a faecal and/or body fluid specimen. Thus, the method may optionally comprise a step of analysing a faecal and/or body fluid specimen.

In one aspect, the method may be a method of analysing a compound. Thus, the method may optionally comprise a step of analysing a compound and/or a biomarker for a compound.

Optionally, the method may include 2 or more of the aspects disclosed herein, e.g., 3 or more, 4 or more 5, or more etc. For example, the method may optionally comprise a step of analysing a faecal and/or body fluid specimen, wherein a microbial biomarker and/or a compound biomarker is analysed.

Optional features of any of these methods are discussed below. Thus, unless otherwise stated, any reference to "a method" or "the method" is intended to be a reference to any of the methods of the invention listed herein. It is explicitly intended that any of these features may be present in any combination in any of these methods.

Targets and analysis thereof The method may be carried out on a "target", which may optionally be a biological material, e.g., a subject or a specimen derived from a subject.

The "subject" may be a human or a non-human animal. The subject may be alive or dead. If the method is carried out on a living subject, then it may be referred to as an in vivo method. If the method is carried out on a specimen, then it may be referred to as an in vitro or ex vivo method.

Optionally, the animal may be a mammal, optionally selected, for example, from any livestock, domestic or laboratory animal, such as, mice, guinea pigs, hamsters, rats, goats, pigs, cats, dogs, sheep, rabbits, cows, horses, camels, donkeys, buffalos, lamas, chickens, ducks, geese, and/or monkeys. Optionally, it may be an insect, bird or fish, e.g. a fly or a worm. Thus, any veterinary applications of the method of the invention are contemplated.

The method may optionally be carried out on an in vivo target, i.e. on a living subject.

For example, it may be carried out by using a thermal ablation method.

Alternatively or in addition, it may optionally be carried out on a dead subject, for example as part of an autopsy or a necropathy.

Alternatively or in addition, it may optionally be carried out on an ex vivo or in vitro target, e.g., on a specimen. The specimen may optionally be a provided specimen, i.e. a specimen that was previously obtained or removed from a subject. Optionally, the method may include a step of obtaining a specimen from a subject.

Thus, it may optionally be carried out on a specimen, which may optionally be selected, for example, from a surgical resection specimen, a biopsy specimen, a xenograft specimen, a swab, a smear, a body fluid specimen and/or a faecal specimen.

Resection is the surgical removal of part or all of a tissue.

A biopsy specimen may optionally be obtained, e.g., by using a needle to withdraw tissue and/or fluid comprising cells; by using an endoscope; and/or during surgery. A biopsy may optionally be incisional, excisional, or be retrieved from a surgical resection. A biopsy specimen comprises cells and may optionally be a tissue specimen, for example, comprising or consisting of diseased and/or non-diseased tissue.

A "xenograft specimen" is a tissue specimen derived from a xenograft. A "xenograft" refers to cellular material, such as tissue, that originated from a first subject and was inserted into a second subject. Optionally, the xenograft may comprise or consist of tumour cells. For example, cells or tissue obtained from a human tumour may be xenografted into a host animal.

Optionally, a xenograft may be analysed in vivo, in which case the target may be referred to as a subject comprising the xenograft. Thus, the target may optionally be a subject comprising a xenograft. Optionally, a specimen may be derived from a xenograft.

A "swab" is intended to be understood as comprising a "standard medical swab" i.e. a swab that is designed for sampling biological samples such as mucosal membranes. For example, the term "standard medical swab" should be understood as covering a "cotton bud" (British) or a "cotton swab" (American) i.e. a small wad of cotton wrapped around one or both ends of a tube. The tube may be made from plastic, rolled paper or wood.

A swab may optionally, for example, comprise a tissue or other cellular material, e.g., a mucosal sample.

A smear may, for example, optionally be a specimen that has been smeared onto a solid support, e.g. between two slides.

A body fluid may, for example, optionally be selected from blood, plasma, serum, sputum, lavage fluid, pus, urine, saliva, phlegm, vomit, faeces, amniotic fluid, cerebrospinal fluid, pleural fluid, semen, sputum, vaginal secretion, interstitial fluid, and/or lymph. Optionally, it may be dried, collected with a swab, and/or dispensed onto an absorbent carrier, e.g. a filter or paper. Optionally, it may be a pellet. A pellet may be prepared, e.g., by centrifuging the body fluid at a suitable force and for a suitable time to sediment any cells, large structures and/or macromolecules to form a pellet. The remainder of the fluid, i.e. the supernatant, may then be discarded, e.g. by tipping it out of via aspiration.

Optionally, the specimen may be sectioned and/or sequentially disassociated, e.g., mechanically and/or enzymatically, for example with trypsin, to obtain different layers of the specimen, and/or to derive cells from different layers of a specimen. For example, this may be of interest if the specimen is a tissue, e.g., a xenograft tissue. Different layers, or cells derived from different layers, of the specimen may then be analysed. During tissue growth and/or maintenance, different layers of a tissue may have been exposed to different environmental conditions, and/or been exposed to different concentrations of a substance, as substances may not penetrate each layer at the same rate. Thus, the method may optionally be used to analyse one or more different layers of a specimen, or cells derived therefrom.

The method may optionally involve the analysis of one or more different targets.

Optionally, 2 or more targets from different subjects, and/or from different locations within a subject, may be analysed. Optionally, the targets may be at or from 2 or more different locations, e.g., specimens may be at or from 2 or more locations in/of a subject. For example, in the case of coeliac disease, it is recommended that more or more biopsy specimen be obtained from each of the second and third duodenal portion of the GI tract, and such a strategy may also be suitable for any of the other diseases discussed herein.

Optionally, a target may be at or from one or more locations known or suspected to be healthy; and one or more locations known or suspected to be diseased. In the case of cancer, for example, a target may optionally be at or from at least 1 location known or suspected to be healthy; at least 1 location known or suspected to be a tumour margin; at least 1 location known or suspected to be a tumour stroma; and/or at least 1 location known or suspected to be a neoplastic tumour.

Optionally, the method may involve the analysis one 2 or more locations of a target. Optionally, distinct locations of a target may be analysed, e.g., a series of points may be sampled, optionally with or without spatial encoding information for imaging purposes.

The analysis may optionally be made intra-operatively, i.e. whilst a surgical procedure is under way. Thus, the analysis may optionally be used to provide real-time analysis of a target. The analysis may optionally be used to identify disease margins. A disease margin may optionally be analysed, e.g., by analysing the concentration of a particular cell type, e.g. a diseased, cancerous, and/or necrotic cell in a target region. The analysis may optionally be made in vivo, e.g., during a surgical procedure. This may optionally involve using, e.g., a thermal ablation surgical method, e.g., REIMS technology, such as, the iKnife technology. For example, a tissue on which surgery is being performed may be analysed in vivo and the results of the analysis may be used to inform, influence or determine a further surgical step.

The surgery may optionally be surgery in relation to any of the diseases mentioned herein, such as, cancer surgery, neurosurgery, and the like. The surgery may optionally be laparoscopic, and/or endoscopic.

The analysis may optionally be made in vitro or ex vivo. This may optionally be, e.g., in parallel to a surgical procedure. For example, a specimen, such as, a biopsy, may be obtained during a surgical procedure. Such a provided specimen may then be analysed ex vivo and the results of the analysis may be used to inform, influence or determine a further surgical step.

The method may optionally be carried out on a target that is native. By "native' is meant that the target has not been modified prior to performing the method of the invention. In particular, the target may be native in that the tissue or cells present in the target are not subjected to a step of lysis or extraction, e.g., lipid extraction, prior to performance of the method of the invention. Thus, a target may be native in that it comprises or consists essentially of intact cells Thus, by native is meant that the target has not been chemically or physically modified and is thus chemically and physically native. Optionally, the target may be chemically native, i.e. it may be chemically unmodified, meaning that it has not been contacted with a chemical agent so as to change its chemistry. Contacting a target with a matrix is an example of a chemical modification.

Optionally, the target may be physically native, i.e. it may be physically unmodified, meaning that it has not been modified physically. Freezing, thawing, and/or sectioning are examples of physical modifications. The skilled person will appreciate that although physical actions, such as, freezing, may affect a specimen's chemistry, for the purpose of this invention such an action is not considered to be a chemical modification.

Thus, optionally the target may be chemically native, but not physically native, e.g. because it has been frozen and/or sectioned.

Optionally, the target may be frozen, previously frozen and then thawed, fixed, sectioned, and/or otherwise prepared, as discussed with regard to specimen preparation. Optionally, the method may be carried out on a target that has not undergone a step of preparation specifically for the purpose of mass and/or ion mobility spectrometry analysis.

The target may not have been contacted with a solvent, or a solvent other than water, prior to generating the smoke, aerosol or vapour from the target.

Additionally, or alternatively, the target may not be contacted with a matrix prior to generating the smoke, aerosol or vapour from the target. For example, the target may not be contacted with a MALDI matrix or other matrix for assisting ionisation of material in the target. A MALDI matrix may, e.g., comprise or consist of small organic acids such as a-cyano-4-hydroxycinnamic acid (CHCA) and/or 2,5-dihydroxybenzoic acid (DHB).

The method may optionally be carried out on a target that has been prepared for a particular mass and/or ion mobility spectrometry analysis; and/or that has been prepared for any of the analytical methods mentioned elsewhere herein.

Specimen preparation (for any of the methods of the invention and/or any of the analytical methods disclosed herein) may optionally involve one or more of the following.

The specimen or part thereof may optionally be deposited on a solid surface, such as, a glass or plastic slide.

The specimen may optionally be fixed chemically; or via a frozen section procedure, e.g., to preserve tissue from degradation, and to maintain the structure of the cell and of sub-cellular components such as cell organelles, e.g., nucleus, endoplasmic reticulum, and/or mitochondria. The fixative may, for example, be 10% neutral buffered formalin. The specimen may optionally be processed with e.g., epoxy resins or acrylic resins to allow or facilitate sections to be cut. The sample may optionally be embedded, for example, in paraffin. The specimen may optionally be cut into sections of, for example, 1 pm to 200 nm. For example, the specimen may optionally be about 5 pm thick for light microscopy, or about 80-100 nm thick for electron microscopy. Optionally, the specimen may be cut into sections of at least 1, 3, 5, 7, 9, 10, 12, 14, 16, 18, 20, 22, 24 or 25 pm and no more than 100, 90, 80, 70, 60, 50, 40, 35, 30, 28, or 26 pm, for example, 5-25 pm.

Frozen sections may optionally be prepared, e.g., by freezing and slicing the specimen.

Prior to freezing, the specimen may optionally be embedded, e.g. as described above.

Embedding medium helps conduct heat away from the specimen during freezing, helps protect the tissue from drying during storage, and supports the tissue during sectioning.

Freezing may optionally be performed, e.g., by contacting the specimen with a suitable cooling medium, such as, dry ice, liquid nitrogen, or an agent that has been cooled in dry ice or liquid nitrogen, e.g. isopentane (2-methyl butane). Frozen specimens may optionally be stored at, e.g., between about -80 and -4 degrees Celsius, e.g. at -70 or -20 degrees Celcius.

The specimen or sections thereof may be stained, for example, with Hematoxylin and eosin (H&E stain). Hematoxylin, a basic dye, stains nuclei blue due to an affinity to nucleic acids in the cell nucleus; eosin, an acidic dye, stains the cytoplasm pink.

Any of the methods may optionally include automatic sampling, which may optionally be carried out using a REIMS device. Any of the methods may optionally comprise using a disposable sampling tip.

Biomarkers The method may optionally involve the analysis of one or more biomarkers. A biomarker may be an objective, quantifiable characteristic of, e.g., a cell type, disease status, microbe, compound, and/or biological process.

The term "biomarker" is sometimes used explicitly herein, but it should also be understood that any of the analyses mentioned herein may optionally be the analysis of a biomarker.. Thus, e.g., any reference to analysing a "microbe" should be understood optionally to be "analysing a microbial biomarker"; any reference to analysing "bile" should be understood optionally to be "analysing a bile biomarker"; any reference to analysing a "compound" should be understood optionally to be "analysing a biomarker for that compound"; and so on.

The biomarker may optionally be a spectrometric biomarker. The term "(mass-) spectral biomarker" is used herein to refer to spectrometric data that is characteristic of a cell type, disease status, microbe, compound, and/or biological process, but for simplicity, a spectrometric biomarker may simply be referred to as a "biomarker".

By "characteristic of a cell type" is meant that the biomarker may optionally be used to analyse, e.g., detect, identify and/or characterise said cell type. Optionally, the biomarker may be used to distinguish between cells originating from different tissues; between genotypically and/or phenotypically different cell types; between an animal cell and a microbial cell; between a normal and an abnormal cell; between a wild-type and a mutant cell; and/or between a diseased and a healthy cell.

By "characteristic of a disease status" is meant that the biomarker may optionally be used to analyse the disease status of a target. Optionally, the biomarker may be used to distinguish between healthy and diseased cells; and/or to analyse the severity, grade, and/or stage of a disease.

By "characteristic of a microbe" is meant that the biomarker may optionally be used to analyse, e.g., detect, identify and/or characterise said microbe. As discussed elsewhere herein, identification may be on any level, for example, on a taxonomic level. A biomarker that allows identification of a microbe as belonging to a particular taxonomic level may be referred to as a "taxonomic marker" or "taxonomic biomarker". Thus, a taxonomic marker may be specific for a Kingdom, Phylum, Class, Order, Family, Genus, Species and/or Strain.

By "characteristic of a compound" is meant that the biomarker may optionally be used to analyse, e.g., detect, identify and/or characterise said compound.

By "characteristic of a biological process" is meant that the biomarker may optionally be used to analyse a biological process. Optionally, the biomarker may be used to analyse the start, progression, speed, efficiency, specificity and/or end of a biological process.

Different cell types, disease states, compounds, microbes, biological progresses and the like may be characterised by the presence or absence, and/or relative abundance, of one or more compounds, which may serve as biomarkers. Any reference herein to a biomarker being a particular compound, or class of compounds, should be understood optionally to be the spectrometric data of that compound, or class of compounds.

For example, a reference to a "C24:1 sulfatide (048H91N0115)" biomarker should be understood to be a reference to the spectrometric data corresponding to C24:1 sulfatide (C48H91N0115) which may, e.g., be a signal corresponding to m/z of about 888.6; whereas a reference to a "glycosylated ceramide" biomarker should be understood to be a reference to the spectrometric data corresponding to glycosylated ceramide, which may, e.g., be a signal corresponding to m/z of 842, 844 or 846.

As explained above, a biomarker may be indicative of a cell type, disease status, microbe, compound, and/or biological process. A biomarker which is indicative of cancer may therefore be referred to as a "cancer biomarker"; a biomarker which is indicative of Pseudomonas aeruginosa may be referred to as a "Pseudomonas aeruginosa biomarker" and so on.

Optionally, a spectrometric biomarker may be identified as being the spectrometric data of a particular compound, or class of compounds. Thus, a signal corresponding to a particular mass, charge state, m/z and/or ion mobility (e.g., due to cross-sectional shape or area)may optionally be identified as being indicative of the presence of a particular compound, or class of compounds.

Optionally, spectrometric signal may serve as a biomarker even if a determination has not been made as to which particular compound, or class of compounds gave rise to that signal.

Optionally, a pattern of spectrometric signals may serve as a biomarker even if a determination has not been made as to which particular compounds, or class of compounds, gave rise to one or more signals in that pattern, or any of the signals in a pattern.

The work disclosed herein has led to the identification of a range of biomarkers, as well as allowing the identification of further biomarkers. Optionally, the biomarker may be selected from any of the biomarkers disclosed herein, including in any of the Examples and/or the Tables, particularly Tables 1-19. Optionally, the biomarker may be a biomarker of the substituted or unsubstituted form of any of the biomarkers mentioned herein; and or of an ether, ester, phosphorylated and/or glycosylated form, or other derivative, of any of the biomarkers mentioned herein.

Optionally, the biomarker may be a biomarker of a lipid; a protein; a carbohydrate; a DNA molecule; an RNA molecule; a polypeptide, such as, a ribosomal peptide or a non-ribosomal peptide; an oligopeptide; a lipoprotein; a lipopeptide; an amino acid; and/or a chemical compound, optionally an organic chemical molecule or an inorganic chemical molecule.

A biomarker may optionally be the clear-cut presence or absence of a particular compound, which may optionally manifest itself as the presence or absence of a spectrometric signal corresponding to a specific mass, charge state, m/z and/or ion mobility.

A biomarker may optionally be the relative abundance of a particular biomolecule or compound, which may optionally manifest itself as the relative intensity of a spectrometric signal corresponding to a specific mass, charge state, m/z and/or ion mobility.

A biomarker may optionally be the relative abundance of more or more compounds, which may optionally manifest itself as the relative intensity of two or more spectrometric signals corresponding to two or more specific mass, charge state, m/z and/or ion mobility.

Thus, a biomarker may optionally be an increased or decreased level of one or more compounds, e.g., a metabolite, a lipopeptide and/or lipid species, which may optionally manifest itself as an increase and/or decrease in the intensity of two or more spectrometric signals corresponding to two or more specific mass, charge state, m/z and/or ion mobility.

The presence, absence and relative abundance of a variety of compounds may be referred to as a molecular "fingerprint" or "profile". The totality of the lipids of a cell may be referred to as a lipidomic fingerprint/profile, whereas the totality of metabolites produced by a cell may be referred to as a metabolomic fingerprint/profile.

Thus, the biomarker may be a molecular fingerprint, e.g., a lipid fingerprint and/or a metabolomic fingerprint, more particularly e.g., a (i) a lipidomic profile; (ii) a fatty acid profile; (iii) a phospholipid profile; (iv) a phosphatidic acid (PA) profile; (v) a phosphatidylethanolamine (PE) profile; (vi) a phosphatidylglycerol (PG) profile; (vii) a phosphatidylserines (PS) profile; or (viii) a phosphatidylinositol (PI) profile.

By way of example, phosphatidylglycerol may be found in almost all bacterial types, but it may be present in different bacteria in different relative amounts. Phosphatidylglycerol may be present at a level of only 1-2% in most animal tissues. It may therefore be a biomarker for bacteria in an animal specimen, and/or be a biomarker for specific types of bacteria.

The biomarker may optionally be a direct biomarker or an indirect biomarker. By "direct" biomarker is meant that the spectrometric data is produced directly from the biomarker. For example, if a particular compound has a specific spectrometric signal or signal pattern, then obtaining this signal or signal pattern from a sample provides direct information about the presence of that compound. This may be the case, for example, for a metabolite produced in significant amounts by a cell or microbe. Optionally, in such an example, the spectrometric data from the compound may alternatively or in addition serve as an indirect biomarker for the cell or microbe that produced this compound.

By "indirect" biomarker is meant that the spectrometric data is produced from one or more biomarkers that is/are indicative of a particular compound, biological process, and/or type of microbe or cell. Thus, an indirect biomarker is spectrometric data generated from one or more molecules that provides information about a different molecule. For example, a molecular fingerprint, such as, a lipid fingerprint, may be indicative of the expression of a particular protein, e.g. a receptor; or of a particular cell type or microbial type.

A lipid biomarker may optionally be selected from, e.g., fatty acids, glycerolipids, sterol lipids, sphingolipids, prenol lipids, saccharolipids and/or phospholipids. A brief overview of various lipids is provided below, but it must be appreciated that any particular lipid may fall into more than one of the groups mentioned herein.

A fatty acid is an aliphatic monocarboxylic acid. The fatty acid may optionally have a carbon chain comprising precisely or at least 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 36, 38 or 40 carbons. It may optionally be monounsaturated, polyunsaturated, or saturated.

It may optionally be an eicosanoid. It may, for example, be oleic acid, palmitic acid, arachidonic acid, a prostaglandin, a prostacyclin, a thromboxane, a leukotriene, or an epoxyeicosatrienoic acid.

The glycerolipid may optionally be selected from e.g., monoacylglycerol, diacylglycerol, and/or triacylglycerol.

The sterol may optionally be selected from free sterols, acylated sterols (sterol esters), alkylated sterols (steryl alkyl ethers), sulfated sterols (sterol sulfate), sterols linked to a glycoside moiety (steryl glycosides) and/or acylated sterols linked to a glycoside moiety (acylated sterol glycosides).

The sterol may optionally have an aliphatic side chain of precisely or at least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 21, 22, 23, 24, 25, 26, 27, 28, 29, 20, 35 or 40 carbon atoms.

The number of carbon atoms in the aliphatic side chain may be expressed by the letter C followed by the number, e.g., C27 for cholesterol. It may, for example, be selected from cholesterol, cholesterol sulphate, ergosterol, lanosterol, dinosterol (4a,23,24-trimethyl-5acholest-22E-en-3b-ol), oxysterol and/or a derivative of any thereof.

A phospholipid may comprise two fatty acids, a glycerol unit, a phosphate group and a polar molecule. The Phospholipid may optionally comprise an ester, ether and/or other 0-derivative of glycerol. The phospholipid may optionally be selected from, e.g., Phosphatidylglycerol, diphosphatidylglycerol (cardiolipin), Acylphosphatidylglycerol (1,2-diacylsn-glycero-3-phospho-(3'-acy1)-I-sn-glycerol), and/or plasmalogen.

The phosphatidylglycerol lipid may optionally be selected from phosphatidic acids (PAs), phosphatidylethanolamines (PEs), phosphatidylglycerols (PGs), phosphatidylcholines (PCs), phosphatidylinositols (P1s) and/or phosphatidylserines (PSs). A sphingolipid is a lipid containing a sphingoid. It may optionally be selected from, e.g., a ceramide, i.e. an N-acylated sphingoid; sphingomyelin, i.e. a ceramide-1-phosphocholine; phosphoethanolamine dihidroceramide, and/or a glycosphingolipid, i.e. a lipid containing a sphingoid and one or more sugars. For example, it may optionally be a glycosylated ceramide.

The biomarker may optionally be a metabolite, such as, a primary or a secondary metabolite; an antibiotic; a quorum sensing molecule; a fatty acid synthase product; a pheromone; and/or a biopolymer.

A biomarker compound may optionally be characterised by one or more of the following functional groups: alcohol, ester, alkane, alkene, alkyne, ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic, carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it may additionally be identified as being primary, secondary or tertiary, e.g., a primary alcohol, a secondary amine, or the like.

For example, it may optionally be a terpene; prenylquinone; sterol; terpenoid; alkaloid; glycoside; surfactin; lichenysin, 2-Heptyl-3-hydroxy-4(1 H)-quinolone or 2-hepty1-3,4-dihydroxyquinoline ("PQS" or Pseudomonas quinolone signal); 4-hydroxy-2-heptylquinoline ("HNC"); phenol, such as, a natural phenol; phenazine; biphenyl; dibenzofurans; beta-lactam; polyketide; rhamnolipid; mycolic acids; and/or polyhydroxyalkanoates; The biomarker may optionally be selected from, e.g., Glycerophosphocholines, Sphingomyelins, Glycerophospholipids, Galactoceramides, Glycerophosphoinositols, Glycerophosphoserines, Glycerophosphoglycerols, Cholesterol sulphate, sulfatides, seminolipids, citric acid, Glycerophosphoethanolamines, Glycerophosphoethanolamines, 2-hydroxygluterate, glutamine, glutamate, succinate, fumarate, palmitoylglycine, ubiquinones, gadoteridol and/or any of the other biomarkers mentioned herein, including any of the Tables.

The inventors have identified inter alia the following biomarkers: Mycolic acids for bacteria belonging to the Corynebacterineae suborder such as Mycobacterium spp., Corynebacterium spp. and Rhodococcus spp.. In particular, the following mycolic acids have been detected from the corresponding genera: Mycobacterium spp.: C77-C81 (even and odd numbered, 0-2 unsaturations); Corynebacterium spp.: C28-C36 (even numbered, 0-2 unsaturations); Nocardia spp.: C48-056 (even numbered, 0-3 unsaturations); Rhodococcus spp.: C28-C38 (even and odd numbered, 0-4 unsaturations).

A variety of sphingolipid species were found to be specific for members of the Bacteroidetes phylum. These sphingolipids include oxidized ceramides species, phosphoethanolamine dihydroceramides and C15:0-substituted phosphoglycerol dihydroceramides and dihydroceramide. Among those sphingolipid species, a series of galactosylated sphingolipids was found to be specific for Bacteroides fragilis (Bacteroides fragilis alpha-Galactosylceramides).

Among bacteria, plasmalogens are highly specific for anaerobic bacteria such as Clostridium spp. and Fusobacterium spp.. This is due to the fact that aerobic bacteria lost the biochemical pathway required for plasmalogen synthesis. Humans are able to synthesize plasmalogens (although via a different biochemical pathway from anaerobes), although these were generally found to have longer chain lengths than bacterial plasmalogens.

Other biomarkers that are indicative of a certain group of bacteria include, for instance, lipopeptides that are produced specifically by certain Bacillus species, such as, surfactin for B. subtilis and lichenysin for B. licheniformis. Production of these two molecules also enables straightforward differentiation of these otherwise very closely related bacteria. A further example includes PQS-derived quorum-sensing molecules and mono-and di-rhamnolipid species found for Pseudomonas aeruginosa.

Quorum sensing is a form of cell-to-cell communication which relies on the principle that when a single microbe releases quorum sensing molecules into the environment, the concentration of such molecules is too low to be detected. However, when sufficient bacteria are present, quorum sensing molecule concentrations reach a threshold level that allows the microbes to sense a critical cell mass and, in response, to activate or repress particular genes. Quorum sensing molecules may therefore also be referred to as autoinducers. Pathogens may use quorum sensing molecules as virulence factors.

Some examples of quorum sensing molecules are listed above. Additional examples include N-acyl homoserine 'octanes (N-acyle HSLs), such as, 3-oxo-C8-HSL, 3-oxo-C10-HSL, or 3-oxo-C1rHSL; diketopiperazines; 3-hydroxypalmitic acid methyl ester; and peptide-based quorum sensing molecules, such as, that of Staphylococcus aureus, which is an oligopeptide that has been termed the autoinducing peptide (AIP), encoded by the gene agrD. The active AIP is 7-9 amino acids, with a 5-membered thiolactone ring.

By way of example, sphingomyelin lipids may optionally be a biomarker, e.g. for cancer; ergosterol may optionally be a biomarker, e.g., for fungi; dinosterol may optionally be a biomarker, e.g. for dinoflagellates; cholesterol sulphate may optionally be a biomarker, e.g., for cancer; 2-hydroxygluterate may optionally be a biomarker, e.g., for cancer; and/or one or more sulfatides may optionally be a biomarker, e.g., for cancer, for example, astrocytoma. Optionally, the sulfatide may be selected from C48H91N011S, C48F192N012S, and/or Csol-194N011S.

so-CI 5:0-substituted phosphoglycerol dihydroceramides may be specific for the Porphyromonadaceae family. m/z = 566.4790 may be a biomarker for members of the Flavobacteria class.

The method of the invention may optionally involve the analysis of an exogenous compound, i.e. a compound that was administered to a subject and/or brought into contact with a subject or specimen. Thus, the biomarker may be an exogenous compound. The exogenous compound may optionally, e.g., be a contrast agent, e.g., a gadolinium-containing contrast agent, optionally selected from gadoterate, gadodiamide, gadobenate, gadopentetate, gadoteridol, gadoversetamide, gadoxetate, and/or gadobutrol.

Compounds The method may optionally involve the analysis of one or more compounds. Unless otherwise stated, the terms "compound", "molecule" and "biomolecule" are used interchangeably herein.

The compound may optionally be intracellular and/or extracellular. It may optionally be endogenous, i.e. produced by the subject, and/or exogenous, i.e. added to the subject, tissue, cell, and/ or microbe.

The compound may optionally comprise or consist of any of the compounds or classes of compounds mentioned herein, e.g. any of the biomarker compounds mentioned herein. Optionally, it may comprise or consist of, for example, a lipid, such as, a glycolipid or phospholipid; carbohydrate; DNA; RNA; protein; polypeptide, such as, a ribosomal peptide or a non-ribosomal peptide; oligopeptide; lipoprotein; lipopeptide; amino acid; and/or chemical molecule, optionally an organic chemical molecule.

The compound may optionally be linear, cyclic or branched.

The compound may optionally be a metabolite, such as, a primary or a secondary metabolite; an antibiotic; a quorum sensing molecule; a fatty acid synthase product; a pheromone; and/or a biopolymer.

The compound may optionally be characterised by one or more of the following functional groups: alcohol, ester, alkane, alkene, alkyne, ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic, carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it may additionally be identified as being primary, secondary or tertiary, e.g., a primary alcohol, a secondary amine, or the like.

Analysis of tissues The term "tissue is used herein to denote a structure of cells, which may optionally be, for example, a structure, an organ, or part of a structure of organ. The tissue may be in vivo or ex vivo. It may be in or from a human or a non-human animal.

Examples of tissues that may optionally be analysed are adrenal gland tissue, appendix tissue, bladder tissue, bone, bowel tissue, brain tissue, breast tissue, bronchi, ear tissue, oesophagus tissue, eye tissue, endometrioid tissue, gall bladder tissue, genital tissue, heart tissue, hypothalamus tissue, kidney tissue, large intestine tissue, intestinal tissue, larynx tissue, liver tissue, lung tissue, lymph nodes, mouth tissue, nose tissue, pancreatic tissue, parathyroid gland tissue, pituitary gland tissue, prostate tissue, rectal tissue, salivary gland tissue, skeletal muscle tissue, skin tissue, small intestine tissue, spinal cord, spleen tissue, stomach tissue, thymus gland tissue, trachea tissue, thyroid tissue, ureter tissue, urethra tissue, soft and connective tissue, peritoneal tissue, blood vessel tissue and/or fat tissue; (ii) grade I, grade grade III or grade IV cancerous tissue; (iii) metastatic cancerous tissue; (iv) mixed grade cancerous tissue; (v) a sub-grade cancerous tissue; (vi) healthy or normal tissue; or (vii) cancerous or abnormal tissue.

The analysis may optionally relate to a disease or condition, such as, any of the diseases or conditions listed in this section and/or elsewhere herein. The terms "disease" and "condition" are used interchangeably herein.

The condition may optionally be a skin condition selected, for example, from Acne, Alopecia, Boils, Bowen's Disease, Bullous pemphigoid (BP), Carbuncle, Cellulitis, Chilblains, Cysts, Darier's disease, Dermatitis, Dermatomyositis, Eczema, Erythema, Exanthema, Folliculitis, Frostbite, Herpes, lchthyosis, Impetigo, Intertrigo, Keratosis, Lichen planus, Linear IgA disease, Melanoma, Moles, Onychomycosis, Papillioma, Petechiae, Prurigo, Psoriasis, Rosacea, Scabies, Scleroderma, Sebaceous Cyst, Shingles/ Chickenpox, Telangiectasia, Urticaria (Hives), Warts and/or Xeroderma.

The condition may optionally be a liver condition selected from, for example, hepatitis, fatty liver disease, alcoholic hepatitis, liver sclerosis and/or cirrhosis.

Lung conditions may optionally be selected from, for example, Asthma, Atelectasis, Bronchitis, Chronic obstructive pulmonary disease (COPD), Emphysema, Lung cancer, Pneumonia, Pulmonary edema, Pneumothorax, and/or Pulmonary embolus.

The thyroid gland is an endocrine gland which normally produces thyroxine (T4) and triiodothyronine (T3). The condition may optionally be a thyroid condition, e.g., hypothyroidism or hyperthyroidism.

Optionally, a lesion, optionally of any of the tissues mentioned herein, may be analysed. A lesion is region in a tissue which is abnormal as a consequence of, e.g., injury or disease. The lesion may, for example, be selected from a wound, an ulcer, an abscess, and/or a tumour. The lesion may, for example, be a diabetic lesion, such as, a diabetic limb or digit, or a diabetic ulcer.

Further examples of tissues that may be analysed are discussed elsewhere herein, e.g., tissue affected by, or in the vicinity of, cancer, necrosis, microbes and the like. For example, the tissue may optionally comprise or consist of mucosa, which is discussed elsewhere herein.

Optionally, the method may involve the analysis of the cellular composition of a tissue.

For example, the proportion of one or more particular cell types may be analysed. The cell types may optionally be selected from any known cell types, e.g., any of the cell types mentioned herein.

Optionally, the method may comprise analysing an immune response to a disease, which may optionally be selected from any of the diseases listed elsewhere herein, e.g., to a cancer and/or an infection. Thus, optionally, cells that form part of a subject's immune response may be analysed. For example, the presence. location, spatial distribution, concentration and/or type of one or more cells that form part of a subject's immune response may be analysed, e.g., in a tissue.

Cancer and/or tumour analysis The method of the invention may optionally involve the analysis of a cancer or tumour cell or tissue. The method of the invention may optionally involve the analysis of a cancer biomarker.

The uncontrolled growth and division of cells may give rise to cancer, such as, blood cancers or malignant tumours; or to benign tumours. Cells that grow and divide in an uncontrolled way may also be referred to as neoplastic cells. A cancer may therefore also be referred to as a "neoplasm" and a tumour may be referred to as comprising "neoplastic cells".

A "tumour" is a population of cells characterized by abnormal growth. Most tumours are solid, i.e. a mass of cells. Tumours are typically classed as either benign or malignant, based on the criteria of spread and invasion. Malignant tumours are capable of invading and destroying surrounding tissues. Their cells may also spread beyond the original site of the tumour. Benign tumours do not possess these characteristics, but benign tumours may progress to a malignant stage, so it can be useful to detect and potentially treat benign tumours. For example, in oral squamous carcinoma, neoplasia is not usually treated, but this condition can rapidly progress into a malignant stage where parts or the whole tongue has to be surgically removed. Moreover, benign tumours may still be per se undesirable, particularly if they are large and grow adjacent to vital organs, and so treatment of a benign tumour which thereby reduces subsequent similar benign tumours can be desirable.

Thus, "malignant" cells may be defined as cells that exhibit uncontrolled proliferation, evading growth suppressors, avoiding cell death, limitless proliferative capacity (i.e. immortality), metastatic capacity and/or genetic instability, or any combination thereof.

Optionally, a tumour may be benign or malignant, which may optionally be known before the method of the invention is performed. Optionally, a tumour may be analysed to determine whether it is benign or malignant. Thus, the method of the invention may optionally involve the characterisation of a tumour as being benign or malignant.

Metastasis is a complex series of biological steps in which cancerous cells leave an original site and migrate to another site in a subject via a number of different possible routes, such as via the bloodstream, the lymphatic system, or by direct extension. Metastatic cancer or "metastasis" is the spread of a cancer from one organ to another organ or another site in a subject. Thus, metastatic cancer gives rise to metastatic tumours, i.e. "metastases", at distal sites from a primary tumour site within a subject.

The method of the invention may optionally involve the characterisation of a tumour as being metastatic. Optionally, one or more metastases may be analysed.

Optionally, a pre-cancerous state may be analysed.

A great hurdle in the search for a way to treat cancer is that cancers develop from cells which originate from the subject's own body. The immune system struggles to recognise them as abnormal. Recognition of foreign or abnormal cells by the immune system typically involves the detection of molecules located at the cell surface, antigens. Most cancer cells possess at least one kind of antigen which distinguishes them from normal cells and in many cases the antigens are specific for a particular type of cancer. Some cancer cells may possess a variety of antigens, whilst others may only possess a single type of antigen. The type of antigen, the number of different antigens and the prominence of the antigens on the cell surface may all influence the chances that the immune system may recognise the cancer cells as abnormal. Many types of cancer possess very few antigens, or only antigens which are poorly recognised by the immune system as foreign and are thus capable of escaping recognition and destruction by the immune system. The type and quantity of antigens possessed by any particular cancer type thus plays a big part in determining how "immunogenic" a cancer is. By "immunogenic" is meant the ability to elicit an immune response, so the more immunogenic a cancer is, the more likely it is that it will be recognised and attacked by the immune system. The method of the invention may optionally involve analysing how immunogenic a cancer is.

Tumours comprise two distinct, but interdependent, compartments: the parenchyma consisting essentially of neoplastic cells; and the stroma. The stroma comprises a variety of non-neoplastic cell types, including, for example, fibroblasts, myofibroblasts, glial cells, epithelial cells, fat cells, immune-competent cells, vascular cells, and/or smooth muscle cells; as well as an extracellular matrix (ECM) and extracellular molecules, such as, inflammatory cytokines and/or chemokines. Macrophages may, for example, represent up to 50% of the tumour mass.

Although most cells in the stroma initially possess certain tumour-suppressing abilities, the stroma typically changes during malignancy and eventually promotes growth, invasion, and/or metastasis. Stromal changes may include the appearance of carcinoma-associated fibroblasts (CAFs) through the transdifferentiation of fibroblasts to CAFs, typically driven to a great extent by cancer-derived cytokines, such as, transforming growth factor-n. CAFs may constitute a major portion of the tumour stroma and play a crucial role in tumour progression.

The method of the invention may optionally involve the analysis of a tumour stroma. The method may optionally involve the analysis of a tumour margin, for example, the margin between the parenchyma, the stroma, and/or healthy tissue.

"Tumour heterogeneity" is a term used to refer to differences between tumours of the same type in different subjects, and between neoplastic cells within a tumour. Both can lead to divergent responses to therapy. The differences may, for example, be genetic and/or epigenetic.

The method of the invention may optionally involve the analysis of tumour heterogeneity. The cancer or tumour may optionally be selected from, for example, carcinomas, sarcomas, leukaemias, lymphomas and gliomas.

More particularly, it may optionally be selected from, for example, Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Adrenocortical Carcinoma, adenoma, Anal Cancer, Appendix Cancer, Astrocytomas, Basal Cell Carcinoma, Bile Duct Cancer, Birch-Hirschfield, Blastoma, Bladder Cancer, Bone Cancer, Ewing Sarcoma, Osteosarcoma, Malignant Fibrous Histiocytoma, Brain Stem Glioma. Brain cancer, glioblastoma multiforme ("GBM"), Astrocytomas, Spinal Cord cancer, Craniopharyngioma, Breast Cancer, Bronchial Tumour, Burkitt Lymphoma, Carcinoid Tumour, Cervical Cancer, Cholangiocarcinoma, Chordoma, Chronic Lymphocytic Leukemia (CLL), Chronic Myelogenous Leukemia (CML), Chronic Myeloproliferative Neoplasms, Colon Cancer, Colorectal Cancer, Craniopharyngioma, Childhood, Ductal Carcinoma In Situ (DCIS), Endometrial Cancer, Ependymoma, Esophageal Cancer, Esthesioneuroblastoma, Fibroadenoma, Intraocular Melanoma, Retinoblastoma, Fallopian Tube Cancer, Gallbladder Cancer, Gastric (Stomach) Cancer, Germinoma, Hairy Cell Leukemia, Head and Neck Cancer, Heart Cancer, Heptacarcinoma, Hodgkin Lymphoma, Hypopharyngeal Cancer, Kahler, Kaposi Sarcoma, Kidney cancer, Laryngeal Cancer, Leiomyoma, Lip and Oral Cavity Cancer, Liver Cancer, Lung Cancer (such as, Non-Small Cell or Small Cell), Lymphoma, Lymphoblastoma, Male Breast Cancer, Malignant Fibrous Histiocytoma of Bone, Melanoma, Melanocarcinoma, Medulloblastoma, Merkel Cell Carcinoma, Mesothelioma, Mouth Cancer, Myeloma, Multiple Myeloma, Mycosis Fungoides, Myeloproliferative disorder, Nasal Cavity and Paranasal Sinus Cancer, Nasopharyngeal Cancer; Neuroblastoma, Nephroblastoma, Non-Hodgkin Lymphoma, Oral Cancer, Oropharyngeal Cancer, Osteosarcoma, Ovarian Cancer, Pancreatic Cancer; Papillomatosis, Paraganglioma, Parathyroid Cancer, Penile Cancer, Peritoneal cancer, Pharyngeal Cancer, Pheochromocytoma, Pineoblastoma; Pituitary Tumour, Prostate Cancer, Rectal Cancer, Retinoblastoma, Rhabdomyosarcoma, Salivary Gland Cancer, Sezary Syndrome, Skin Cancer, Seminoma, Teratoma; Testicular Cancer, Throat Cancer, Thyroid Cancer, thoracic cancer, Urethral Cancer, Vaginal Cancer, Vulvar Cancer, Waldenstrom macroglobulinemia, and/or Wlm's tumour. In the above list, any reference to a "cancer" or a "tumour" should be understood to include a reference to a "cancer and/or a tumour" of that type.

Optionally, the brain cancer may be glioblastoma multiforme, glioblastoma, giant cell glioblastoma, recurrent gliobastoma, anaplastic astrocytoma, oligodendroglioma and/or diffuse astrocytoma.

If the cancer is breast cancer, it may optionally be selected from, for example, ductal carcinoma in situ (DCIS), lobular carcinoma in situ (LCIS), Invasive breast cancer (NST), Invasive lobular breast cancer, Inflammatory breast cancer, breast cancer associated with Paget's disease and angiosarcoma of the breast.

The cancer may be caused by, associated with, and/or characterised by a mutation or other genetic variation, which may optionally result in the altered expression of a molecule, e.g. a molecule comprising or consisting of a lipid, such as, a glycolipid or phospholipid; a carbohydrate; DNA; RNA; a protein; a polypeptide, such as, a ribosomal peptide or a non-ribosomal peptide; an oligopeptide; a lipoprotein; a lipopeptide; an amino acid; and/or a chemical compound, optionally an organic chemical compound. More particularly, a mutation may optionally result in the altered expression of a protein and/or metabolite.

A cancer may optionally express one or more metabolites that may serve as a biomarker for that cancer. For example, optionally a metabolite such as succinate, fumarate, 2-HG, and/or any of the other metabolites mentioned herein may accumulate in a cancer.

Subtypes of cancer may optionally be identified, e.g., based on such altered expression.

For example, a cancer may optionally be identified as being of a particular subtype based on the expression, or lack thereof, of a receptor, e.g., selected from estrogen receptors (ER), progesterone receptors (PR) and human epidermal growth factor receptor 2 (HER2). A cancer may therefore, for example, be referred to as ER negative if it lacks expression of ER; or be referred to as triple-negative breast cancer (TNBC); if it is ER-, PR-and Her2-.

The mutation may optionally, e.g., be in a gene encoding isocitrate dehydrogenase 1 (IDH1) and/or 2 (10H2) yielding mutant enzymes capable of converting alpha-ketoglutarate to 2-hydroxyglutarate (2-HG). Such a mutation may optionally be present, e.g., in a glioma, intrahepatic cholangiocarcinoma, acute myelogenous leukaemia (AML) and/or chondrosarcomas. 2-HG may thus be referred to as an oncometabolite. 2-HG may be present in very small amounts in normal tissues, whereas it may be present in high concentrations, e.g., several micromoles per gram of tumour, in mutant tumours.

Thus, a cancer subtype may have a specific biomarker. The method of the invention may optionally involve the analysis of a cancer subtype.

The method may optionally involve the analysis of the phenotype and/or genotype of a cancer, which may optionally involve an analysis of any of the mutations discussed above. The grade of a tumour is a measure of the aggressive potential of the tumour. It is an indicator of how quickly a tumour is likely to grow and spread. Generally speaking, "low grade" cancers tend to be less aggressive than "high grade" cancers.

Tumour grade is the description of a tumour based inter alia on the differentiation stage of the tumour cells. The differentiation stage may be assessed microscopically. In layman's terms, it is a measure of how abnormal the tumour cells and the tumour tissue look under a microscope. If the cells of the tumour and the organization of the tumour's tissue are close to those of normal cells and tissue, the tumour may be called "well-differentiated." If the tumour comprises abnormal-looking cells and/or the tumour tissue lacks normal tissue structures, the tumour may be called "undifferentiated" or "poorly differentiated".

Based on these and other differences in microscopic appearance, a numerical "grade" may be assigned to most cancers. The factors used to determine tumour grade vary between different types of cancer. Thus, grading systems differ depending on the type of cancer.

In general, tumours may optionally be graded as 1, 2, 3, or 4, depending on the amount of abnormality. In Grade 'I tumours, the tumour cells and the organization of the tumour tissue appear close to normal. These tumours tend to grow and spread slowly. In contrast, the cells and tissue of Grade 3 and Grade 4 tumours do not look like normal cells and tissue. Grade 3 and Grade 4 tumours tend to grow rapidly and spread faster than tumours with a lower grade. If a grading system for a tumour type is not specified, the following system may optionally be used: GX: Grade cannot be assessed (undetermined grade) 01: Well differentiated (low grade) G2: Moderately differentiated (intermediate grade) G3: Poorly differentiated (high grade) G4: Undifferentiated (high grade) Breast and prostate cancers are the most common types of cancer that have their own grading systems.

The Nottingham grading system (also called the Elston-Ellis modification of the ScarffBloom-Richardson grading system) may optionally be used for breast cancer. This system grades breast tumours based on the following features: (i) Tubule formation: how much of the tumour tissue has normal breast (milk) duct structures; (H) Nuclear grade: an evaluation of the size and shape of the nucleus in the tumour cells; and (iii) Mitotic rate: how many dividing cells are present, which is a measure of how fast the tumour cells are growing and dividing.

Each of the categories gets a score between 1 and 3; a score of "1" means the cells and tumour tissue look the most like normal cells and tissue, and a score of "3" means the cells and tissue look the most abnormal. The scores for the three categories are then added, yielding a total score of 3 to 9. Three grades are possible: (i) Total score = 3-5: G1 (Low grade or well differentiated); (ii) Total score = 6-7: G2 (Intermediate grade or moderately differentiated); (iii) Total score = 8-9: G3 (High grade or poorly differentiated).

The Gleason scoring system may optionally be used to grade prostate cancer. The Gleason score is based on biopsy samples taken from the prostate. The pathologist checks the samples to see how similar the tumour tissue looks to normal prostate tissue. Both a primary and a secondary pattern of tissue organization are identified. The primary pattern represents the most common tissue pattern seen in the tumour, and the secondary pattern represents the next most common pattern. Each pattern is given a grade from 1 to 5, with 1 looking the most like normal prostate tissue and 5 looking the most abnormal. The two grades are then added to give a Gleason score. Based on a recommendation of the American Joint Committee on Cancer Gleason scores may be grouped into the following categories: (i) Gleason X: Gleason score cannot be determined; (H) Gleason 2-6: The tumour tissue is well differentiated; (Hi) Gleason 7: The tumour tissue is moderately differentiated; (iv) Gleason 8-10: The tumour tissue is poorly differentiated or undifferentiated.

With regard to bladder cancer, the term "high grade bladder cancer" (HGBC) means and includes a tumour that has invaded into the muscularis propria of the bladder: non-muscle invasive bladder cancer (NMIBC, Ta, TI) and muscleinvasive bladder cancer (MIBC, >T2) including bladder cancer metastases.

The method of the invention may optionally involve the analysis of a tumour grade.

In addition or instead of tumour grade, one or more other factors, such as cancer stage and/or a subject's age and general health, may be used to develop a treatment plan and to determine a subject's prognosis. Generally, a lower grade indicates a better prognosis. A higher-grade cancer may grow and spread more quickly and may require immediate or more aggressive treatment. The importance of tumour grade in planning treatment and determining a subject's prognosis is particularly important for cancers, such as, soft tissue sarcoma, primary brain tumours, and breast and/or prostate cancer.

Staging is a well-known way of describing the size of a (primary) tumour and how far it has grown. A cancer may optionally be stage 1,2,3 or 4; or, alternatively viewed, early stage, advanced stage and/or metastatic; or, alternatively viewed, non-invasive non-metastatic, noninvasive metastatic, invasive non-metastatic or invasive metastatic.

Stage 1 may also be referred to as "early stage" cancer and is characterised by a tumour which is relatively small and contained within the organ it started in. Stage 2 typically means the cancer has not started to spread into surrounding tissue, but the tumour is larger than in stage 1. Cancer cells may or may not have spread into lymph nodes close to the tumour, depending on the particular type of cancer. Stage 3 may also be referred to as "advanced" cancer. It is characterised by a large tumour, which may have started to spread into surrounding tissues. It is also characterised by cancer cells in at least some of the lymph nodes. Stage 4 may also be referred to as "metastatic" cancer. The stages of a (primary) tumour may be referred to as T1, T2, T3 and/or T4.

The method may optionally be carried out on cancerous tissue in vivo, and/or on a specimen, such as, a biopsy. The specimen may optionally comprise tumour tissue; stroma tissue and/or healthy tissue. The specimen may optionally comprise part or all of a tumour.

The specimen may optionally comprise tissue from a lymph node, e.g., a sentinel lymph node and/or a regional lymph node. A regional lymph node is a lymph node that drains lymph from the region around a tumour. A sentinel lymph node is defined as the first lymph node to which cancer cells are most likely to spread from a primary tumour. Sometimes, there can be more than one sentinel lymph node.

A cancer may alternatively or in addition be staged by reference to lymph nodes. The letter N followed by a number from 0 to 3 indicates whether the cancer has spread to lymph nodes near the primary tumour and, if so, how many lymph nodes are affected. These stages may be referred to as NX, NO, N1, N2 and/or N3.

NX: Nearby lymph nodes cannot be assessed (for example, if they were removed previously).

NO: Cancer has not spread to nearby lymph nodes. N1 to N3 indicate the severity of spread of the cancer to lymph nodes. The exact staging criteria vary from cancer to cancer, but as a general rule, N1 denotes a spread to at least 1 or a small number of lymph nodes; N2 denotes a spread to a greater number of lymph nodes; and N3 denotes a spread to an even greater number of lymph nodes.

A cancer may alternatively or in addition be staged by reference to Metastasis.

MX: Distant spread (metastasis) cannot be assessed; MO: No distant spread is found on x-rays (or other imaging procedures) or by physical exam; M1: Cancer has spread to distant organs. The method may optionally involve the analysis of a cancer stage.

Optionally, the type, subtype, phenotype, grade and/or stage of a cancer or tumour may provide prognostic information. Thus, optionally, the method may be a prognostic method and/or involve a step of making a prognosis.

The method may optionally involve the analysis of a cancer in an animal model, e.g. in a xenograft model. For example, a tumour or specimen thereof may be obtained from a subject, and/or a tumour cell line may be used. The tumour cell may optionally be genetically manipulated, e.g. it may be transformed by introducing a transgene and/or by exposing it to a mutagen. The tumour cell may optionally be cultured ex vivo. The (optionally transformed) tumour cell may optionally be injected or xenografted into an animal model, which may optionally be selected from any of the animals mentioned herein. The animal model may optionally be treated with a known anti-cancer agent and/or a test agent. The tumour, its stroma, and/or the tissue in the vicinity of the tumour, e.g. the tumour microenvironment, may optionally be analysed. This method may optionally be used to analyse the effect of a transgene on a cancer; to analyse the effect of an anti-cancer agent on a cancer; and/or to analyse the effect of a test agent on a cancer.

Genetic manipulation of cells may optionally involve targeted mutagenesis and/or random mutagenesis, which may optionally, e.g., be the knock-out, alteration, and/or insertion of genetic information. A cell that has been manipulated via targeted mutation may be referred to as a "transformed' cell, particularly if a new gene or gene variant, i.e. a "transgene" has been inserted. A gene that has been knocked-out may also be referred to as a silenced gene. The analysis of cancer will now be discussed in more detail with reference to ovarian cancer, but it should be understood that the information applies mutatis mutandis to any other cancer types, e.g., any of the other cancer types listed elsewhere herein.

Primary epithelial ovarian cancer (EOC) has a poor prognosis and remains the most lethal gynaecological malignancy. In greater than 80% of cases, EOC presents with late stage disease, once the disease has already left the realms of the pelvis. Disease burden at this stage can be extensive and involve metastatic dissemination to the upper abdomen, diaphragm, hepatic and splenic parenchyma as well as distant spread beyond the abdominal cavity. Five-year relative survival for EOC presenting at stage three and four is 18.6% and 3.5% respectively.

Cytoreductive surgery has proven prognostic benefit for progression-free and overall survival, especially in patients with stage III and IV disease. One study shows three-year overall survival in patients with zero residual disease to be 72.4% versus 45.2% in patients with >1cm residual disease. Cytoreductive surgery may be the only treatment, but alternatively and/or in addition patients may receive, chemotherapy, e.g., platinum and/or taxane based chemotherapy. Maximal cytoreduction generally confers survival benefit.

Once disease has progressed beyond the ovaries and affects other peritoneal surfaces, it may be difficult to discriminate from non-malignant disease. This identification may be more challenging in a delayed primary surgery setting after the administration of neo-adjuvant chemotherapy. Lesions may undergo morphological changes, which may include fibrosis, calcification and/or lymphocytic infiltration. The surgeon may rely, e.g., on pre-chemotherapy computed tomography imaging and/or experience to identify the location and malignant nature of lesions. The robust evidence that proves survival benefit from maximal surgical effort may promote a more radical surgical approach. Debulking operations for EOC may include, e.g., appendicectomy, splenectomy, peritonectomy, omentectomy, diaphragmatic stripping, and/or total hysterectomy with bilateral salpingo-oophorectomy. Until recently, there has been no technology to accurately guide the surgeon during the operation. Surgeons cannot be sure of complete resection of disease and healthy margins of tissue may be taken in excess.

Prior to surgery, the precise histopathological nature of the pelvic or ovarian tumour is often unknown. Only during surgery can an attempt at diagnosis be made. The only established technique for intraoperative diagnosis is histopathological frozen section, which is time consuming, costly, and its diagnostic accuracy varies. A meta-analysis of 18 studies showed diagnostic sensitivity for benign tumours to be 65-97% and 71-100% for malignant tumours at frozen section. Other studies have shown that borderline ovarian tumours are especially difficult to characterise at frozen section with diagnostic sensitivity ranging from 25-87%. Low stage borderline ovarian tumours can be treated more conservatively and younger women may wish to opt for unilateral oophorectomy to preserve their fertility. With frozen section diagnostic accuracy for borderline tumours being so low, it is likely that many women of child bearing age have radical cytoreductive surgery, which may be unnecessary.

During surgery, electrosurgical diathermy instruments may be used to cut tissue as they provide haemostasis. Surgical smoke is a by-product when cutting the tissue, which has been historically extracted from the surgical field. However! this smoke may be a rich source of biological information and mass spectrometry (MS) and/or ion mobility spectrometry may be used to measure its metabolomic composition.

This coupling of the surgical diathermy, which converts tissue components into gas-phase ionic species, with a mass spectrometer has been described as rapid evaporative ionisation mass spectrometry (REIMS) technology. Infra-operative direct sampling with MS was in the past not possible, as MS usually requires sample preparation, which is not possible in a surgical setting. REIMS functions at atmospheric pressure in ambient conditions, which makes it ideal for intra-operative use.

Coupling of REIMS technology with handheld sampling devices has resulted in iKnife sampling technology, which can provide intra-operative tissue identification. The iKnife sampling technology allows surgeons to more efficiently resect tumours intra-operatively through minimizing the amount of healthy tissue removed whilst ensuring that all the cancerous tissue is removed.

Statistical analysis of REIMS spectra with comparison to histologically authentic spectral libraries may optionally be used for the unambiguous in vivo or ex-vivo identification of major tissue types, optionally selected from any of the tissue types mentioned elsewhere herein, such as, liver, lung, and/or colon. It may optionally be used to identify the origin of metastatic lesions in an ex-vivo and/or in-vivo setting. It may optionally be used in an in-vivo endoscopic setting, e.g., to classify intestinal wall, cancer and/or polyps.

The present application presents the first use of the surgical diathermy with spectrometric analysis in gynaecological targets. As explained in the Examples, particularly Example 13, samples ranging from normal through to malignant were included to demonstrate the potential of the method as a real-time diagnostic surgical tool.

Analysis of necrosis "Necrosis" is unprogrammed cell death, which may be contrasted with apoptosis, which is a form of programmed cell death.

Necrosis typically involves damage to the cell membrane and/or damage to intracellular compartments, such as. lysosomes. Necrosis is typically accompanied by the release of intracellular molecules, such as, enzymes, organic chemical molecules and the like. For example, it may include the release of the lysosomal enzymes. The release of such molecules may cause inflammation and/or damage to neighbouring cells.

The necrosis may optionally be caused by, or associated with, for example, injury, infection, cancer, infarction, toxins, inflammation, lack of proper care to a wound site, frostbite, diabetes, and/or arteriosclerosis. Optionally, the necrosis may be necrosis of cancerous or noncancerous tissue.

The necrosis may optionally, for example, be coagulative, liquefactive, caseous, fat necrosis, fibrinoid necrosis and/or gangrenous necrosis.

A visual and/or microscopic examination of a subject or tissue sample may optionally be carried out to determine the presence or absence of one or more characteristics of a type of necrosis optionally selected from coagulative, liquefactive, caseous, fat necrosis, fibrinoid necrosis and/or gangrenous necrosis. By visual examination is meant examination without the aid of a microscope, typically with the bare eye.

Coagulative necrosis may arise due to ischemia, i.e., lack of blood flow to the affected tissue. Visually, it may be characterised by firm tissue. Microscopically, it may be characterised by preserved cell outlines, i.e., cells of a ghostly appearance, and redness.

Liquefactive necrosis may arise due to infections, although it may alternatively occur due to a brain infarct. Visually; it may be characterised by liquified tissue and/or pus, which may be creamy yellow. Microscopically, it may be characterised by the presence of neutrophils and cell debris.

Gaseous necrosis may arise due to an infection, such as, tuberculosis, in response to which the body tries to fight the infective microbe with macrophages. Visually, it may be characterised by white, soft, caseous material. Microscopically, it may be characterised by a granuloma, such as, fragmented cells and debris surrounded by a collar of lymphocytes and macrophages.

Fat necrosis may arise due to injury or trauma, e.g., from a seat belt, biopsy, or implant removal. Visually, it may be characterised by saponification, i.e. chalky, white areas from the combination of the newly-formed free fatty acids with calcium. Microscopically, it may be characterised by shadowy outlines of dead fat cells and/or a bluish cast from calcium deposits. Fibrinoid necrosis may arise due to autoimmune disorders such as rheumatoid arthritis or polyarteritis nodosa. Visually, it may be characterised by the presence of an amorphous eosinophilic material reminiscent of fibrin. Microscopically, it may be characterised by thickened and pinkish-red vessel walls, typically called "fibrinoid".

Necrosis may also be referred to as "gangrene", which may be divided into "dry gangrene" and "wet gangrene".

Necrosis treatment may involve surgery, such as, debridement (the surgical removal of the dead and dying tissue) and/or amputation. A balance must be struck between the need remove the necrotic tissue, and the desire to maintain as much of the subjects affected area, such as a limb, digit, or organ, as possible.

The method may optionally involve the analysis of necrosis, e.g. the analysis of tissue to determine whether a particular tissue is necrotic or healthy. Thus, the margin between healthy and necrotic tissue may optionally be analysed. This analysis may be used to assist in deciding which tissue to remove surgically and which tissue may be viable enough to be retained by the subject.

Necrosis can arise through insufficient oxygenation of a tissue. It may therefore be desirable to analyse, e.g., the oxygenation status or ability of a tissue. Thus, optionally, the method may involve the analysis of tissue oxygenation. Optionally, the functional capacity of tissue to process oxygen may be analysed, which may optionally be used to determine the viability of tissue. For example, Oxy haemoglobin (OxyHb) and/or deoxyhaemoglobin (DeoxyHb) may be analysed. DeoxyHb is the form of haemoglobin without oxygen, whereas OxyHb is the form of haemoglobin with oxygen. For example, the relative amount of OxyHb versus DeoxyHb may be analysed.

Mucosal analysis The mucosa lines several passages and cavities of the body, particularly those with openings exposed to the external environment, including the oral-pharyngeal cavity, gastrointestinal (GI) tract, respiratory tract, urogenital tract, and exocrine glands.

Thus, the mucosa may optionally be selected from Bronchial mucosa, Endometrium (mucosa of the uterus), Esophageal mucosa, Gastric mucosa, Intestinal mucosa (gut mucosa), Nasal mucosa, Olfactory mucosa, Oral mucosa, Penile mucosa and/or Vaginal mucosa.

Broadly speaking, the mucosa comprises a mucus layer (the inner mucus layer); an epithelium; a basement membrane, a Lamina propria (LP), which is a layer of connective tissue; and a Muscularis mucosae, which is a thin layer of smooth muscle. Thus, the term "mucosa" is used herein to refer to this entire complex, unless stated otherwise. The term "mucosal membrane" is used to refer to the mucosa without the mucus layer, i.e., the epithelium, basement membrane, LP and Muscularis mucosae. The mucosa may also be covered by a further, outer mucus layer, which is typically more loosely associated therewith. Any reference herein to a "mucosa" may include reference to this further, outer mucus layer. Adjacent to the mucosa is the submucosa.

The submucosa in the GI tract represents a connective tissue layer containing arterioles, venules and lymphatic vessels. It is made up of mostly collagenous and elastic fibres with varying amounts of adipose elements.

The inner mucus layer may be degraded by microbes. For example, mucin monosaccharides may be used by bacteria, e.g., commensal bacteria, as an energy source.

Therefore, continuous renewal of the inner mucus layer is very important.

The epithelium is a single or multiple layer(s) of epithelial cells. The epithelium may comprise, for example, intra-epithelial lymphocytes (IELs), endocrine cells, goblet cells, enterocytes and/or Paneth cells.

The basement membrane may comprise various proteins, particularly structural or adhesive proteins, such as, laminins, collagens, e.g., collagen IV, proteoglycans, and/or calcium binding proteins such as fibulin.

The Lamina propria is connective tissue which may comprise, for example, plasma cells, eosinophils, histiocytes, mast cells and/or lymphocytes. Neutrophils are generally absent in the Lamina propria of healthy humans.

As discussed below, the mucosa may also comprise, for example, antigen presenting cells (APCs) and microfold cells (M-cells). The mucosa may include one or more distinct types of regulatory immune cells, including intestinal intraepithelial lymphocytes (IELs), Foxp3(+) regulatory T cells, regulatory B cells, alternatively activated macrophages, dendrite cells, and/or innate lymphoid cells.

The mucosa typically secretes mucus, which forms a mucus layer between the mucosal epithelium and the lumen. The mucus layer may have a protective function. A major constituent of mucus are mucins, which are produced by specialized mucosal cells called goblet cells. Mucins are glycoproteins characterized mainly by a high level of 0-linked oligosaccharides. The level to which the protein moiety is linked to the carbohydrate moieties, as well as the precise identity of the charbohydrate moieties, may vary significantly.

Mucosa establish a barrier between sometimes hostile external environments and the internal milieu. However, mucosae are also responsible for nutrient absorption and waste secretion, which require a selectively permeable barrier. These functions place the mucosal epithelium at the centre of interactions between the mucosal immune system and luminal contents, including dietary antigens and microbial products. Thus, many physiological and immunological stimuli trigger responses in the mucosa. Dysfunctional responses may contribute to disease.

The mucosal immune system is a localized and specific immune organisation. The mucosal immune system at different organs share similar anatomical organization and features. The GI mucosal immune system is best understood, and is discussed below for illustrative purposes. The GI mucosal immune system is composed of three major compartments: the epithelial layer; the lamina propria (LP); and the mucosal-associated lymphoid tissue (MALT), which, in the GI tract, may be referred to as gut-associated lymphoid tissue, and which comprises Peyer's patches and isolated lymphoid follicles.

Dendritic cells may project dendrites into the epithelium to uptake antigens and migrate to the LP, secondary lymphoid tissue and draining lymph nodes, where they prime naive T cells.

Microfold cells (M-cells), located in the epithelium of Peyer's patches, may pass the antigens to dendritic cells, macrophages and other antigen presenting cells. Naive T cells in secondary lymphoid tissues may become activated after being primed by antigen presenting cell and home to LP (called LPLs) or infiltrate into inflamed epithelium.

The gastrointestinal (GI) tract can be divided into four concentric layers that surround the lumen in the following order: (i) Mucosa; (ii) Submucosa; (iii) Muscular layer; and (iv) Adventitia or serosa.

Thus, the GI mucosa is the innermost layer of the gastrointestinal tract. This layer comes in direct contact with digested food. In the GI mucosa, the epithelium is responsible for most digestive, absorptive and secretory processes, whereas the Muscularis mucosae aids the passing of material and enhances the interaction between the epithelial layer and the contents of the lumen by agitation and peristalsis. GI mucosae are highly specialized in each organ of the GI tract to deal with the different conditions. The most variation may occur in the epithelium.

Different types of mucosa differ from one another and the inventors have shown that the method of the invention may optionally be used, e.g., to distinguish between different types of mucosa, e.g. vaginal, nasal and oral.

Fig. 26 illustrates a variety of microbes that may be present in the human microbiome. As shown in Fig. 26, the human microbiome may include various bacteria, fungi, archaea, viruses, yeasts, protozoa, etc. which may be present, e.g., in the mouth, pharynx, respiratory system, skin, stomach, intestines, and/or urogenital tract, etc. Fig. 27 illustrates various different mucosa or mucosal membranes which are present in the human body.

Mucosal membranes 2700 comprise a layer of epithelial tissue which lines all passages in the human body that are open to the external environments including the nose and parts of the digestive, urogenital and respiratory tracts. Mucosal membranes typically act as a protective barrier to trap pathogens such as bacteria, viruses and fungi. As shown in Fig. 27, mucosal membranes are present in the mouth, pharynx, and respiratory system 2710, as well as in the gastro-intestinal tract 2720 and the urogenital tract 2730, and include the endometrium, intestinal, gastric, oral, vaginal, esophageal, gingival, nasal, buccal and bronchial membranes.

Studies as part of the human microbiome project have revealed that colonization by different microbial species within the mucosa has an immense impact upon human health and disease. As discussed elsewhere herein, many diseases (e.g. cancer, infections, etc.) are associated with the mucosa. As such, the mucosal membrane is an easily accessible and highly clinically relevant sample to analyse, e.g., diagnose diseases, e.g., microbial and/or cancerous associated diseases.

As shown in Fig. 28, a typical mucosal membrane may be present in a lumen 2800 and may include mucus 2810, bacteria 2820, lymphatic vessels 2830, blood vessels 2840, mucosal glands 2850, and submucosa 2860. As illustrated by Fig. 28, the biological tissue of the mucosa itself, e.g. mucus 2810, and/or bacteria 2820 present in or associated with the mucosa represent potential analytes/biomarkers. For example, membrane lipids, and/or inflammatory markers of the mucosa, and/or complex lipids and/or signalling molecules of intact bacteria cells represent potential analytes/biomarkers.

Mucosal analysis Optionally, the method may involve the analysis of a mucosal target, which may be in vivo, or a specimen comprising or consisting of mucosa. Optionally, the method may involve the analysis of a mucosal target to analyse the cellular composition of the mucosa; to analyse a disease; to analyse the response to a drug; to analyse the response to a particular food, diet, and/or a change in diet; to analyse a mucosal microbe; to analyse a microbial interaction with the mucosa, and/or to analyse the mucosal microbiome.

The analysis of the cellular composition of a mucosa, may, e.g., analyse the presence or absence and/or proportion of one or more cell types, which may optionally be selected from any of the cell types listed herein. Optionally, the method may involve the analysis of MALT and/or a Peyer's patch. Optionally, the method may involve the analysis of the phenotype and/or genotype of one or more cell types, which may optionally be selected from any of the cell types listed herein.

Optionally, the method may involve the analysis of a change in the mucosa, which may optionally be a change in, e.g., the cellular composition of the mucosa, the microbial interaction(s) with the mucosa, and/or the mucosal microbiome. By a "change" in the mucosa is meant that the mucosa is different from how it would typically present in a healthy subject; that it is different in one location compared to another location within the same subject; and/or that it is different from how it was when it was analysed at an earlier point in time. A change in the mucosa may optionally, for example, be caused by, or associated with, a disease, the response to a substance, such as a drug, and/or the response to a food, diet, and/or diet change.

A disease may optionally be selected from an autoimmune disorder, an inflammatory disease, tropical sprue, a food intolerance, an infection, a cancer, and/or any of the disorders mentioned herein.

More particularly, the disease may optionally be selected from, for example, asthma, Coeliac disease, gastritis, peptic duodenitis, Gluten-sensitive enteropathy; allergy and/or intolerance to an allergen, e.g. to milk, soy, tree nut(s), egg; wheat, meat, fish, shellfish, peanut, seed, such as sesame, sunflower, and/or poppy seeds, garlic, mustard, coriander, and/or onion; Hashimoto's thyroiditis; Irritable bowel syndrome; Graves's disease; reactive arthritis; psoriasis; multiple sclerosis; Systemic lupus erythematosus (SLE or lupus); ankylosing spondylitis; progressive systemic sclerosis (PSS); glomerulonephritis; autoimmune enteropathy; IgA deficiency; common variable immunodeficiency; Crohn's disease; colitis, such as, lymphocytic colitis; collagenous colitis and/or ulcerative colitis; diffuse lymphocytic gastroenteritis; ulcer; intestinal T-cell lymphoma; infection, e.g., pharyngitis, bronchitis, and/or infection with a microbe selected, for example, from Giardia, Cryptosporidium, Helicobacter and/or any of the other microbes mentioned herein; and/or cancer, details of which are discussed elsewhere herein.

The method may, e.g., optionally involve the analysis of the interaction of the mucosa with microbes, or a change in the mucosa caused by, or associated with, such an interaction. Optionally, the interaction may, e.g., be the translocation of microbes into the mucosa, e.g., the translocation of commensal bacteria. The method may, e.g., optionally involve the analysis of the mucosal microbiome, or a change in the mucosa caused by, or associated with, the mucosal microbiome. The method may, e.g., optionally involve the analysis of an infection, or a change in the mucosa caused by, or associated with, an infection. The analysis of microbes, a microbial interaction, infections and/or the microbiome are also discussed elsewhere herein.

As mentioned above, IELs are a normal constituent of the small intestinal mucosa. They play a significant role in immune surveillance and activation. In healthy humans, the vast majority of IELs are of T-cell type and express an wp T-cell receptor on their surface. It is generally accepted that healthy humans have no more than about 20 lymphocytes per 100 epithelial cells in the intestinal mucosa.

An increased number of lymphocytes in a mucosal specimen may optionally be indicative of a change, such as, a disease, the response to a drug, and/or a microbial change. The term "elevated" or "increased" levels of IELs is therefore used to refer to more than 20 IELs per 100 epithelial cells in the intestinal mucosa, optionally at least 22, 24, 25, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 65, 70, 75, 80 20 IELs per 100 epithelial cells in the intestinal mucosa.

The gamma-delta receptor of T lymphocytes is not expressed by more than 2-3% of T lymphocytes in normal conditions. An increase in the percentage of T lymphocytes expressing this receptor may therefore be indicative of a change, such as, a disease, the response to a drug, and/or a microbial change. The method may therefore involve determining the presence or percentage of T lymphocyte gamma-delta receptor expression. For example, in coeliac disease 20-30% of mucosal T lymphocytes may express this receptor.

Thus, the method may optionally involve the analysis of lymphocytes in a target, which may optionally be T lymphocytes, e.g. gamma-delta receptor-positive T lymphocytes. Optionally, a target may be analysed for an increase or decrease in the number of lymphocytes. Optionally, the phenotype and/or genotype of the lymphocytes may be analysed.

Polymorphonuclear leukocytes (PMN), also called neutrophils, are the most abundant leukocyte population in the blood, comprising 50-60% of the circulating leukocytes (25 x 109 cells). PMN are critical components of the innate immune response that are essential in protecting the host, e.g., from microbial pathogens, while also minimizing deleterious effects mediated by dying or injured cells.

PMN may perform a variety of antimicrobial functions such as degranulation and phagocytosis. They are uniquely capable of forming large amounts of reactive oxygen species and other toxic molecules that may weaken and/or destroy pathogens. Upon PMN contact with invading microbes, reactive oxygen species may be generated in an oxidative burst by an nicotinamide adenine dinucleotide phosphate (NADPH) oxidase PMN may also possess different pools of intracellular granules that contain antimicrobial peptides, such as, a-defensins and/or cathelicidins; myeloperoxidase; hydrolytic enzymes, such as, lysozyme, sialidase, and/or collagenase; proteases, such as, cathepsin G; azurocidin, and/or elastase; cationic phospholipase; and/or metal chelators such as lactoferrin. Such granules may be released upon contact with microbes.

PMN may also be capable of imprinting the tissue with neutrophil extracellular traps (NETs). NETs may be composed of nuclear contents (DNA and chromatin) mixed with toxic molecules from intracellular granules and the cytosol. Invading microorganisms may be sequestered in these NETs and effectively destroyed.

During intestinal inflammation, resident monocytes contribute to the recruitment of neutrophils through production of macrophage-derived chemokines. Neutrophils present in the blood sense the chemoattractant gradient and traverse the vascular endothelium to reach the intestinal lamina propria. In this manner, neutrophils are recruited to sites of infection or inflammatory stimuli within minutes. The response typically peaks by 24-48 hours. Under certain physiological or pathological conditions, neutrophils may cross the epithelium into the intestinal lumen.

At inflammatory sites, neutrophils may selectively release monocyte chemoattractants, such as CAP18, cathepsin G, and/or azurocidin. Thus, shortly after arrival of PMN to the mucosa, macrophages are recruited for a second-wave inflammatory response that ensues for the next several days.

Thus, the method may optionally involve the analysis of neutrophils in a target. Optionally, the presence of reactive oxygen species and/or neutrophils generating reactive oxygen species in a target may be analysed. Optionally, the presence of NETs and/or neutrophils generating NETs in a target may be analysed. Optionally, the presence of monocyte chemoattractants and/or neutrophils generating monocyte chemoattractants in a target may be analysed.

As described in the Examples, a total of n=85 mucosal membrane models were collected from three cohorts (urogenital tract, nasal and oral cavity). The mucosal membrane samples were subjected to desorption electrospray ionisation ("DESI") spectrometric analysis and the resulting spectrometric data was subjected to multivariate statistical analysis. Multivariate statistical analysis was able to separate different mucosa classes and biomarker changes that can be associated with a diverse microbiome within the mucosa.

According to various embodiments, microbial, e.g., bacterial, and/or animal, e.g., human mucosal membrane analytes may be characterised, e.g. using ambient mass and/or ion mobility spectrometry based techniques such as the desorption electrospray ionisation ("DESI") technique and the rapid evaporative ionisation mass spectrometry ("REIMS") technique.

As illustrated by Fig. 29, these analytes (e.g., membrane lipids and inflammatory markers of the mucosa, and complex lipids and signalling molecules of intact bacteria cells) can be useful in identifying a number of clinical disorders.

Accordingly, various embodiments are directed to the development of a real time point of care ("POC") diagnostic method to investigate various clinical disorders. In particular, various embodiments are directed to mass spectrometry ("MS") and/or ion mobility spectrometry based real-time point of care ("POC") techniques.

For example, infections such as pharyngitis, bronchitis, and/or infections with any of the microbes mentioned herein can be identified e.g. by analysing, e.g., identifying microbes.

Changes in the microbiome can also be analysed, e.g., detected, e.g., by identifying microbes, and by way of example, determining a change in the microbiome of a pregnant patient can be used to identify those patients who are at an increased risk of having a pre-term or premature delivery during pregnancy.

Furthermore, the various analytes taken from mucosal membranes, e.g. biomarker profiling, can be used to identify various immunological disorders (e.g., asthma, allergies) as well as to identify cancer and/or pre-cancerous states.

As further illustrated by Fig. 30, metabolomic profiling of analytes from various mucosal membranes using swabs can be useful in identifying a number of clinical disorders. For example, allergies may be identified, e.g., by identifying inflammatory mediators (eicosanoids) such as prostaglandins (PGD2), leukotriends, histamine, etc. Inflammation (such as pharyngitis, angina, etc.) may be identified, e.g., by identifying microbial, e.g., bacterial secondary metabolites, lipids, etc. from bacteria such as streptococcus sp., staphylococcus sp., haemophilus sp., etc. Pre-term delivery may also be identified, e.g. by identifying healthy (e.g. comprising a stable lactobacilli environment including e.g., L. crispatus dominant, L. iners dominant, and/or L. gasseri mix, etc.) or unhealthy mucosa (e.g. comprising an overgrowth of pathogens including, e.g., Escherchia colt, Atopobium vaginae, Peptostreptococcus, and/or Bacteroides sp., etc.).

According to various embodiments, mucosal diagnostics enable non-invasive direct sampling of the mucosa from patients at a clinical point of care.

According to various embodiments, analytes may be obtained from mucosal membranes using, e.g., a standard medical swab.

For clinical analysis, the swabs may be wiped over or into an infected area, e.g. to sample microbe rich body fluid, such as, sanies, and/or the mucosa. The swab may then be placed into a sterile tube containing a buffer solution for storage before the tube is sent to a laboratory for analysis. A laboratory receiving the tube may wipe the smear content across a culture medium such as an agar plate. The culture medium may then be incubated to allow organisms present to grow. Microbial identification may then be performed under a microscope. Any organisms present in the sample may also be identified, e.g., by sequence analysis, e.g.,16S gene-sequencing of bacteria, and/or by using matrix-assisted laser desorption ionisation ("MALDI") mass and/or ion mobility spectrometry and then comparing the mass and/or ion mobility spectra with a commercially available database.

Fig. 31 illustrates a microbe identification workflow and shows sampling 311 an analyte using a swab and then transporting 312 the swab to a specialist laboratory for microbe culturing 313 and further analysis. As shown in Fig. 31, such culture based analysis may comprise imaging using a microscope 314 and/or Matrix Assisted Laser Desorption Ionisation ("MALDI") Mass Spectrometry ("MS") 315 followed by statistical analysis 316, etc. 16s rRNA sequencing 317 is a culture independent analysis method.

Although easy to handle, the current analysis of medical swabs for diagnostic purposes is culture-dependent and involves a relatively time consuming and relatively costly workflow.

Diagnosis of pathogen-associated diseases and appropriate treatment is therefore associated with considerable delay. Furthermore, around 95% of bacteria cannot be cultured for analysis.

Various embodiments which are described in more detail below provide a fast and direct way to investigate clinical samples from mucosal membranes, e.g. by identifying microbes and/or biomarkers characteristic of specific clinical disorders in mucosal samples, thereby permitting faster diagnoses and treatment of patients.

Various embodiments are directed to real time rapid and direct analysis of analytes present, e.g., on a swab, using ambient mass and/or ion mobility spectrometry. Ambient ionisation mass and/or ion mobility spectrometry based techniques may be employed for direct analysis of the sample surface. A sample may be analysed in its native state with minimal or no prior sample preparation.

In particular, Desorption Electrospray Ionisation ("DESI") has been found to be a particularly useful and convenient method for the real time rapid and direct analysis of analytes, e.g. those present on a swab. Desorption electrospray ionisation ("DESI") allows direct and fast analysis of surfaces without the need for prior sample preparation. DESI is described elsewhere herein.

The desorption electrospray ionisation ("DESI") technique allows for ambient ionisation of a trace sample at atmospheric pressure with little sample preparation. The desorption electrospray ionisation ("DESI') technique allows, for example, direct analysis of biological compounds such as lipids, metabolites and peptides in their native state without requiring any advance sample preparation.

Some embodiments described herein relate to directly analysing medical swabs using desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry. According to various embodiments chemical signature identification of specific microbes, e.g., bacteria and/or biomarkers on the surface of the swabs is possible within a relatively short period of time.

Various specific embodiments relate to the rapid diagnosis of infections and/or dysbiosis, e.g., associated with preterm (premature) delivery (and these results may optionally be compared with standard microbial testing).

Further embodiments relate to a real-time rapid medical swab analysis using desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry to reveal pathogenic and/or inflammatory metabolomic markers.

Various embodiments relate to the development of a non-invasive point of care diagnostic technique, directed toward detection of diseases with a particular emphasis on the detection of infections, dysbiosis, cancer and/or inflammatory diseases, and/or any of the other diseases mentioned elsewhere herein.

Clinical studies have shown that vaginal microbial, e.g., bacterial diversity is associated with specific vaginal mucosal metabolites. For example, during healthy pregnancy the vaginal mucosa is colonized mainly by the Lactobacillus species. However, importantly, a shift towards vaginal dysbiosis during pregnancy may be a causal trigger for preterm birth.

Using the ambient ionisation mass and/or ion mobility spectrometry based technique disclosed herein allows females, e.g., women, who have had a spontaneous preterm birth to be evaluated and compared to controls in order to identify biomarkers that can be used to predict preterm delivery. Moreover, the vaginal mucosa of pregnant females may be analysed using the ambient ionisation mass and/or ion mobility spectrometry based technique disclosed herein to analyse, e.g., diagnose or predict the risk of, a (spontaneous) preterm birth.

Spectrometric profiling of vaginal mucosa can enable an early identification of females, e.g., women who are at risk of infection during pregnancy based upon microbial, e.g., bacterial diversity in the vaginal mucosa. Furthermore, this enables targeted treatment response strategies.

Various embodiments are contemplated and include: (i) identification of vaginal mucosa metabolite biomarkers that are related to specific microbial, e.g., bacterial communities, optionally as determined using sequencing microbiome analysis; (ii) profiling of vaginal mucosal membrane during healthy pregnancy wherein microbe, e.g., bacteria-specific metabolites and signatures that are excreted during healthy pregnancy may be characterised in detail; and (iii) identification of diagnostic and prognostic metabolic signatures from vaginal mucosa membranes with poor pregnancy outcomes (e.g. preterm delivery).

It will be appreciated that various embodiments provide a new desorption electrospray ionisation ('DESI") mass and/or ion mobility spectrometry setup for non-invasive and fast analysis of the mucosal metabolome profile from the surface of medical swabs. This arrangement has been successfully shown to be capable of differentiating animal, e.g., human mucosal membrane models and to enable microorganism identification.

Since desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry allows a less destructive analysis method which preserves the main content of the sample surface material, according to various embodiments the medical swab can optionally be sent directly after desorption electrospray ionisation ("DESI") analysis to a microbiological lab for further cultivation and microbe identification/confirmation.

Various embodiments provide a new point of care mucosal screening diagnostic method which uses standard cotton medical swabs as both the sampling probe for mucosal membrane uptake and ionisation probe for desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry analysis. After data acquisition the obtained spectra may be compared with spectra collected in a database to provide a rapid diagnosis to the patient, e.g., within several seconds.

Various embodiments relate to the application of the desorption electrospray ionisation ("DESI") technique for direct metabolomic profiling of specific mucus models (nasal, vaginal, pharyngeal, bronchial, oesophageal) from the surface of standard medical swabs. Various embodiments relate to a rapid point-of-care diagnostic method for diseases, optionally selected from any of the diseases mentioned herein, e.g., inflammatory and pathogen-related diseases such as in immunological disorders, dysbiosis in the microflora (which may, e.g. be indicative of the risk of pre-term delivery during pregnancy), microbial, e.g., bacterial infections, or the detection of cancer or pre-cancerous states. The metabolomic profiling of animal, e.g., human mucosal membrane followed by detailed statistical analysis permits the identification of disease-specific metabolic profiles and/or taxon specific microbial, e.g., bacterial markers in a rapid, robust manner conducive to a point-of-care diagnostic method.

As shown in Fig. 39, according to various embodiments, desorption electrospray ionisation ("DESI") spectrometric analysis 390 of a sample sampled 391 onto a swab may be subjected to statistical analysis 392 in order to provide a diagnosis 393 (or prognosis).

The sample may be additionally or alternatively be analysed by rapid evaporative ionisation mass spectrometry ("REIMS") 394, or any other ambient ionisation mass and/or ion mobility spectrometry method.

Embodiments are contemplated wherein multiple different analysis techniques may be applied to the same swab (or another swab) so as to additionally perform analyses that rely on culturing 165, such as DNA extraction and PCR analysis, e.g., to produce complementary 165 rRNA microbiome data.

As shown in Fig. 39, any one or more or all of the additional analyses may be used to validate the desorption electrospray ionisation ("DESI") based diagnosis 393.

Various embodiments described herein also relate to methods of rapid evaporative ionisation mass spectrometry ("REIMS") analysis of a swab, wherein a sample on a swab is subjected to rapid evaporative ionisation mass spectrometry ("REIMS") analysis. This approach, however, is destructive for the swab, and in the bipolar mode the contact closure of the electrodes is restricted.

When a swab is analysed by rapid evaporative ionisation mass spectrometry, then the swab may be dipped, soaked or otherwise immersed in a fluid (such as water) prior to be being subjected to rapid evaporative ionisation mass spectrometry ("REIMS") analysis.

As discussed above, a particular benefit of using desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry to analyse a sample provided on a medical swab is that multiple different analyses of the same sample; i.e. of the same swab, may be performed.

Performing multiple different analyses of or on the same sample enables multiple different sets of information about the same sample to be obtained in a particularly convenient and efficient manner. This is in particular possible because desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry is a relatively non-destructive analysis technique and also because various commercial analysis techniques, such as culturing techniques and nucleic acid sequencing techniques, e.g., 16S rRNA sequencing techniques, are optimised to use samples which are provided on medical swabs.

Accordingly, following a single sample acquisition onto a swab, the sample on the swab may be analysed multiple times using multiple different analysis techniques, where at least one of the techniques (e.g. the first technique used) comprises desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry.

Medical swabs were analysed by desorption electrospray ionisation ("DESI") mass and/or ion mobility spectrometry as shown in Example 16.

Healthy submucosa and GI polyps were analysed via a method of the invention, as shown in Example 19 and Figures 54-56. Clear differences were observed between the rapid evaporative ionisation mass spectrometry fingerprints of the submucosa and mucosal layer. This may optionally be exploited as a potential safety function for interventional surgery, e.g., endoscopy.

Colonoscopic procedures involving electrocautery are associated with a 9x increase in perforation risk compared to a purely diagnostic procedure. It has also been reported that endomucosal resection ("EMR") of ulcerated lesions are at higher risk of perforation. Optionally the method of the invention may use REIMS in GI surgery to analyse whether there is a breach of the submucosal layer during surgery, such as polypectomy or endomucosal resection. Thus, the method of surgery may involve the use of REIMS technology as described herein to analyse whether there is a breach of the submucosal layer during surgery, such as polypectomy or endomucosal resection.

Thus, the method advantageously helps in decreasing perforation rates and the significant morbidity associated with this complication.

Real time and/or delayed information may be provided to a user of an electrosurgical tool that may comprise spectrometric information and/or tissue classification information. A feedback device and/or an alarm and/or an alert may also may be provided to provide a user of the electrosurgical tool with feedback and/or an alarm and/or an alert that analyte from an undesired target region or area is being analysed by the analyser or that the electrosurgical tool is operating in and/or is located in an undesired target region or area.

The method may optionally be used to analyse cancer in the mucosa, as illustrated in

Example 20.

Analysis of microbes and/or the microbiome A "microbe", also known as a micro-organism, is an organism which is too small to be visible to the naked eye, i.e. is microscopic. A microbe may be selected from bacteria, fungi, archaea, algae, protozoa and viruses. Although the terms bacteria, fungi, archaea, algae, protozoa and viruses technically denote the plural form, it is common practice to use them also to denote the singular form. Consequently, the terms "bacteria" and "bacterium" are used interchangeably herein; the terms "fungi" and "fungus" are used interchangeably herein; the terms "archaea" and "archaeum" are used interchangeably herein; the terms "protozoa" and "protozoum" are used interchangeably herein; and the terms "viruses" and "virus" are used interchangeably herein.

In the case of a microbe, analysis may optionally be on any taxonomic level, for example, at the Kingdom, Phylum or Division, Class, Order, Family, Genus, Species and/or Strain level.

"Taxonomy" is the classification of organisms, and each level of classification may be referred to as a "taxon" (plural: taxa). Organisms may be classified into the following taxa in increasing order of specificity: Kingdom, Phylum or Division, Class, Order, Family, Genus, Species and Strain. Further subdivisions of each taxon may exist. It must be appreciated that within the vast scientific community there are some discrepancies within some taxonomic classifications. There may also be a lack of consensus with regard to the nomenclature of certain microbes, resulting in a particular microbe having more than one name or in two different microbes having the same name.

As a shorthand, the term "type" of microbe is used to refer to a microbe that differs from another microbe at any taxonomic level.

In some embodiments, the microbe may be selected from bacteria, fungi, archaea, algae and protozoa. In some embodiments, it may be selected from bacteria and fungi. In some embodiments. it may be selected from bacteria.

The microbe may be single-cellular or multi-cellular. If the microbe is a fungus, it may optionally be filamentous or single-cellular, e.g., a yeast.

A fungus may optionally be yeast. It may optionally be selected from the genus Aspergillus, Arthroascus, Brettanomyces Candida, Cryptococcus, Debaryomyces, Geotrichum, Pichia, Rhodotorula, Saccharomyces, Trichosporon, and Zygotorulaspora.

It may optionally be selected from the species Arthroascus schoenii, Brettanomyces bruxellensis, Candida albicans, C.asca/aphidarum, C.amphixiae, C.antarctica, C.argentea, C.atlantica, C.atmosphaerica, C.blattae, C.bromeliacearum, C.carpophila, C.carvajalis, C.cerambycidarum, C.chautiodes, C.cotydali, C.dosseyi, adubliniensis, C.ergatensis, C.fructus, C.glabrata, C.fermentati, C.guilliermondii, Chaemulonii, C.insectamens. C.insectorum, C.intermedia, C.jeffresii, C.kefyr, C.keroseneae, C.krusei, C.lusitaniae, C.tyxosophila, C.maltosa, C.marina, C.membranifaciens, C.milleri, C. magi', C.oleophila, C.oregonensis, C.parapsilosis, C.quercitrusa, C.rugosa, C.sake, C.shehatea, C.temnochilae, C.tenuis. C.theae, C.tolerans, C.tropicalis, C.tsuchiyae, C.sinolaborantium, C.sojae, C.subhashii, C. viswanathii, C.utilis, C.ubatubensis, C.zernplinina, Cryptococcus neoformans, Cryptococcus uniguttulatus, Debaryomyces carsonti, Geotrichum capitatum,Trichosporon asahri. Trichosporon mucoides, Trichosporon inkin, Saccharomyces cerevisiae, Pichia acaciae, Pichia anomala, Pichia capsulata, Pichia farinesa, Pichia guilliermondir, Pichia spartinae, Pichia ohmeri, Rhodotorula glutinous, Rhodotorula mucilaginosa, Saccharomyces boulardii, Saccharomyces cerevisiae, and/or Zygotorulaspora t7orentinus.

The protozoa may be selected from the group of amoebae, flagellates, ciliates or sporozoa. It may be selected from the genus Acanthamoeba, Babesia, Balantidium, Ciyptosporidium, Dientamoeba, Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium Paramecium, Trichomonas, Trypanosome, Typanosoma, Toxoplasma The protozoa may be of the species Balantidium colt, Entamoeba histolytica, Giardia lamblia (also known as Giardia intestinalis, or Giardia duodenalis), Leishmania donovani, L. tropica, L. brasiliensis, Plasmodium falciparum, P. vivax, P. ovale, P. malariae, P. knowlesi, P. reichenowi, P. gaboni, P. mexicanum, P. floridense Trypanosoma brucei, Typanosoma evansi, Trypanosoma rhodesiense, Trypanosoma cruzi, Toxoplasma The bacteria may optionally be selected from the phylum Aquficae, Thermotogae, Thermodesulfobacteria, Deinococcus-Thermus, Chrysiogenetes, Chloroflexi, Thermomicrobia, Nitrospira, Deferribacteres, Cyanobacteria, Chlorobi, Proteobacteria, Firmicutes, Actinobacteria, Planctomycetes, Chlamydiae, Spirochaetes, Fibrobacteres, Acidobacteria, Bacteroidetes, Fusobacteria, Verrucomicrobia, Dictyoglomi, Gemmatomonadetes, and Lentisphaerae.

The bacteria may optionally be selected from the class Actinobacteria, Alphaproteobacteria, Bacilli, Betaproteobacteria, Clostridia, Deltaproteobacteria, Epsilonproteobacteria, Flavobacteriaceae, Fusobacteria, Gammaproteobacteria, Mikeiasis, Mollicutes, or Negativicutes.

The bacteria may optionally be of the Order Aeromonadales, Actinomycetales, Bacillales, Bacteroidales, Bifidobacteriales, Burkholderiales, Campylobacterales, Caulobacterales, Cardiobacteriales, Clostridiales, Enterobacteriales, Flavobacteriales, Fusobacteriales, Lactobacillales, Micrococcales, Neisseriales, Pasteurellales, Pseudomonadales, Rhizobiales, Rhodospirillales, Selenomonadales, Vibrionales, Xanthomonadales.

The bacteria may optionally be selected from the Family Acetobacteraceae, Alcaligenaceae, Bacillaceae, Bacteroidaceae, Burkholderiaceae, Caulobacteraceae, Comamonadaceae, Enterobacteriaceae, Flavobacteriaceae, Fusobacteriaceae Nocardiaceae, Prevotellaceae, Porphyromonadaceae, Pseudomonadaceae. Rikenellaceae, Rhizobiaceae, Sutterellaceae The bacteria may optionally be of a genus selected from, e.g., Abiotrophia, Achromobacter, Acidovorax, Acinetobacter, Actinobacillus, Actinomadura, Actinomyces, Aerococcus, Aeromonas, Anaerococcus, Anaplasma, Bacillus, Bacteroides, Bartonella, Bifidobacterium, Bordetella, Borrelia, Brevundimonas, Brucella, Burkholderia Campylobacter, Capnocytophaga, Chiamydia, Citrobacter: Chlamydophila, Chlyseobacterium, Clostridium.

Comamonas, Corynebacterium, Coxiella, Cupriavidus, Delftia, Dermabacter Ehrlichia, Eikenella, Enterobacter, Enterococcus, Escherichia, Erysipelothrix, Facklamia, Finegoldia, FranciseIla, Fusobacterium, Gemella, Gordonia, Haemophilus, Helicobacter, Klebsiella, Lactobacillus, Legionella, Leptospira, Listeria, Micrococcus, Moraxella, Morganella, Mycobacterium, Mycoplasma, Neisseria, Nocardia, Orientia, Pandoraea, Pasteurella, Peptoniphilus, Peptostreptococcus, Plesiomonas, Porphyromonas, Pseudomonas, Prevotella, Proteus, Propionibactenum, Rhodococcus, Ralstonia, Raoultella, Rickettsia, Rothia.

Salmonella, Serratia, Shigella, Staphylococcus, Stenotrophomonas, Streptococcus, Tanner°Ila, Treponema, Ureaplasma, Vibrio or Yersinia.

The bacteria may optionally be of a species selected from, e.g., Abiotrophia defective, Achromobacter xylosoxidans, Acidovorax avenae, Acidovorax citruffl, Akkermansia muciniphila, Bacillus anthracis, B. cereus, B. subtilis, B. licheniformis. Bacteroides Bartonella henselae, Bartonella quintana, Bordetella pertussis, Borrelia burgdorferi, Borrelia garinii, Borrelia afzelii, Borrelia recurrent's, Brucella abortus, Brucella canis, Brucella melitensis, Brucella suis, Burkholderia cepacia, Burkholderia genomovars, Campylobacterjejuni, Chlamydia pneumoniae, Chlamydia trachomatis, Chlamydophila psittaci, Citrobacter koseri, Clostridium botulinum Clostridium difficile, C. perfringens, C. tetani. Corynebacterium diphtheriae. C. striatum, C. minutissimum, C. imitans, C. amycolatum, Delftia acidovorans, Enterobacter aerogenes, E. cloacae Enterococcus faecalis, Enterococcus faecium, Escherichia colt, Francisella tularensis, Fusobacterium nucleatum, Haemophilus /nfluenzae, Helicobacter pylori, Klebsiella oxytoca, K. pneumonia, Legionella pneumophila, Leptospira interrogans, Leptospira santarosar Leptospira weilii, Leptospira noguchii, Listeria ivanovii, Listeria monocytogenes, Micrococcus luteus, Morganella morganii, Moraxella catarrhal's, Mycobacterium avium, M. fortuitum, M. leprae, M. peregrium, M. tuberculosis, M. ulcerans, Mycoplasma pneumoniae, Neisseria gonorrhoeae, N. lactamica, N. meningitidis, Nocardia asteroids, Proteus mirabiles, Pseudomonas aeruginosa, Rhodococcus equi, Rhodococcus pyridinivorans,Rickettsia fickettsii, Salmonella typhi, Salmonella typhimurium, Serratia marcescens, Shigella sonnei, Staphylococcus aureus, S. capitis, S. epidermidis, S. haemolyticus, S. hominis. S. saprophyticus, Stenotrophomonas maltophilia. Streptococcus agalactiae, S. pyogenes, S. pneumonia, Treponema pallidum. Ureaplasma urealyficum, Vibrio cho/erae, Yersinia pestis, Yersinia enterocolitica and Yersinia pseudotuberculosis.

The virus may optionally be a DNA virus, and RNA virus or a retrovirus. It may optionally be a single stranded (ss) or a double stranded (ds) virus. More particularly, it may optionally be a ssDNA, dsDNA, dsRNA, ssRNA(positive strand), ssRNA (negative strand), ssRNA (reverse transcribed) or dsDNA (reverse transcribed) virus.

It may optionally be selected from one or more of the Herpesviridae, optionally selected from Simplexvirus, Varicellovirus, Cytomegalovirus, Roseolovirus, Lymphocryptovirus, and/or Rhadinovirus; the Adenoviridae, optionally selected from Adenovirus and/or Mastadenovirus; Papillomaviridae, optionally selected from Alphapapillomavirus, Betapapillomavirus, Gammapapilloma-virus, Mupapillomavirus, and/or Nupapillomavirus; Polyomaviridae, optionally selected from Polyomavirus; Poxviridae, optionally selected from Molluscipoxvirus, Orthopoxvirus and/or Parapoxvirus; Anelloviridae, optionally selected from Alphatorquevirus, Betatorquevirus, and/or Gammatorquevirus; Mycodnaviridae, optionally selected from Gemycircular-viruses; Parvoviridae, optionally selected from Erythrovirus, Dependovirus, and/or Bocavirus; Reoviridae, optionally selected from Coltivirus, Rotavirus, and/or Seadornavirus; Coronaviridae, optionally selected from Alphacoronavirus, Betacoronavirus, and/or Torovirus; Astroviridae, optionally selected from Mamastrovirus; Caliciviridae, optionally selected from Norovirus, and/or Sapovirus; Flaviviridae, optionally selected from Flavivirus, Hepacivirus, and/or Pegivirus; Picornaviridae, optionally selected from Cardiovirus, Cosavirus, Enterovirus, Hepatovirus, Kobuvirus, Parechovirus, Rosavirus, and/or Salivirus; Togaviridae, optionally selected from Alphavirus and/or Rubivirus; Rhabdoviridae, optionally selected from Lyssavirus, and/or Vesiculovirus; Filoviridae optionally selected from Ebolavirus, and/or Marburgvirus; Paramyxoviridae, optionally selected from Henipavirus, Heffalumpvirus, Morbilivirus, Respirovirus, Rubulavirus, Metapneumovirus, and/or Pneumovirus; Arenaviridae, optionally selected from Arenavirus; Bunyaviridae, optionally selected from Hantavirus, Nairovirus, Orthobunyavirus, and/or Phlebovirus; Orthomyxoviridae, optionally selected from Influenzavirus A, Influenzavirus B, Influenzavirus C and/or Thogotovirus; Retroviridae, optionally selected from Gammaretrovirus, Deltaretrovirus, Lentivirus, Spumavirus; Epadnaviridae, optionally selected from Orthohepadnavirus; Hepevirus; and/or Deltavirus.

The microbes may optionally be pathogenic, or non-pathogenic. A pathogenic microbe, which may also be called a "pathogen", may be defined as a microbe that is able to cause disease in a host, such as a plant or animal. A pathogen may optionally be an obligate pathogen or an opportunistic pathogen.

The ability of a microbe to cause disease depends both on its intrinsic virulence factors and on the ability of the host to fight off the microbe. The distinction between non-pathogens and opportunistic pathogens is therefore not clear-cut, because, for example, immunocompromised hosts will be susceptible to infection by microbes that may be unable to infect a host with a healthy immune system.

For example, Neisseria gonorrhoeae is an obligate pathogen, Pseudomonas aeruginosa and Candida albicans are typically referred to as opportunistic pathogens, and Lactobacillus acidophilus and Bifidobacterium bifidum are typically considered to be non-pathogens, and may be referred to as "commensal".

Drugs, such as, an antimicrobial and/or an anti-inflammatory drug, may also create an environment in which a microbe will flourish as an opportunistic pathogen. Thus, the use of drugs may alter a microbiome. The method may therefore optionally involve analysing the microbiome, e.g., the mucosal microbiome, to analyse the response to a drug.

Pathogenic microbes may optionally be characterised by the expression of one or more virulence factors, i.e. factors that allow or facilitate infection of a host. Virulence factors may optionally be selected from factors that mediate cell adherence, cell growth, the ability to bypass or overcome host defence mechanisms, and/or the production of toxins. Toxins may be selected from exotoxins and endotoxins. The method may optionally involve analysing one or more virulence factors.

Commensal microbes are those which are part of the natural flora of a human or animal and which, in a balanced state, do not cause disease.

The community of microbes in a particular environment may be referred to as a "microbiome". Thus, the microbiome comprises the community of microorganisms that inhabit human or non-human animal bodies, e.g., human bodies. Humans and non-human animals have co-evolved with microbes as a symbiotic system. Complex reactions of microbe communities influence health and disease.

A microbiome may be a complex mixture of a vast number and vast variety of different microbes. The GI microbiome is estimated to comprise over 100 trillion microbes that represent at least several hundreds or even over a thousand different species. The healthy human gut microbiota is dominated by the Bacteroidetes and the Firmicutes, whereas, for example, Proteobacteria, Verrucomicrobia, Actinobactetia, Fusobacteria, and Cyanobacteria are typically present in minor proportions.

The microbiome may vary from one environment to another within the same human or animal, so a person's gastrointestinal (GI) microbiome may be different from that person's nasal microbiome. The GI microbiome may further be divided into the different GI regions, such as, stomach, duodenum, jejunum, ileum, and/or colon. The lumen microbiome may also differ from the mucosal microbiome. Each microbiome may also vary from one individual to another. The disturbance of the normal microbiome may be referred to as "dysbiosis". Dysbiosis may cause, or be associated with, a disease, such as, any of the diseases mentioned herein. The method may optionally involve the analysis of a microbiome to analyse dysbiosis. The GI microbiome may also be referred to as the "gut flora".

The microbiome may change during pregnancy, so an analysis of the female (human or animal) microbiome may allow an analysis of pregnancy. Dysbiosis in pregnancy is associated with complications, such as, an increased risk of premature birth.

Dysbiosis may involve the presence of one or more types of microbes that are normally, or were previously, absent from a particular microbiome. However, more commonly, dysbiosis may involve a relative increase in the proportion of one or more particular microbes, and/or a relative decrease in the proportion of one or more particular microbes.

As mentioned above, the mucosa comprises layers of mucus. Microbes, such as bacteria, may adhere to and/or partially or fully infiltrate the mucus layer. The microbial adherence and/or proliferation may be influenced by carbohydrate modifications present on mucins; by antimicrobial agents, such as, host-derived antimicrobial peptides; by drugs; and/or by toxins, such as, toxins produced by (pathogenic) microbes.

The mucosal (epithelial) surface beneath the mucus layer is free of microbes in at least about 80% of healthy humans. The thickness of the mucus layer and its spread may vary, for example, they may decrease with increasing severity of inflammation. Under certain conditions, for example, in a disease, microbes may infiltrate and/or adhere to the mucus layer, the epithelium and/or the LP. For example, bacteria may typically be found within the mucus of biopsy specimens from subjects with ulcerative colitis, SLC, and/or acute appendicitis. The concentration of microbes within the mucus layer may inversely correlate to the numbers of leucocytes.

The term "mucosal microbiome" is used herein to denote the microbiome which is associated with the mucosa, including the microbiome that has infiltrated the mucosa and the microbiome that is associated with (for example, through adhesion or partial or full infiltration) with mucus layer.

The method may optionally involve the analysis of a target to detect, identify and/or characterise a microbe. For example, the method may be used to analyse whether a target is sterile or non-sterile; whether any microbes present are pathogenic or commensal; whether any microbes present are the cause of an infection; and/or whether any microbes present in a target specimen were present in the subject from which the specimen was provided, or whether the microbes represent contamination of the specimen. For example, when taking a blood sample, there is typically a risk of the blood becoming contaminated with microbes that were present at or around the site at which the needle is inserted, which can lead to the presence, and hence detection, of microbes in a blood sample that would otherwise not have contained said microbes. Thus, the method may optionally be used to determine the significance of any microbes present in the target; and or to determine whether the subject from which the specimen was derived should receive an antimicrobial treatment.

The method may optionally involve the analysis of an infection, .e.g., the diagnosis of an infection, analysis of the genotype or phenotype of the infection-causing microbe, monitoring of progression of infection, and/or monitoring of treatment response to infection.

The method may optionally involve the analysis of vaccination. This may, .e.g., involve analysing a target prior to and after vaccination. Optionally, the subject may be challenged after vaccination with the microbe against which the vaccination is aimed, and a suitable target may then be analysed to determine whether, or at what level, the microbe is present. The presence or level of the microbe may be indicative of the success of vaccination, e.g., the absence or presence at low levels of the microbe may be indicative of successful vaccination, whereas the presence, or presence at high levels of the microbe may be indicative of the vaccine being deficient or ineffective.

Faecal or body fluid specimen analysis The analysis of a faecal or body fluid specimen may provide information about a disease and/or microbiome, optionally a mucosal microbiome and/or the microbiome of the GI lumen. Thus, optionally, the method may involve the analysis of a faecal and/or body fluid specimen. For example, a faecal and/or body fluid specimen may be analysed for the presence of a cell, a compound, and/or a microbe.

The method may optionally allow an analysis of metabolic differences between various conditions, which may optionally be selected from any of the conditions listed elsewhere herein, e.g., Irritable Bowel Syndrome, Colorectal cancer and/or Inflammatory Bowel Disease. By identifying taxonomic specific biomarkers the method may optionally allow the analysis, e.g., diagnosis, of microbial infections and/or mixed microbial communities.

The cell may, e.g., be a mammalian cell, a white blood cell, a red blood cell, a foetal cell, and/or a cancer cell.

The compound may, e.g., comprise or consist of a biomolecule, an organic compound, and/or an inorganic compound. Optionally, it may be bile, haemoglobin, or a derivative of any 35 thereof.

Optionally, a faecal and/or body fluid specimen may be analysed for the presence of a microbe and/or to analyse a microbiome. Details of analysis of microbes and/or the microbiome are provided elsewhere herein.

Optionally, a faecal and/or body fluid specimen other than blood may be analysed for the presence of blood. For example, the presence of blood in urine may be indicative of an infection or other disease. For example, the presence of blood in a faecal specimen may optionally be used to analyse a bleed in the GI tract and/or anus. Optionally, the bleed may be G0 indicative of a disease selected; for example, from anal fissure, diverticular disease, an inflammatory disease, angiodysplasia, and/or any of the diseases mentioned elsewhere herein. Optionally, a faecal and/or body fluid specimen may be analysed for the presence of bile or a derivative thereof, e.g., to analyse a liver and/or kidney disease, and/or any of the diseases mentioned elsewhere herein.

Optionally, a faecal and/or body fluid specimen may be analysed for the presence and/or level of a compound, e.g., a compound comprising or consisting of a lipid, such as, a glycolipid or phospholipid; a carbohydrate; DNA; RNA; a protein; a polypeptide, such as. a ribosomal peptide or a non-ribosomal peptide; an oligopeptide; a lipoprotein; a lipopeptide; an amino acid; and/or a chemical molecule, optionally an organic chemical molecule. Optionally, the compound may be endogenous, i.e. produced by the subject, or exogenous, i.e., administered, ingested or otherwise introduced into the subject.

Optionally, the compound may be a therapeutic drug, an illicit drug, or a metabolite or derivative of a therapeutic or illicit drug.

It may optionally be selected; e.g., from any of the drugs or agents mentioned herein, and/or Mescaline, PCP (Phencyclidine), Psilocybin, LSD, Heroin, Morphine, Codeine, dextroamphetamine, bupropion, cathinone, lisdexamfetamine, Allobarbital, Alphenal (5-ally)-5-phenylbarbituric acid), Amobarbital, Aprobarbital, Brallobarbital, Butobarbital, Butalbital, Cyclobarbital, Methylphenobarbital, Mephobarbital, Methohexital, Pentobarbital, Phenobarbital, Secobarbital, Talbutal, Thiamylal, and/or Thiopental. Ranitidine, phenylalanine PKU, dimethylamylamine, cocaine, diazepam, androstadienedione, stigmastadienone, androsteronehemisuccinate, 5a-androstan-313,17/3-dio1-16-one, androsterone glucuronide, epitestosterone, 6-dehydrocholestenone; phenylalanine, leucine, valine; tyrosine, methionine, sitamaquine, terfenadine, prazosin, methadone, amitripyline, nortriptyline. pethidine, DOPA, ephedrine, ibuprofen, propranolol, atenolol, acetaminophen, bezethonium, citalopram, dextrorphan, paclitaxel, proguanil, simvastatin, sunitinib, telmisartan, verapamil, amitriptyline, pazopanib, tamoxifen, imatinib, cyclophosphamide, irinotecan, docetaxel, topotecan, acylcarnitines (02-C18), nicotine, cotinine, trans-3-hydroxycotinine, anabasine, amphetamine, amphetamine-like stimulants, methamphetamine, MDA, MDMA, MDEA, morphine, A9-THC, tacrolimus, benzethonium, meprobamate; 0-desmethyl-cis-tramadol, carisoprodol, tramadol, nordiazepam, EDDP, norhydrocodone, hydromorphone, codeine, temazepam, noroxycodone, alprazolam, oxycodone, buprenorphine, norbuprenorphine, fentanyl, propoxyphene, 6-monoacetylmorphine, caffeine, carbadox, carbamazepine, digoxigenin, diltiazem, diphenhydramine, propanolol, sulfadiazine, sulfamethazine, sulfathiazole, thiabendazole, ketamine, norketamine, BZE, AMP, MAMP, and/or 6-MAM.

The analysis of faecal specimens may optionally involve the use forceps-based REIMS, wherein a sample of the faecal specimen may be taken between the forceps and the probes may then be drawn together.

Imaging According to the various embodiments herein, ion imaging may be used to generate an image or map of one or more properties of the target. This may be achieved by using the first 61.

device to generate aerosol, smoke or vapour from multiple different regions of the target; ionising analytes in the smoke, aerosol or vapour originating from the different regions to produce analyte ions (or ions derived therefrom, e.g., fragment ions); and then analysing the analyte ions (or ions derived therefrom) to obtain spectrometric data for each of the regions of the target. The spectrometric data is correlated to the region of the target to which it relates (i.e. from where the smoke, aerosol or vapour that generated the spectrometric data originated from) so as to generate image or map data. An image or map of the target can then be generated based on the image or map data. For example, one or more properties of each region of the target may be determined from the spectrometric data and this may be included in the image or map data and hence mapped as a function of location within the target. The image or map data may then be displayed to a user.

The first device may be stepped between multiple spaced apart regions of the target so as to generate the aerosol, smoke or vapour from discrete regions of the target. Alternatively, a plurality of devices may be used to generate the aerosol, smoke or vapour from discrete regions of the target, optionally simultaneously. These plurality of devices may not move across the target, although may move into and out of engagement with the target. Spatial profiling of the target may therefore be performed (e.g., which does not perform a continuous map). Alternatively, the first device may be moved across or through the target continuously so as to generate aerosol, smoke or vapour from the different regions of the target. Any movements of the first device, or the plurality of devices, may be automated and controlled by a machine.

The spectrometric data for each region may be analysed and converted into data representative of the type, condition or constituent(s) of the material at that region in the target.

The representative data may then be displayed as an image or map showing the type, condition or constituents of the material as a function of location in the target.

For example, the representative data may indicate the type, level, presence and/or absence of: diseased; cancerous; and/or necrotic material at each of the regions in the target. For example, the spectrometric data may be used to identify and/or display the locations of margins of diseased, cancerous, and/or necrotic tissue in the target. These tissue types, such as tumour tissue, may closely resemble normal tissue and may have indistinct boundaries, making it difficult to determine where the tumour ends and the normal tissue begins. The method of the invention enables the locations of such tissue margins to be identified. Additionally, or alternatively, the spectrometric data may be used to identify and/or display the location and/or margins of one or more cell or tissue type of interest. For example, the cell or tissue type of interest may comprise diseased and/or cancerous and/or necrotic tissue or cells in the target; and/or the cell or tissue type of interest may comprise healthy tissue or cells.

The representative data may indicate the different type of cells or constituents in the target.

Additionally, or alternatively, the representative data may indicate the presence and/or distribution of one or more types of microbes within the target.

Additionally, or alternatively, the representative data may indicate the presence and/or distribution of one or more types of compounds within the target.

Additionally, or alternatively, the representative data may indicate the type or level of biomarker in the target, and the distribution of the type or level of biomarkers within a target may be identified and/or displayed.

The ion imaging and map data may be generated and/or displayed in real-time. This may be useful, for example, to determine action to be taken during surgical procedures. The position of at least a portion of the first device and/or another tool relative to the target may be displayed on the image or map, e.g., in real time. For example, the position of a surgical tool, such as a tool for resecting or ablating tissue, may be displayed on the map of the target. This enables the surgeon to selectively resect or ablate tissue based on the representative data displayed in the image or map.

Ion imaging mass and/or ion mobility spectrometry technology, such as DESI-MS and/or REIMS technology, may optionally be used to obtain the spectrometric data for the different regions of the target. A REIMS technology device may optionally be used in cutting and/or pointing mode.

Ion imaging is illustrated in Example 18 and exemplary details are also provided in

Example 21.

This ion imaging analysis may optionally be combined with a further analysis of the specimen. Details of further analysis methods and tools are provided elsewhere herein. Optionally, the results of mass and/or ion mobility spectrometry imaging may be correlated with the results of a further analysis.

For example, optionally the method may be used for imaging to distinguish between tumour, stroma and/or healthy tissue.

Therapy-related methods The method of the present invention may optionally be used to monitor the progress of disease.

During therapy or subsequent to therapy, the method of the present invention may optionally be used to monitor the progress of disease to assess the effectiveness of therapy, or to monitor the progress of therapy.

Optionally, serial (periodic) analysis of a target for a change may be used to assess whether or not therapy has been effective; the extent to which therapy has been effective; whether or not a disease is re-occurring or progressing in the subject; and/or to assess the likely clinical outcome (prognosis) of the disease, should it re-occur or progress.

Optionally, the method may be used in the active monitoring of subjects which have not been subjected to therapy, e.g. to monitor the progress of the disease in untreated subjects.

Optionally, serial (periodic) analysis of a target for a change may be used to assess whether or not, or the extent to which, the disease is progressing, thus, for example, allowing a more reasoned decision to be made as to whether therapeutic intervention is necessary or advisable.

Such monitoring may optionally be carried out on a healthy individual, e.g., an individual who is thought to be at risk of developing a particular disease, in order to obtain an early and ideally pre-clinical indication of said disease. A particular example is cervical smear testing to analyse the cervix for cancer or pre-cancerous biomarkers.

The skilled person will appreciate that any of the methods provided herein may optionally be combined with one or more of the other methods provided herein and/or with one or more further methods.

For example, provided is a method which is a combination of two or more, e.g. three or more, four or more or five or more of the methods disclosed herein. Two or more of the diagnosis, prognosis, prediction, assessment, monitoring and/or stratification methods disclosed herein may be combined in any combination. When combining the methods, each method may be referred to as a step. The details provided herein regarding the methods of the invention apply mutatis mutandis to these steps.

Thus, provided is a method of assessing the onset and course of a disease, said method including at least two steps selected from a step of diagnosing disease, a step of monitoring the progression of disease, a step of predicting the likelihood of disease response to treatment, a step of stratification, a step of prognosis; and a step of assessing response to treatment. Optionally, said method includes at least 3, 4, 5 or 6 of these steps. Optionally, any of these steps may be carried out more than once. For example, a step of monitoring the progression of disease may optionally be carried out both before and after treatment.

Optionally, any of the methods provided herein may also include a step of determining whether the subject should receive a treatment. Suitable treatments are discussed elsewhere herein. Particularly, if the method involves a determination that the subject has a disease, that a disease has developed, that a disease has progressed, that the prognosis is poor, that a disease is likely to respond to treatment, and/or that a disease has responded to treatment, then the method may include a step of determining that the subject should receive an appropriate treatment.

Optionally, any of the methods provided herein may also include a step of determining, for a subject who is receiving, or has received, treatment; whether the treatment should be altered or ceased. For example, the method may optionally include a step of determining that the treatment dose and/or frequency should be increased or decreased. In particular, if the method involves a determination that one or more biomarkers for a disease are increased, have increased over time, or have not decreased (or not decreased sufficiently) in response to a treatment, then the method may optionally include a step of determining that the treatment dose and/or frequency should be increased; and if the method involves a determination that one or more biomarkers for a disease are not increased, have decreased over time; or have decreased in response to a treatment, then the method may optionally include a step of determining that the treatment dose and/or frequency should be decreased or that the treatment may be ceased; or vice versa.

The method may include a step of determining that a particular treatment should be replaced by another treatment, for example that one drug should be replaced with another drug. In particular, if the method involves a determination that one or more biomarkers for a disease are increased, have increased over time, or have not decreased (or not decreased sufficiently) in response to a treatment, then the method may include a step of determining that the treatment should be replaced by another treatment; and if the method involves a determination that one or more biomarkers for a disease are not increased, have decreased over time, or have decreased in response to a treatment, then the method may include a step of determining that the treatment should not be replaced by another treatment; or, vice versa.

Optionally, any of the methods provided herein may also include a step of administering a treatment to said subject. The method may then, for example, be referred to as a method of diagnosis and treatment; monitoring and treatment; prognosis and treatment; prediction and treatment; or stratification and treatment.

Optionally, any of the methods provided herein may be used in conjunction with any other known methods, particularly a known diagnostic, prognostic, predictive, and/or monitoring method for a disease.

Treatments and agents Cancer treatments and anti-cancer agents The treatment may optionally be an anti-cancer treatment, for example, if cancer is detected. Reference herein to "anti-cancer treatment" includes any treatment/agent directed at treating cancer. The terms "drug treatment", "drug" and "agent" are used interchangeably herein. The treatment may optionally involve surgery, radiation and/or drugs. Drug treatment may optionally involve chemotherapy. Optionally, the treatment may be a combination treatment in which 2 or more different therapeutic agents are used simultaneously, separately or sequentially.

Surgery may optionally be selected, for example, from lumpectomy and mastectomy.

Drugs may optionally be selected, for example, from hormonal therapy with, e.g., tamoxifen or aromatase inhibitors. Drug treatment may optionally involve, for example, an antibody specific for a receptor expressed by cancer cells, which may optionally be conjugated to a chemotherapy drug or to a radioactive particle.

The antibody may optionally, for example, be selected from a HER-2/neu specific monoclonal antibody, such as, Trastuzumab (Herceptin); Adecatumumab, alemtuzumab, Blinatumomab, Bevacizumab, Catumaxomab, Cixutumumab, Gemtuzumab, Rituximab, Trastuzumab, and/or lbritumomab.

Drug treatment may optionally involve, for example, an anti-angiogenic agent.

Drug treatment may optionally involve, for example, a cytostatic agent, optionally selected from an alkylating agent, a cross-linking agent, an intercalating agent, a nucleotide analogue, an inhibitor of spindle formation, and/or an inhibitor of topoisomerase I and/or II. More, particularly, it may optionally be selected from, for example, actinomycin D, BCNU (carmustine), carboplatin, CCNU, Campothecin (CPT), cantharidin, Cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, Doxorubicin, DTIC, epirubicin, Etoposide, gefinitib, gemcitabine, ifosamide irinotecan, ionomycin, Melphalan, Methotrexate, Mitomycin C (MMC), mitozantronemercaptopurine, Oxaliplatin, Paclitaxel (taxol), PARP-1 inhibitor, taxotere, temozolomide (TZM), teniposide, topotecane, treosulfane vinorelbine, vincristine, vinblastine, 5-Azacytidine, 5,6-Dihydro-5-azacytidine and 5-fluorouracil.

Antimicrobial treatments G5 The treatment may optionally be an antimicrobial treatment, for example, if a microbial infection or imbalance is detected.

The term "antimicrobial" includes any agents that act against any type of microbe. Thus, the antimicrobial may optionally be selected from antibacterial, an antiviral, an antifungal, and an antiprotozoal. More particularly, it may optionally be selected from aminoglycosides, beta-lactam antibiotics, chloramphenicol, fluroquinolones, glycopeptides, lincosamides, macrolides, polymixins, rifampins, streptogramins, sulphonamides, tetracyclines, and/or diaminopyrimidines.

The Aminoglycoside may optionally be selected from gentamicin, tobramycin, amikacin, streptomycin, kanamycin. The beta-lactam antibiotic may optionally be selected from a penicillin such as methicillin, penicillin, amoxicillin, ampicillin, carbenicillin, oxacillin or nafcillin; a cephalosporin, such as, cephalothin, cefamandole, cefotaxime, ceftazidime, cefoperazone, or ceftriaxone; a carbapenem, such as, imipenem, meropenem, ertapenem, ordoripenem; or a monobactam, such as, aztreonam. The fluroquinolone may optionally be selected from Enrofloxacin, ciprofloxacin, Danofloxacin, Difloxacin, Ibafloxacin, Marbofloxacin, Pradofloxacin and Orbifloxacin. The glycopeptide may optionally be selected from vancomycin, teicoplanin and avoparcin. The lincosamide may optionally be selected from Lincomycin, Clindamycin and Pirlimycin. The macrolide may optionally be selected from Erythromycin, Tylosin, Spiramycin, Tilmicosin and Tulathromycin. The polymixin may optionally be selected from Polymixin B and colistin (Polymixin E). The rifampin may optionally be selected from Rifampin, Rifabutin and Rifapentine. The Streptogramin may optionally be selected from Virginiamycin. The sulfonamide may optionally be selected from Sulfadiazine, sulfamethoxazole and sulfadoxine. The tetracycline may optionally be selected from Chlortetracycline, oxytetracycline, demethylchlortetracycline, rolitetracycline, limecycline, clomocycline, methacycline, doxycycline and minocycline. The Diaminopyrimidine may optionally be selected from Trimethoprim, Aditoprim, Baquiloprim and/or Ormetoprim.

Probiotic treatments The treatment may optionally be an probiotic treatment, for example, if a microbial imbalance is detected, or in the treatment of a gastrointestinal disorder, such as, any of those mentioned herein.

The probiotic may comprise one or more live bacteria and/or yeasts. Optionally, it may also comprise one or more prebiotics, which are carbohydrates that act as food for probiotics and are non-digestible by humans.

Gastrointestinal and/or anti-inflammatory treatments The treatment may optionally involve surgery and/or drugs.

Drug treatment may optionally involve, for example, an antibody, selected, for example, from Adalimumab, Certolizumab, Infliximab, and/or Natalizumab.

Drug treatment may optionally involve, for example, an anti-inflammatory drug.

Anti-inflammatory drugs may optionally be selected from, e.g., steroids, diclofenac, ibuprofen, naproxen, celecoxib, mefenamic acid, etoricoxib, indomethacin, and/or aspirin.

Analysis of radio-tracers Positron Emission Tomography (PET) is a radiotracer imaging technique, in which tracer compounds labelled with positron-emitting radionuclides are injected into the subject of the study. These radio-tracer compounds can then be used to track biochemical and physiological processes in vivo. One of the prime reasons for the importance of PET in medical research and practice is the existence of positron-emitting isotopes of elements such as carbon, nitrogen, oxygen and fluorine which may be processed to create a range of radio-tracer compounds which are similar to naturally occurring substances in the body.

Optionally, the radio-tracer may be a compound labelled with "C, 13N, 150, and/or 15F.

Optionally, it may be selected from the compounds listed in the table below.

Isotope Tracer compound Physiological process or function Typical application 11C methionine protein synthesis oncology flumazenil benzodiazepine receptor antagonist epilepsy raclopride D2 receptor agonist movement disorders 13N ammonia blood perfusion myocardial perfusion carbon dioxide blood perfusion brain activation studies 750 water blood perfusion brain activation studies 18F Fluoro-deoxy-glucose glucose metabolism oncology, neurology, cardiology 15F Fluoride ion bone metabolism oncology 18F Fluoro- hypoxia oncology -response to radiotherapy mizonidazole Thus, e.g., if the biologica y active molecule chosen is fluorodeoxyglucose (FDG), an analogue of glucose, the concentrations of tracer will indicate tissue metabolic activity as it corresponds to the regional glucose uptake. Use of this tracer to explore the possibility of cancer metastasis (i.e., spreading to other sites) is the most common type of PET scan in standard medical care (90% of current scans).

Optionally, a subject and/or specimen may be exposed to a radio-tracer and the method may be used to analyse the location and/or concentration of a radio-tracer. Thus, the method may optionally be used to analyse the metabolism of a compound labelled with a positron-emitting radionuclide.

Xenografts Cells and/or tissue may optionally be xenografted into a host organism for a suitable period of time, e.g., at least 1, 2 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours and/or 1, 2 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17. 18, 19, 20, 21, 22; 23, or 24 days. For example, cells or tissue obtained from a human tumour may be xenografted into a host animal. Optionally; the method may involve making a xenograft and/or removing a xenograft or sample thereof from a host organism. Optionally, the method may be performed on a provided xenograft.

Optionally, the xenograft may comprise or consist of tumour cells. A xenograft specimen may optionally be analysed, e.g., to analyse the impact of the host environment on the cells of the xenograft. Optionally, a cell population and/or tissue may be analysed prior to and after xenografting, and/or a xenograft specimen may be compared to a cell population or tissue that was not xenografted.

Further Definitions The term "target entity" is used herein to refer to the entity which it is desired to analyse within the target. Thus, any reference to a "target" should be understood to mean a target comprising one or more different target entities. Thus, the target entity may, e.g., be a cell, microbe and/or compound. For example, the target may be tissue and the target entity may be cancer cells.

The terms "analysis", "analysing" and derivatives of these terms are used herein to encompass any of the following: detection of a target entity; identification of a target entity; characterisation of a target entity; determination of the location of target entity; determination of a status, e.g. a disease status; and/or determination of a margin between two different disease or tissue types and the like.

It should be understood that any reference herein to "analysing" a target is intended to mean that the target is analysed on the basis of the spectrometric data. Thus, for example, by an expression, such as, "analysing spectrometric data in order to identify a cell type" is meant that the identity of a cell type is determined based upon the spectrometric data.

The analysis may be qualitative and/or quantitative. Thus, optionally, any type of analysis may involve determining the concentration, percentage, relative abundance or the like of the target entity. For example, the percentage of cancer cells within a tissue, the relative abundance of microbes in a target, and/or the concentration of a compound may be analysed. Optionally, an increase or decrease in a target entity may be analysed.

The terms "detection", "detecting" and derivations of these terms are used interchangeably herein to mean that the presence or absence of a target entity or biomarker therefor is determined.

The terms "identify", "identification" and derivations of these terms are used interchangeably herein to mean that information about the identity of a target entity or biomarker therefor is obtained. This may optionally be the determination of the identity, and/or the confirmation of the identity. This may optionally include information about the precise identity of the target entity or biomarker therefor. However, it may alternatively include information that allows the target entity to be identified as falling into a particular classification, as discussed elsewhere herein.

By "identifying" a microbe is meant that at least some information about the identity is obtained, which may, for example, be at any taxonomic level.

By "identifying" a cell is meant that at least some information about the cell type is obtained. By "identifying" a diseased cell is meant that it is determined or confirmed that a cell is diseased.

By "identifying" a compound is meant that at least some information about the structure and/or function of the compound is obtained, e.g., the information may optionally allow a compound to be identified as comprising or consisting of a compound selected from any of the types disclosed herein, and/or as being characterised by one or more of the functional groups disclosed herein.

The terms "diagnosis" or "diagnosing" and derivations of these terms as used herein refer to the determination whether or not a subject is suffering from a disease. Optionally, the method may involve analysing a target and, on the basis of one or more of the following making a diagnosis that a subject is or is not suffering from a particular disease: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

An increase or decrease may be determined by reference to a suitable reference, comparator or control. For example, it is known how many inflammatory cells or inflammatory molecules are typically present in the tissue of a healthy individual, so an increase in inflammatory cells or inflammatory molecules in a target may easily be determined by comparing it to a healthy control.

The term "monitoring" and derivations of this term as used herein refer to the determination whether any changes take place/have taken place. Typically, it is determined whether any changes have taken place over time, i.e. since a previous time point. The change may, for example, be the development and/or progression of a disease, such as, any of the diseases mentioned. Optionally, the method may involve analysing a target and, on the basis of one or more of the following monitoring a subject or disease: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

The term "prognosis" and derivations of this term as used herein refer to risk prediction of the severity of disease or of the probable course and clinical outcome associated with a disease. Thus, the term "method of prognosis" as used herein refers to methods by which the skilled person can estimate and/or determine a probability that a given outcome will occur. The outcome to which the prognosis relates may be morbidity and/or mortality. In particular, the prognosis may relate to "progression-free survival" (PFS), which is the length of time that a subject lives with the disease without the disease progressing. Thus, PFS may, for example, be the time from the start of therapy to the date of disease progression, or the time from the end of therapy to the date of disease progression. G9

Optionally, the method may involve analysing a target and, on the basis of one or more of the following making a prognosis: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

By "progressing" or "progression" and derivations of these terms is meant that the disease gets worse; i.e. that the severity increases. For example, in the case of cancer, it may mean that the tumour burden increases, for example a tumour increases in size and/or weight; that the cancer becomes malignant or more malignant; and/or that metastasis develops or the incidence and/or rate of metastasis increases.

The prognosis may relate to overall survival. By "overall survival" (OS) is meant the length of time that a subject lives with the disease before death occurs. Overall survival may, for example, be defined as the time from diagnosis of the disease; the time of treatment start; or the time of treatment completion, until death. Overall survival is typically expressed as an "overall survival rate", which is the percentage of people in a study or treatment group who are still alive for a certain period of time after they were diagnosed with, or started treatment for, or completed treatment for, a disease. The overall survival rate may, for example, be stated as a five-year survival rate, which is the percentage of people in a study or treatment group who are alive five years after their diagnosis or the start or completion of treatment.

Statistical information regarding the average (e.g. median, mean or mode) OS and PFS of subjects having a particular type of disease is available to those skilled in the art. A determination whether a subject has, or is likely to have, an increased or decreased OS or PFS compared to such an average may therefore be made.

A determination that the likelihood and/or length of PFS and/or overall survival is decreased means that the prognosis is poor or adverse. The terms "poor" and "adverse" are used interchangeably herein. A "poor" prognosis may be defined as a prognosis that is worse than the reference prognosis for a subject, so it may also be referred to as a "worse" prognosis, and a "good" or "non-adverse" prognosis may be defined as a prognosis that is better than the reference prognosis for a subject so it may also be referred to as a "better" prognosis. The skilled person will appreciate that for the "reference prognosis" subjects having the same type of disease, optionally the same stage of disease, should be used. The "reference prognosis" may be the average prognosis or a typical prognosis determined by any other suitable method.

An adverse or worse prognosis may be defined as a shorter overall survival or an increased likelihood of shorter overall survival and/or shorter PFS or an increased likelihood of shorter PFS.

By "regressing" or "regression" is meant that the disease improves, i.e. that the severity decreases. For example, in the case of cancer or a tumour, it may mean that the tumour burden decreases, for example a tumour decreases in size and/or weight, or becomes undetectable; that the cancer becomes less malignant; and/or that the incidence and/or rate of metastasis decreases.

A response to treatment may include progression, regression, a combination of progressive and regressive elements, or the absence of any progression or regression. Thus, for example, in the case of cancer, a response to treatment may include a change in one or more criteria selected from tumour size, tumour weight, tumour number, malignancy and metastasis.

By "development" is meant the onset of a disease.

The term "prediction" or "predicting" as used herein refers to determining the likelihood of a particular outcome.

The term "stratification" or "stratifying" as used herein refers to the division of a population into subpopulations on the basis of specified criteria. More particularly, it refers to the division of a cohort of subjects into at least two groups on the basis of specific criteria, which in the context of the present invention comprise or consist of the results of the method of analysis. Optionally, subjects may be stratified into those likely to respond to a particular treatment and those unlikely to respond; and/or subjects may be stratified based on their diagnosis, prognosis and/or the response that they have presented to treatment.

Optionally, the method may involve analysing a target and, on the basis of one or more of the following, stratifying subjects: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

The term "treatment" or "treating" as used herein refers to a course of action which is aimed at bringing about a medical benefit for a subject. The treatment may be prophylactic or therapeutic.

By "prophylactic" is meant that the treatment is preventative, i.e. it is applied before the onset of disease. By "therapeutic" is meant that the treatment is applied after the onset of disease.

Optionally, the method may involve analysing a target and, on the basis of one or more of the following, determining that a subject should or should not receive a particular treatment: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

Optionally, the method may involve analysing a target and, on the basis of one or more of the following, determining that a subject has or has not responded a particular treatment: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

Optionally, the method may involve analysing a target and, on the basis of one or more of the following, administering a particular treatment to a subject: detecting a target entity; identifying a target entity; detecting an increase in a target entity; detecting a decrease in a target entity.

Optionally, the method may additionally involve one or more of the following steps, particularly in the context of diagnosis: a) Determining the presence of one or more symptoms of disease; b) blood test; c) bone marrow test; d) bone scan; e) computerised tomography (CT) scan; f) x-ray; m) MRI; n) positron emission tomography (PET) scan; o) ultrasound scan; p) biopsy analysis; q) Metabolomics, i.e. the study of the entire set of small-molecule metabolites present in a biological specimen.

Analysis of spectrometric data Any of the methods of the invention may optionally involve the analysis of spectrometric data; more particularly, the analysis of spectrometric data from a target, e.g., a first target location. The terms "spectral data" and "spectrometric data" are used interchangeably herein.

The analysis of a target may be based solely on the analysis of spectral data, or it may optionally involve one or more further analytical tools, details of which are discussed elsewhere herein.

In some embodiments, the spectrometric data may optionally provide direct information about the target or target entity.

For example, if a particular cell type has a specific spectrometric signal pattern, then obtaining this signal pattern from a target provides direct information about the presence, identity and/or characteristics of that cell type.

For example, if a particular microbe has a specific spectrometric signal pattern, then obtaining this signal pattern from a target provides direct information about the presence, identity and/or characteristics of that microbe.

For example, if a particular compound has a specific spectrometric signal pattern, then obtaining this signal pattern from a target provides direct information about the presence, identity and/or characteristics of that compound. This may be the case, for example, for a compound which is secreted by a cell and/or by a microbe, or for an agent, such as, a drug or a metabolite thereof.

However, in other embodiments, spectrometric data may optionally provide indirect information about the target or target entity. This may be the case, for example, for a compound which is produced, but not secreted, by a cell and/or by a microbe. The presence of this compound may optionally be detected indirectly by detecting a spectrometric signal pattern which is characteristic of a cell and/or microbe containing said compound.

Spectrometric data obtained from a target, e.g., a first target location, may optionally be compared to one or more other spectrometric data, which may conveniently be referred to herein as "reference", "control" or "comparator" spectrometric data.

As explained elsewhere herein, analysing spectrometric data may optionally comprise analysing one or more sample spectra so as to classify an aerosol, smoke or vapour sample. This may comprise developing a classification model or library using one or more reference sample spectra, or may comprise using an existing library.

Optionally, an analysis may be made to determine whether spectrometric data obtained from a target matches or corresponds sufficiently to the "reference", "control" or "comparator" spectrometric data to make a positive determination. Optionally, a positive determination may be made if the spectrometric data corresponds more closely to one library entry than any other library entry.

The term "reference" spectrometric data is used herein to mean spectrometric data from a known cell type, microbe or compound. Reference spectrometric data may optionally be publicly available, or the skilled person may generate a library of reference spectrometric data.

The method may optionally involve comparing the spectrometric data to one or more reference spectrometric data. If the spectrometric data obtained from a target matches or corresponds sufficiently to a reference spectrometric data, then optionally a positive determination may be made. If the spectrometric data obtained from a target does not match or correspond sufficiently to a reference spectrometric data, then optionally a negative determination may be made.

The term "comparator' spectrometric data is used herein to mean spectrometric data obtained from a second target location. The first and second target locations may be located in different targets, or at the different locations of the same target. The method may optionally involve comparing the spectrometric data to one or more comparator spectrometric data. If the spectrometric data obtained from a target matches or corresponds sufficiently to a comparator spectrometric data, then optionally a positive determination may be made. If the spectrometric data obtained from a target does not match or correspond sufficiently to a comparator spectrometric data, then optionally a negative determination may be made.

The term "control" spectrometric data is used herein to mean spectrometric data obtained from the first target at an earlier point in time. Control spectrometric data may, for example, be used when monitoring, e.g., an operation; a disease, a cell culture, a tissue culture, and/or a microbial culture. Any of the methods may optionally involve comparing the spectrometric data to one or more control spectrometric data. If the spectrometric data obtained from a target matches or corresponds sufficiently to a control spectrometric data; then optionally a positive determination may be made. If the spectrometric data obtained from a target does not match or correspond sufficiently to a control spectrometric data, then optionally a negative determination may be made.

By a "positive determination" is meant that the presence, identity and/or characteristics of a particular cell type, microbe and/or compound is determined. For example, a positive determination may involve determining that a target entity of a particular classification is present; that a target entity has a certain characteristic; and/or that a particular compound is present.

For example, in the case of a microbial target entity, a positive determination may, e.g., involve determining that a microbe of a particular taxonomic rank is present; that a particular microbe has a certain characteristic, such as, resistance to a particular drug; and/or that a particular compound is being produced by a microbe.

For example, in the case of a cell target entity, a positive determination may, e.g., involve determining that a cancer cell or lymphocyte is present; and/or that a cell has a certain characteristic, such as, that it expresses a particular cell surface marker.

For example, in the case of a compound target entity, a positive determination may, e.g., involve determining that a particular type of compound is present; and/or that a compound has a certain characteristic, such as, a particular glycosylation pattern.

Thus, for example, if the spectrometric data of a first sample matches or corresponds sufficiently to a reference spectrometric data, then the presence in the first sample of a target entity corresponding to the entity from which the reference spectrometric data was obtained may optionally be confirmed. If the spectrometric data of a first sample matches or corresponds sufficiently to a reference spectrometric data, then the target entity present in the first sample may optionally be identified as corresponding to the identity of the entity from which the reference spectrometric data was obtained. If the spectrometric data of a first sample matches or corresponds sufficiently to a reference spectrometric data, then the target entity present in the first sample may optionally be characterised as having a characteristic corresponding to the characteristic of the entity from which the reference spectrometric data was obtained. If the spectrometric data of a first sample matches or corresponds sufficiently to a reference spectrometric data, then a determination may optionally be made that the target entity present in the first sample produces the compound produced by the entity from which the reference spectrometric data was obtained.

As explained elsewhere herein, by determining or confirming the "identity" of a microbe or cell is meant that at least some information about the identity is obtained, which may, for example, be at any taxonomic level. Thus, for example, if the reference spectrometric data is from Candida albicans, then in one embodiment a match or sufficient correspondence may optionally be used to identify the first microbe as belonging to the genus Candida, whereas in another embodiment a match or sufficient correspondence may optionally be used to identify the first microbe as belonging to the species Candida albicans.

As another example, if the spectrometric data of a first sample matches or corresponds sufficiently to a comparator spectrometric data, then the presence in the first sample of a target entity corresponding to the entity from which the comparator spectrometric data was obtained may optionally be confirmed. If the spectrometric data of a first sample matches or corresponds sufficiently to a comparator spectrometric data, then the target entity present in the first sample may optionally be identified as corresponding to the identity of the entity from which the comparator spectrometric data was obtained. If the spectrometric data of a first sample matches or corresponds sufficiently to a comparator spectrometric data, then the target entity present in the first sample may optionally be characterised as having a characteristic corresponding to the characteristic of the entity from which the comparator spectrometric data was obtained. If the spectrometric data of a first sample matches or corresponds sufficiently to a comparator spectrometric data, then a determination may optionally be made that the target entity present in the first sample produces the compound produced by the entity from which the comparator spectrometric data was obtained.

In other words, a match or sufficient correspondence to a reference or comparator spectrometric data respectively may be used to confirm that the first target entity and the reference or comparator entity respectively have the same identity, whereas the lack of a match or sufficient correspondence to a reference or comparator spectrometric data respectively may be used to confirm that the first target entity and the reference or comparator entity respectively do not have the same identity.

By a "negative determination" is meant that the absence of a particular target entity is determined; and/or that it is determined that a target entity does not have a particular identity and/or characteristic.

For example, a negative determination may involve determining that a particular target entity is not present; that a particular target entity does not have a certain characteristic; and/or that a particular compound is not present.

For example, in the case of a microbial target entity, a negative determination may, e.g., involve determining that a microbe of a particular taxonomic rank is not present; that a particular microbe does not have a certain characteristic such as resistance to a particular drug; and/or that a particular compound is not being produced.

For example, in the case of a cell target entity, a negative determination may, e.g., involve determining that a cancer cell or lymphocyte is not present; and/or that a cell does not have a certain characteristic, such as, that it does not express a particular cell surface marker.

For example, in the case of a compound target entity, a negative determination may, e.g., involve determining that a particular type of compound is not present; and/or that a compound does not have a certain characteristic, such as, a particular glycosylation pattern.

Thus, for example, if the spectrometric data of a first sample does not match or correspond sufficiently to a reference spectrometric data, then the absence or insufficient presence in the first sample of a target entity corresponding to the entity from which the reference spectrometric data was obtained may optionally be confirmed. If the spectrometric data of a first sample does not match or correspond sufficiently to a reference spectrometric data, then the target entity present in the first sample may optionally be identified as not corresponding to the identity of the entity from which the reference spectrometric data was obtained. If the spectrometric data of a first sample does not match or correspond sufficiently to a reference spectrometric data, then the target entity present in the first sample may optionally be characterised as not having a characteristic corresponding to the characteristic of the entity from which the reference spectrometric data was obtained. If the spectrometric data of a first sample does not match or correspond sufficiently to a reference spectrometric data, then a determination may optionally be made that the target entity present in the first sample does not produce, or insufficiently produces, the compound produced by the entity from which the reference spectrometric data was obtained.

As another example, if the spectrometric data of a first sample matches or corresponds sufficiently to a control spectrometric data, then a determination may be made that no, or no significant, change has taken place, whereas if the spectrometric data of a first sample does not match or correspond sufficiently to a control spectrometric data, then a determination may be made that a change, optionally a significant change, has taken place. Examples of a change may, for example, be the presence of a contaminating or infiltrating cell, microbe and/or compound; or a change in the cell or microbe's behaviour or its environment, such as, a change in the cell or microbe's growth rate, respiration rate; rate of production of a compound, such a secreted compound; environmental temperature, pH, nutrient availability and so on.

As mentioned elsewhere herein, the method may optionally involve the analysis of biomarkers.

If a biomarker for a target entity or disease status is known (e.g., from the prior art or from the work disclosed herein), then the method may optionally involve analysing the target for the presence of the spectrometric signal of that biomarker. The spectrometric signal of any biomarker may optionally be looked up in the literature, a database, or, if necessary, it may easily be determined experimentally.

For example, as shown herein, C26:1 sulfatide (C50H94N011S) is a biomarker for normal brain tissue, with a spectrometric signal of m/z about 916.655. When analysing a brain target to try to distinguish between healthy and diseased brain tissue, the method may optionally involve analysing the target for the presence of a spectrometric signal of m/z about 916.655.

As mentioned elsewhere herein, the analyte giving rise to a particular spectrometric signal, e.g., a particular m/z, may optionally be further characterised, e.g., using MS-MS. Thus, ionic species in the mass spectra may optionally be identified based on exact mass measurements, e.g., with a mass deviation <3ppm, and/or MS/MS fragmentation patterns.

Isobaric lipids with different head groups may optionally be differentiated by ion mobility.

Thus, optionally, the method may involve analysing the target for the presence of a spectrometric signal of one or more biomarkers, optionally selected from any of the biomarkers mentioned herein.

A biomarker for diseased cells may optionally be determined, e.g., by subtracting the spectrometric signals obtained from normal cells from the spectrometric signals obtained from diseased cells, to arrive at spectrometric signals that are specific for the diseased cells. Optionally, the analyte giving rise to a particular m/z and/or ion mobility spectrometric signal may optionally be further characterised, e.g., using MS/MS. Thus, ionic species in the mass and/or ion mobility spectra may optionally be identified based on techniques such as use of the ion mobility drift time and/or exact mass measurements (e.g., with a mass deviation <3ppm), and/or MS/MS fragmentation patterns and/or Thus, optionally, the method may involve analysing the target for the presence of a spectrometric signal of one or more biomarkers, optionally selected from any of the biomarkers mentioned herein.

The spectrometric data may comprise one or more sample spectra. Obtaining the spectrometric data may comprise obtaining the one or more sample spectra. Analysing the spectrometric data may comprise analysing the one or more spectra. Obtaining the one or more sample spectra may comprise a binning process to derive a set of time-intensity pairs and/or a set of sample intensity values for the one or more sample spectra. The binning process may comprise accumulating or histogramming ion detections and/or intensity values in a set of plural bins. Each bin in the binning process may correspond to particular range of times or time-based values, such as masses, mass to charge ratios, and/or ion mobilities. The bins in the binning process may each have a width equivalent to a width in Da or Th (Date) in a range selected from a group consisting of: (i) < or > 0.01; (ii) 0.01-0.05; (iii) 0.05-0.25; (iv) 0.25-0.5; (v) 0.5-1.0; (vi) 1.0-2.5; (vii) 2.5-5.0; and (viii) < or > 5.0. It has been identified that bins having widths equivalent to widths in the range 0.01-1 Da or Th (Da/e) can provide particularly useful sample spectra for classifying some aerosol, smoke or vapour samples, such as samples obtained from tissues. The bins may or may not all have the same width. The widths of the bin in the binning process may vary according to a bin width function. The bin width function may vary with a time or time-based value, such as mass, mass to charge ratio and/or ion mobility. The bin width function may be non-linear (e.g., logarithmic-based or power-based, such as square or square-root based). The bin width function may take into account the fact that the time of flight of an ion may not be directly proportional to its mass, mass to charge ratio, and/or ion mobility. For example, the time of flight of an ion may be directly proportional to the square-root of its mass and/or mass to charge ratio.

Spectrometric library The terms "spectrometric library" and "spectrometric database" are used interchangeably herein.

The skilled person may use any publicly available spectrometric data as reference spectrometric data. Examples of useful databases are: LipidMaps, LipidBlast and LipidXplorer, details of which are provided in the following publications: "LipidBlast -in-silico tandem mass spectrometry database for lipid identification" by Kind et al., Nat Methods. 2013 August; 10(8): 755-758; "LipidXplorer: A Software for Consensual Cross-Platform Lipidomics" by Herzog et al. PLoS ONE 7(1): e29851; and "Lipid classification, structures and tools" by Fahy et al. Biochimica et Biophysica Acta (BBA) -Molecular and Cell Biology of Lipids, Volume 1811, Issue 11, November 2011, Pages 637-647, Lipidomics and Imaging Mass Spectrometry, see also http://www.lipidmaps.org/.

Alternatively or in addition, the skilled person may construct a spectrometric library by obtaining spectrometric data from one or more samples, which may optionally, in the case of microbes, include type culture strains and/or clinical and/or environmental microbial isolates; in the case of cells or tissues, the sample(s) may optionally include a cell line, cell culture, tissue sample and the like; in the case of compound, the sample(s) may optionally be purchased or synthesised.

Type culture strains and cell lines may optionally be obtained from culture collections, such as, the American Type Culture Collection (ATCC) (10801 University Boulevard, Manassas, 25 VA 20110 USA).

The present inventors generated a spectrometric library using over 1500 microbial strains, including clinical isolates and type culture strains from the ATCC, encompassing about 95 genera and about 260 species of bacteria and fungi. To expedite the generation of the spectrometric library, the inventors set up high throughput culturing, automated colony imaging, colony picking and REIMS analysis.

The present inventors have also generated spectrometric libraries using tissues and/or cell lines, details of which are provided elsewhere herein, including in the Examples.

The generation of a spectrometric library from microbes, cell lines and/or tissues may optionally be combined with a further analysis, e.g., taxonomic classification and/or histology, e.g., based on any of the further analytical tools discussed elsewhere herein. For example, the tool may be DNA analysis. This may involve DNA sequencing, optionally preceded by DNA isolation and/or amplification using, e.g., PCR. For bacteria, sequencing of all or part of the 16S rRNA gene is particularly suitable, whereas for fungi, sequencing of all or part of the internal transcribed spacer (ITS) region is particularly suitable.

Analysing sample spectra The step of analysing the spectrometric data may comprise analysing one or more sample spectra so as to classify an aerosol, smoke or vapour sample.

Analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample may comprise unsupervised analysis of the one or more sample spectra (e.g., for dimensionality reduction) and/or supervised analysis of the one or more sample spectra (e.g., for classification).

Analysing the one or more sample spectra may comprise unsupervised analysis (e.g., for dimensionality reduction) followed by supervised analysis (e.g., for classification).

Analysing the one or more sample spectra may be performed as discussed elsewhere herein.

A list of analysis techniques which are intended to fall within the scope of the present invention are given in the following table: Analysis Techniques Univariate Analysis Multivariate Analysis Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Maximum Margin Criteria (MMC) Library Based Analysis Soft Independent Modelling Of Class Analogy (SIMCA) Factor Analysis (FA) Recursive Partitioning (Decision Trees) Random Forests Independent Component Analysis (ICA) Partial Least Squares Discriminant Analysis (PLS-DA) Orthogonal (Partial Least Squares) Projections To Latent Structures (OPLS) OPLS Discriminant Analysis (OPLS-DA) Support Vector Machines (SVM) (Artificial) Neural Networks Multilayer Perceptron Radial Basis Function (RBF) Networks Bayesian Analysis Cluster Analysis Kernelized Methods Subspace Discriminant Analysis K-Nearest Neighbours (KNN) Quadratic Discriminant Analysis (QDA) Probabilistic Principal Component Analysis (PPCA) Non negative matrix factorisation K-means factorisation Fuzzy c-means factorisation Discriminant Analysis (DA) Combinations of the foregoing analysis approaches can also be used, such as PCALDA, PCA-MMC, PLS-LDA, etc. Analysing the sample spectra can comprise unsupervised analysis for dimensionality reduction followed by supervised analysis for classification.

By way of example, a number of different analysis techniques will now be described in more detail.

Multivariate analysis -Developing a Model for Classification By way of example, a method of building a classification model using multivariate analysis of plural reference sample spectra will now be described.

Figure 40 shows a method 1500 of building a classification model using multivariate analysis. In this example, the method comprises a step 1502 of obtaining plural sets of intensity values for reference sample spectra. The method then comprises a step 1504 of unsupervised principal component analysis (PCA) followed by a step 1506 of supervised linear discriminant analysis (LDA). This approach may be referred to herein as PCA-LDA. Other multivariate analysis approaches may be used, such as PCA-MMC. The PCA-LDA model is then output, for example to storage, in step 1508.

The multivariate analysis such as this can provide a classification model that allows an aerosol, smoke or vapour sample to be classified using one or more sample spectra obtained from the aerosol, smoke or vapour sample. The multivariate analysis will now be described in more detail with reference to a simple example.

Figure 41 shows a set of reference sample spectra obtained from two classes of known reference samples. The classes may be any one or more of the classes of target described herein. However, for simplicity, in this example the two classes will be referred as a left-hand class and a right-hand class.

Each of the reference sample spectra has been pre-processed in order to derive a set of three reference peak-intensity values for respective mass to charge ratios in that reference sample spectrum. Although only three reference peak-intensity values are shown, it will be appreciated that many more reference peak-intensity values (e.g., -100 reference peak-intensity values) may be derived for a corresponding number of mass to charge ratios in each of the reference sample spectra. In other embodiments, the reference peak-intensity values may correspond to: masses; mass to charge ratios: ion mobilities (drift times); and/or operational parameters.

Figure 42 shows a multivariate space having three dimensions defined by intensity axes.

Each of the dimensions or intensity axes corresponds to the peak-intensity at a particular mass to charge ratio. Again, it will be appreciated that there may be many more dimensions or intensity axes (e.g., -100 dimensions or intensity axes) in the multivariate space. The multivariate space comprises plural reference points, with each reference point corresponding to a reference sample spectrum, i.e., the peak-intensity values of each reference sample spectrum provide the co-ordinates for the reference points in the multivariate space.

The set of reference sample spectra may be represented by a reference matrix D having rows associated with respective reference sample spectra, columns associated with respective mass to charge ratios, and the elements of the matrix being the peak-intensity values for the respective mass to charge ratios of the respective reference sample spectra.

In many cases, the large number of dimensions in the multivariate space and matrix D can make it difficult to group the reference sample spectra into classes. RCA may accordingly be carried out on the matrix D in order to calculate a PCA model that defines a RCA space having a reduced number of one or more dimensions defined by principal component axes. The principal components may be selected to be those that comprise or "explain' the largest variance in the matrix D and that cumulatively explain a threshold amount of the variance in the matrix D. Figure 43 shows how the cumulative variance may increase as a function of the number n of principal components in the PCA model. The threshold amount of the variance may be selected as desired.

The PCA model may be calculated from the matrix D using a non-linear iterative partial least squares (NIPALS) algorithm or singular value decomposition, the details of which are known to the skilled person and so will not be described herein in detail. Other methods of calculating the RCA model may be used.

The resultant PCA model may be defined by a PCA scores matrix S and a RCA loadings matrix L. The RCA may also produce an error matrix E, which contains the variance not explained by the PCA model. The relationship between D, S, L and E may be: D = SLT + E (1) Figure 44 shows the resultant PCA space for the reference sample spectra of Figs. 41 and 42. In this example, the PCA model has two principal components PC0 and PC, and the PCA space therefore has two dimensions defined by two principal component axes. However, a lesser or greater number of principal components may be included in the RCA model as desired. It is generally desired that the number of principal components is at least one less than the number of dimensions in the multivariate space.

The RCA space comprises plural transformed reference points or PCA scores, with each 35 transformed reference point or PCA score corresponding to a reference sample spectrum of Figure 41 and therefore to a reference point of Figure 42.

As is shown in Figure 44, the reduced dimensionality of the RCA space makes it easier to group the reference sample spectra into the two classes. Any outliers may also be identified and removed from the classification model at this stage.

Further supervised multivariate analysis, such as multi-class LDA or maximum margin criteria (MMC), in the RCA space may then be performed so as to define classes and, optionally, further reduce the dimensionality.

As will be appreciated by the skilled person, multi-class LDA seeks to maximise the ratio of the variance between classes to the variance within classes (i.e., so as to give the largest possible distance between the most compact classes possible). The details of LDA are known to the skilled person and so will not be described herein in detail.

The resultant PCA-LDA model may be defined by a transformation matrix U, which may be derived from the RCA scores matrix S and class assignments for each of the transformed spectra contained therein by solving a generalised eigenvalue problem.

The transformation of the scores S from the original RCA space into the new LDA space may then be given by: Z =SU (2) where the matrix Z contains the scores transformed into the LDA space.

Figure 45 shows a PCA-LDA space having a single dimension or axis, wherein the LDA is performed in the RCA space of Figure 44. As is shown in Figure 45, the LDA space comprises plural further transformed reference points or PCA-LDA scores, with each further transformed reference point corresponding to a transformed reference point or PCA score of Figure 44.

In this example, the further reduced dimensionality of the PCA-LDA space makes it even easier to group the reference sample spectra into the two classes. Each class in the PCA-LDA model may be defined by its transformed class average and covariance matrix or one or more hyperplanes (including points, lines, planes or higher order hyperplanes) or hypersurfaces or Voronoi cells in the PCA-LDA space.

The PCA loadings matrix L, the LDA matrix U and transformed class averages and covariance matrices or hyperplanes or hypersurfaces or Voronoi cells may be output to a database for later use in classifying an aerosol, smoke or vapour sample.

The transformed covariance matrix in the LDA space Vg for class g may be given by Vo=U Vo U where V, are the class covariance matrices in the RCA space.

The transformed class average position zo for class g may be given by s9U = zo where so is the class average position in the PCA space.

Multivariate Analysis -Using a Model for Classification By way of example, a method of using a classification model to classify an aerosol, smoke or vapour sample will now be described. (3) (4)

Figure 46 shows a method 2100 of using a classification model. In this example, the method comprises a step 2102 of obtaining a set of intensity values for a sample spectrum. The method then comprises a step 2104 of projecting the set of intensity values for the sample spectrum into PCA-LDA model space. Other classification model spaces may be used, such as PCA-MMC. The sample spectrum is then classified at step 2106 based on the project position and the classification is then output in step 2108.

Classification of an aerosol, smoke or vapour sample will now be described in more detail with reference to the simple PCA-LDA model described above.

Figure 47 shows a sample spectrum obtained from an unknown aerosol, smoke or vapour sample. The sample spectrum has been pre-processed in order to derive a set of three sample peak-intensity values for respective mass to charge ratios. As mentioned above, although only three sample peak-intensity values are shown, it will be appreciated that many more sample peak-intensity values (e.g., -100 sample peak-intensity values) may be derived at many more corresponding mass to charge ratios for the sample spectrum. Also, as mentioned above, in other embodiments, the sample peak-intensity values may correspond to: masses; mass to charge ratios; ion mobilities (drift times); and/or operational parameters.

The sample spectrum may be represented by a sample vector cl., with the elements of the vector being the peak-intensity values for the respective mass to charge ratios. A transformed PCA vector sx for the sample spectrum can be obtained as follows: dxL = sx (5) Then, a transformed PCA-LDA vector zx for the sample spectrum can be obtained as follows: s,U = zx (6) Figure 48 again shows the PCA-LDA space of Figure 45. However, the PCA-LDA space of Figure 48 further comprises the projected sample point, corresponding to the transformed PCA-LDA vector z,, derived from the peak intensity values of the sample spectrum of Figure 47. In this example, the projected sample point is to one side of a hyperplane between the classes that relates to the right-hand class, and so the aerosol, smoke or vapour sample may be classified as belonging to the right-hand class.

Alternatively, the Mahalanobis distance from the class centres in the LDA space may be used, where the Mahalanobis distance of the point zx from the centre of class g may be given by the square root of: (z.-zg)T (V9)1(zx-zg) (8) and the data vector d, may be assigned to the class for which this distance is smallest.

In addition, treating each class as a multivariate Gaussian, a probability of membership of the data vector to each class may be calculated.

Library Based Analysis -Developing a Library for Classification By way of example, a method of building a classification library using plural input reference sample spectra will now be described.

Figure 49 shows a method 2400 of building a classification library. In this example, the method comprises a step 2402 of obtaining plural input reference sample spectra and a step 2404 of deriving metadata from the plural input reference sample spectra for each class of sample. The method then comprises a step 2406 of storing the metadata for each class of sample as a separate library entry. The classification library is then output, for example to electronic storage, in step 2408.

A classification library such as this allows an aerosol, smoke or vapour sample to be classified using one or more sample spectra obtained from the aerosol, smoke or vapour sample. The library based analysis will now be described in more detail with reference to an example.

In this example, each entry in the classification library is created from plural pre-processed reference sample spectra that are representative of a class. In this example, the reference sample spectra for a class are pre-processed according to the following procedure: First, a re-binning process is performed. In this embodiment, the data are resampled onto a logarithmic grid with abscissae: / Xi,,,---[Ncha"log log Mmax M771111 where Ncii"ii is a selected value and [x] denotes the nearest integer below x. In one example, Nch," is 212 or 4096.

Then, a background subtraction process is performed. In this embodiment, a cubic spline with k knots is then constructed such that p% of the data between each pair of knots lies below the curve. This curve is then subtracted from the data. In one example, k is 32. In one example, p is 5. A constant value corresponding to the q% quantile of the intensity subtracted data is then subtracted from each intensity. Positive and negative values are retained. In one example, q is 45.

Then, a normalisation process is performed. In this embodiment, the data are normalised to have mean yi. In one example, yi = 1.

An entry in the library then consists of metadata in the form of a median spectrum value Ai and a deviation value Di for each of the Nch" points in the spectrum.

The likelihood for the i'th channel is given by: 1 Cc-1/2 (C) 1 PrO7 De) 1/2) / _ /232\ IN, = D1 V (C 7C1 -Di) where 1/2 C < co and where F(C) is the gamma function.

The above equation is a generalised Cauchy distribution which reduces to a standard Cauchy distribution for C = 1 and becomes a Gaussian (normal) distribution as C -3. The parameter Di controls the width of the distribution (in the Gaussian limit Di = a, is simply the standard deviation) while the global value C controls the size of the tails.

In one example, C is 3/2, which lies between Cauchy and Gaussian, so that the likelihood becomes: Pr(37,1 Di 4 D, (3/2 + _ ) 2 /0,2)372 For each library entry, the parameters are set to the median of the list of values in the i'th channel of the input reference sample spectra while the deviation Di is taken to be the interquartile range of these values divided by ^;2. This choice can ensure that the likelihood for the i'th channel has the same interquartile range as the input data, with the use of quantiles providing some protection against outlying data.

Library-Based Analysis -Using a Library for Classification By way of example, a method of using a classification library to classify an aerosol, smoke or vapour sample will now be described.

Figure 50 shows a method 2500 of using a classification library. In this example, the method comprises a step 2502 of obtaining a set of plural sample spectra. The method then comprises a step 2504 of calculating a probability or classification score for the set of plural sample spectra for each class of sample using metadata for the class entry in the classification library. The sample spectra are then classified at step 2506 and the classification is then output in step 2508.

Classification of an aerosol, smoke or vapour sample will now be described in more detail with reference to the classification library described above.

In this example, an unknown sample spectrum y is the median spectrum of a set of plural sample spectra. Taking the median spectrum y can protect against outlying data on a channel by channel basis.

The likelihood Ls for the input data given the library entry s is then given by: 1,, = Pr (ylit, D) = i.Di) where pi and Di are, respectively, the library median values and deviation values for channel i. The likelihoods Ls may be calculated as log likelihoods for numerical safety.

The likelihoods Ls are then normalised over all candidate classes 's' to give probabilities, assuming a uniform prior probability over the classes. The resulting probability for the class g is given by: Pi-(c' I y)L LW/ ) The exponent (1./F) can soften the probabilities which may otherwise be too definitive. In one example, h' = 100. These probabilities may be expressed as percentages, e.g., in a user interface.

Alternatively, RMS classification scores Rs may be calculated using the same median sample values and derivation values from the library: 1 Yt -102 Rs (y, D) - Dz Pichan Again, the scores Rs are normalised over all candidate classes '5'.

The aerosol, smoke or vapour sample may then be classified as belonging to the class having the highest probability and/or highest RMS classification score.

Further analytical tools Any of the methods of the invention may optionally include a step of using one or more additional analytical tools. Such a tool may, for example, be selected from microscopic examination; nucleic acid analysis, for example, using restriction enzymes, hybridisation, polymerase chain reaction (PCR) amplification and/or sequencing; and/or testing for antigens.

Such tools are well known in the art, but brief details are provided below.

The specimen may be examined visually, without any additional aids, such as, a microscope.

Microscopic examination may, for example, optionally be light microscopy and/or electron microscopy.

Nucleic acid analysis may optionally involve isolation and purification of DNA and/or

RNA

Nucleic acid analysis via PCR amplification may, for example, optionally involve amplification of all or part of a suitable gene. For example, in the case of a microbe, the gene may be the bacterial 16S rRNA gene, and universal and/or species-specific primers may be used. Other examples of suitable microbial genes which may optionally be analysed Lyn-) alternatively or in addition include, for example, microbial species-specific genes or virulence genes, for example, Shiga toxin (stx), intimin (eae). flagellar H-antigen genes fliC-fliA, hsp65, rpoB and/or recA. For fungi, PCR amplification of all or part of the internal transcribed spacer (ITS) is particularly suitable. When analysing human or animal cells, PCR may, e.g., be used to amplify a disease-specific and/or a tissue-specific gene.

Optionally, the PCR may be Real-time PCR or quantitative PCR. Optionally, Reversetranscriptase polymerase chain reaction (RT-PCR) may be used to analyse RNA expression.

Nucleic acid analysis with restriction enzymes may, for example, optionally involve restriction-fragment length polymorphism (RFLP) analysis. RFLP, is a technique that exploits variations in the length of homologous DNA sequences. RFLP analysis may involve a restriction digest, i.e. incubating a DNA with a suitable restriction enzyme such as BamHI, Hindlll or EcoRl. Each restriction enzyme can recognise and cut a specific short nucleic acid sequence. The resulting DNA fragments may then be separated by length, for example, through agarose gel electrophoresis. The DNA fragments in the gel may optionally be stained, for example, with ethidium bromide, and the pattern of the fragments of different length may be determined.

Optionally, the DNA fragment may be transferred to a membrane via the Southern blot procedure. The membrane may then be exposed to a labelled DNA probe to allow hybrisidation to occur. The label may, for example, be or comprise a radioactive isotope or digoxigenin (DIG). Any unhybridised probe may then be washed off. The label may then be detected and the pattern of the fragments which have hybridised to the labelled probe may be determined.

Sequencing may, for example; optionally involve the dideoxy or chain termination method. In this method, the DNA may be used as a template to generate a set of fragments that differ in length from each other by a single base. The fragments may then be separated by size, and the bases at the end may be identified, recreating the original sequence of the DNA.

Hybridisation analysis may, for example, optionally include DNA-DNA hybridization of one or more selected DNA fragments, genes or whole genomic DNA from a first cell or microbe to a labelled DNA probe to determine the genetic similarity between the first cell or microbe and the known or comparator cell or microbe. Hybridisation analysis may, for example, involve transfer of the DNA to a membrane via the Southern blot procedure, labelling and detection as described above.

Nucleic acid analysis may optionally involve e.g., denaturing gradient gel electrophoresis (DGGE) and/or temperature gradient gel electrophoresis (TGGE).

Fatty acid profiling of cells or microbes may, for example, optionally be carried out using gas-chromatography coupled to a flame ionisation detector (GC-FID), or high performance liquid chromatography (H PLC).

With respect to microbial colony morphology, one or more of the following may, for example, optionally be examined: size; whole colony shape, which may, for example, be circular, irregular, or rhizoid; colony edge, which may, for example, be smooth, filamentous, or undulating; elevation, which may, for example, be flat, raised, convex or crateriform; surface, which may, for example, be wrinkled, rough, waxy, or glistening; opacity, which may, for example, be transparent, translucent, or opaque; pigmentation; colour, which may, for example, be red, yellow, or white; and/or water solubility.

With respect to the morphology of individual microbes, this may, for example, optionally be determined to be a coccus (spherical), bacillus (rod-shaped), spiral (twisted), or pleomorphic. Cocci may optionally be a single coccus, diplococcic, streptococci, tetrads, sarcinae or staphylococci. Bacilli may optionally be a single bacillus, diplobacilli, streptobacilli or coccobacilli. Spirals may optionally be vibrio, spirilla or Spirochetes.

With respect to the morphology of mammalian cells, this may, for example, optionally be determined to be fibroblastic, epithelial-like, lymphoblast-like, and/or neuronal, with or without an axon.

Culture-based screening for nutrient requirements may optionally involve inoculating cells or microbes onto on into one or more different growth media, such as different selective media, and observing in/on which media cell or microbial growth occurs, and to what extent the growth differs between different media.

Culture-based screening for antimicrobial sensitivity may optionally involve inoculating microbes onto one or more different growth media, which may be done, for example, by streaking or plating the microbes onto a petri dish containing a suitable nutrient agar. An antimicrobial agent may then be added, which may be done, for example, by placing a filter paper disk impregnated with the antimicrobial onto the growth medium. Several disks each containing a different antimicrobial agent may be added onto a single petri dish. A determination may then be made as to whether a zone of growth inhibition occurs around any of the disk(s), and, if so, how large this zone is.

Immunohistochemical analysis may involve contacting the tissue sample with one or more labelled agents, such as antibodies. Thus, the presence of specific antigens, particularly on the cell surface of a cell or microbe, may optionally be tested for by using specific antibodies. Testing for antigens may also be referred to as serotyping. The antibodies may be polyclonal or monoclonal. If the antibodies are specific for a particular cell type, then the number of cells of that type may be assessed. The test may optionally involve simply detecting the presence or absence of agglutination, i.e. the formation of complexes of cells/microbes and antibodies. Alternatively or in addition, the antibodies may be labelled and the assay may involve, for example, an enzyme-linked immunosorbent assay ("ELISA") and/or fluorescence activated cell sorting ("FACS").

The antibody may optionally be selected from e.g., a CD3 or CD8 antibody.

Flow cytometry may optionally be used to analyse the properties of cells or microbes in a sample or specimen, e.g., the number of cells/microbes, percentage of live cells/microbes, cell/microbe size, cell/microbe shape, and/or the presence of particular antigens on the cell/microbe surface.

Western blot hybridization may optionally be used to analyse proteins and/or peptides.

Optionally, in situ hybridization of labelled probes to tissues, microbes and/or cells may be performed, optionally using an array format. The method may be Fluorescence in situ hybridization (FISH), which may, e.g., be used to analyse chromosomal abnormalities and/or to map genes.

Examples

Example 1 -DESI-MS analysis of human breast cancer biopsies Manual histological evaluation of the stained biopsy tissue sections has been the gold standard method when it comes to providing a diagnosis for breast cancers. However, the accuracy of this morphology-based tissue diagnosis is often compromised as it is dependent on the pathologists' interpretation, resulting in poor prognosis for a given subject.

DESI-MSI enables the skilled person to visualise spatial distribution of lipid species across tissue sections allowing direct correlation with the histological features. Therefore, breast cancer tissues were analysed with DESI-MSI to obtain lipidomic data. About 45 samples, including Grade II invasive ductal carcinoma (IDC), have been analysed in positive and negative ion mode.

Each individual breast sample was subjected to unsupervised principal component analysis (PCA) to visualize differences between different tissue types (data is in colour and therefore not shown). In both positive and negative ion mode, a clear distinction between the stroma and the tumour tissue was observed in almost all of the samples (Fig. 5a & 6a).

Recursive maximum margin criterion (RMMC) analysis was used for supervised classification (Fig. 5b & 6b). Tissue types in each sample and their spatial distribution were determined by an independent histopathologist based on the H&E stained optical image. Based on this information, a small number of representative mass spectra per tissue were selected from the integrated MS ion image to build a sample-specific RMMC model which was used to classify all pixels in the different tissue types. This data was submitted to cross validation, which exceeded 95% accuracy generally for all tissue types in all samples in both negative and positive ion mode (Fig. 7 a&b).

The method distinguished between the following tissue specimens: malignant tissue, tumour section fibrous section, tumour section adipose tissue, tumour section glandular tissue and necrotic tissue. Thus, the method may optionally be used to analyse, e.g. identify or distinguish between one or more tissue types, optionally selected from, e.g., malignant tissue, fibrous tumour tissue, adipose tumour tissue, glandular tumour tissue, and/or necrotic tissue.

Example 2 Development of spatially -resolved shotgun lipidomic methods for histology-level cancer diagnostics An ovarian cancer dataset with different epithelial carcinomas (endometrioid, serous and clear cell carcinomas), borderline tumours, and healthy ovary and fallopian tube has been analysed. A total of 109 human samples were collected and mass spectrometry data was acquired by DESI-MS in positive and negative ion mode.

The dataset was initially pre-processed and multivariate statistical analysis was performed on each individual sample's dataset in order to compile a database of histologically authentic lipidomic profiles. The morphological regions of interest were assigned by a qualified histopathologist and automatically co-registered and aligned with the mass spectrometry imaging (MSI) dataset.

Using principal component analysis (PCA), it was observed that different tissue types within the same sample show different lipid profiles. For example, normal ovary contains corpus and stroma tissue, and these are completely separated in PCA. In the borderline and cancer samples, one can also distinguish 2 different tissue types, the tumour cells and the surrounding stroma cells presenting large differences in their lipidomic profile. When supervised maximum margin criteria (MMC) analysis and colour map according to the MMC components is applied, it is possible to produce tissue maps that reflect the different tissue types identified in the histological image.

This profile database was also used to perform comparative analysis across multiple samples. RCA was used to perform unsupervised tissue segmentation based on the lipidomic profiles, without taking into account histological assignment. A supervised analysis was then performed and a respective leave-one-tissue-per-subject-out cross validation was calculated.

PCA shows some separation between normal ovary, serous carcinoma, and serous carcinoma associated stroma (Fig. 8). The supervised MMC analysis shows good separation between all three tissue types, with six outliers. Interestingly, all four misclassified normal samples were samples which were classified as normal ovary but were taken from an ovary with a tumour distant from the sampling area. This suggests that the biochemistry of this tissue is altered, even though this cannot be detected in a morphological examination. MMC analysis was repeated under exclusion of the outliers and leave-one-region-per-subject-out cross validation was performed, showing a complete separation of normal tissue and an overall accuracy of 85%.

The variances between different types of samples was also examined. For example, it was evaluated how well negative ion mode DESI-MSI can separate cancer tissues, borderline and healthy ovary (Fig. 9). An overall classification accuracy of 95.6% was achieved.

A further analysis performed was the comparison between different types of epithelial carcinomas in the dataset: endometrioid and serous carcinomas. Using the negative ion mode data, healthy ovary, serous carcinoma, and endometrioid carcinoma could be classified with an overall accuracy of 90% (see Fig. 10).

It was also examined whether, based on the models created, it was possible to predict the different tissue types of a blind sample. The number of serous carcinomas analysed provided a robust model to perform this validation using negative ion mode data (See Fig 11).

The DESI data allowed an excellent prediction of the two tissue types present in the sample, i.e. ovarian stroma and ovarian cancer. Serous carcinoma, serous carcinoma associated stroma, normal ovarian stroma and background were differentiated. A cross validation was performed based on histological annotation performed after this analysis and a classification accuracy of almost 100% was achieved.

Example 3 -Breast cancer diagnosis ex vivo using REIMS technology About 227 samples from tumour, normal and fibroadenoma human tissue were obtained and analysed. The distribution of the samples is shown in Table 3.1. The samples were histologically validated.

Table 3.1

Sample type Number of subjects R9 Normal 120 Tumour 73 Fibroadenoma 34 Sampling took place with either diathermy or plasmablade taking measurements in separate files using cut or coagulation modes if the amount of tissue allowed. Diathermy cut mode was the preferred method if the tissue collected was small. Regardless of whether diathermy or plasmablade were used, and regardless of whether cut or coagulation mode were used, each sample was correctly identified as being normal, tumour or fibroadenoma (for an extract of the data, see Table 3.2).

Table 3.2

IKB349 IKB349 20150713 DOLORES FRESH NORMAL CUT.raw IKB349 20150713 DOLORES FRESH NORMAL COAG.raw Normal IKB349 20150713 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB34920150713_DOLORES _FRESH_NORMAL_PLASMABLADE_COAG.raw IKB352 IKB35220150717_DOLORESFRESH_NORMALCUtraw Normal IKB352_20150717_DOLORES_FRESH_NORMAL_COAG.raw IKB352 20150717 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB35220150717_DOLORES_FRESH_NORMAL_PLASMABLADE_COAG.raw IKB353 IKB353 20150717 DOLORES FRESH NORMAL CUT.raw IKB353 20150717 DOLORES FRESH NORMAL COAG.raw Normal IKB35320150717DOLORESFRESHNORMALPLASMABLADECUT.raw IKB353_20150717_DOLORES_FRESH_NORMAL_PLASMABLADE_COAG.raw IKB353_ 20150717_ DO LOR ES_F R ESH_TU M O U R_CUT.raw Tumour IKB353_20150717_DOLORES_FRESH_TUMOUR_COAG.raw IKB353_20150717_DOLORES_FRESH_TUMOUR_PLASMABLADE_CUT.raw IKB357 IKB353 20150717 DOLORES FRESH TUMOUR PLASMABLADE COAG.raw IKB357 20150721 DOLORES FRESH NORMAL CUT.raw Normal IKB357 20150721 DOLORES FRESH NORMAL raw IKB35720150721_DOLORES_FRESH_NORMAL_PLASMABLADE_CUT.raw IKB357_20150721_DOLORES_FRESH_NORMAL_PLASMABLADLCOAG.raw IKB357_20150721_DOLORES_FRESH_TUMOUR_CUTraw Tumour IKB357_20150721_DOLORES_FRESH_TUMOUR_COAG.raw IKB357_20150721_DOLORES_FRESH_TUMOUR_PLASMABLADE_CUT.raw IKB362 IKB357 20150721 DOLORES FRESH TUMOUR PLASMABLADE COAG. raw IKB362 20150724 DOLORES FRESH NORMAL CUT.raw Normal IKB36220150724_DOLORES_FRESH_NORMAL_COAG.raw IKB36220150724__DOLORESFRESH_NORMAL_CUL2.raw IKB362_20150724_DOLORES_FRESH_NORMAL_PLASMABLADE_CUT.raw IKB362_20150724_DOLORES_FRESH_NORMAL_PLASMABLADE_COAG.raw IKB362 20150724 DOLORES FRESH TUMOUR CUT.raw Tumour IKB362_20150724_DOLORES_FRESH_TUMOUR_COAG.raw IKB363 IKB362 20150724 DOLORES FRESH TUMOUR PLASMABLADE CUT. raw Normal IKB362_20150724_DOLORES_FRESH_TUMOUR PLASMABLADE_COAG.raw 10363..20150727..DOLORES...FRESH_NORMAL...CUT.raw IKB363 20150727 DOLORES FRESH NORMAL COAG.raw IKB363 20150727 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB363 20150727 DOLORES FRESH NORMAL PLASMABLADE COAG raw IKB363 20150727 DOLORES FRESH TUMOUR CUT.raw Tumour IKB36320150727_DOLORES_FRESH_TUMOUR_COAG.raw IKB367 IKB363_20150727_DOLORES_FRESH_TUMOUR_PLASMABLADE_CUT.raw IKB363_20150727_DOLORES_FRESH_TUMOUR_PLASMABLADE_COAG.raw Normal IKB367...20150730...DOLORES_FRESH NORMAL. CUT.raw IKB367 20150730 DOLORES FRESH NORMAL COAG.raw IKB367 20150730 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB367 20150730 DOLORES FRESH NORMAL PLASMABLADE COAG.raw IKB367_20150730_DOLORES_FRESH_TUMOUR_CUT.raw Tumour IKB367 20150730 DOLORES FRESH TUMOUR COAG.raw IKB367 20150730 DOLORES FRESH TUMOUR PLASMABLADE CUT.raw IKB367..20150730..DOLORES...FRESH TUMOUR...PLASMABLADE...COAG.raw IKB369 IKB369_20150730_DOLORES_FRESH_NORMALCUtraw Normal IKB369 20150730 DOLORES FRESH NORMAL COAG.raw IKB369 20150730 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB369 20150730 DOLORES FRESH NORMAL PLASMABLADE COAG.raw IKB369 20150730 DOLORES FRESH TUMOUR CUT.raw Tumour IKB369_20150730_DOLORES_FRESH_TUMOUR_COAG.raw IKB369 20150730 DOLORES FRESH TUMOUR PLASMABLADE CUT. raw IKB369...20150730...DOLORES.. FRESH TUMOUR.. PLASMABLADE.. COAG.raw IKB370 IKB370 20150731 DOLORES FRESH FIBROADENOMA CUT.raw Fibroadenoma IKB370 20150731 DOLORES FRESH FIBROADENOMA COAG.raw IKB37020150731DOLORESFRESHFIBROADENOMAPLASMABLADECUT.raw IKB370_20150731_DOLORES_FRESH_FIBROADENOMA_PLASMABLADE_COAG.raw IKB371 IKB371 20150803 DOLORES FRESH FIBROADENOMA CUT.raw Fibroadenoma IKB37120150803DOLORESFRESHFIBROADENOMACOACiraw IKB371 20150803 DOLORES FRESH FIBROADENOMA PLASMABLADE CUT.raw IKB371.20150803..DOLORES...FRESH_FIBROADENOMA..PLASMABLADE_COAG.raw IKB373 IKB373 20150803 DOLORES FRESH NORMAL CUT.raw Normal IKB373 20150803 DOLORES FRESH NORMAL COAG.raw IKB373 20150803 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB373 20150803 DOLORES FRESH NORMAL PLASMABLADE COAG.raw IKB373 20150803 DOLORES FRESH TUMOUR CUT.raw IKB373 20150803 DOLORES FRESH TUMOUR COAG.raw Tumour I 20150803 DOLORES FRESH TUMOUR PLASMABLADE CUT.raw IKB373_20150803_DOLORES_FRESH_TUMOUR_PLASMABLADE_COAG.raw IKB374 IKB374 20150803 DOLORES FRESH FIBROADENOMA CUT.raw IKB374_20150803_DOLORES_FRESH_FIBROADENOMA_COAG.raw IKB374...20150803..DOLORES...FRESH JIBROADENOMA..PLASMABLADE...CUT.raw Fibroadenoma IKB374 20150803 DOLORES FRESH FIBROADENOMA PLASMABLADE COAG.raw IKB367 IKB367 20150807 DOLORES FRESH FIBROADENOMA CUT.raw Fibroadenoma IKB367 20150807 DOLORES FRESH FIBROADENOMA COAG.raw IKB367 20150807 DOLORES FRESH FIBROADENOMA PLASMABLADE CUT.raw IKB367_20150807_DOLORES_FRESH_FIBROADENOMA_PLASMABLADE_CUT_02.raw IKB377 IKB367_20150807_DOLORES_FRESH_FIBROADENOMA_PLASMABLADE_COAG.raw IKB37720150810DOLORESFRESHFIBROADENOMACUT.raw IKB377...20150810...DOLORES_FRESH...FIBROADENOMA...COAG.raw Fibroadenoma IKB377 20150810 DOLORES FRESH FIBROADENOMA PLASMABLADE CUT.raw IKB377 20150810 DOLORES FRESH FIBROADENOMA PLASMABLADE COAG.raw IKB378 IKB378 20150810 DOLORES FRESH NORMAL CUT.raw Normal 1037820150810DOLORESFRESHNORMALCOAGsa IKB281 IKB378 20150810 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB378 20150810 DOLORES FRESH NORMAL PLASMABLADE COAG.raw IK13281...20150813..DOLORES...FRESH...NORMAL...CUT.raw Normal IKB281_20150813_DOLORES_FRESH_NORMAL_COAGsaw IKB281 20150813 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB281 20150813 DOLORES FRESH NORMAL PLASM ABLADE COAG.raw IKB281 20150813 DOLORES FRESH TUMOUR CUT.raw Tumour IKB281 20150813 DOLORES FRESH TUMOUR COAG.raw IKB382 IKB281_20150813_DOLORES_FRESH_TUMOUR_PLASMABLADE_CUT.raw IK8281 20150813 DOLORES FRESH TUMOUR PLASMABLADE COAG.raw IK13382...20150817...DOLORES_FRESH...NORMALCUT.raw Normal IKB3 2 20150817 DOLORES FRESH NOR AL COAG.raw IKB382 20150817 DOLORES FRESH NORMAL PLASMABLADE CUT.raw IKB38220150817_DOLORES_FRESH_NORMAL_PLASMABLADE_COAG.raw 10382_20150817_DOLORES_FRESHTUMOUR_CUT.raw Tumour IKB382 20150817 DOLORES FRESH TUMOUR COAG.raw IK838220150817DOLOBESFRESH I UM OURPLASMABLADECU[saw IKB382 20150817 DOLORES FRESH TUMOUR PLASMABLADE COAG. raw IKB3 2.20150817..DOLORES...FRESH...NORMAL TO TUMOUR JEST...CUT.raw Margin test IKB382 20150817 DOLORES FRESH NORMAL T TUMOUR TEST COAG.raw IKB382 20150817 DOLORES FRESH NORMAL TO TUMOUR TEST PLASMABLADE C UT. raw IKB382_20150817_DOLORES_FRESH_NORMALTO_TUMOUR_TEST_PLASMABLADE_C OAG.raw IKB382_20150817_DOLORES_FRESH_RULER_MARGIN_TEST_CUT.raw Margin test IKB382 20150817 DOLORES FRESH RULER MARGIN TEST COAG.raw IKB391 IKB39120150820DOLORESFRESHFIBROADENOMACUT.raw Fibroadenoma I 20150820 DOLORES FRESH FIBROADENOMA COAG.raw IK8391_20150820_DOLORES_FRESH_FIBROADENOMA_PLASMABLADE_CUT.raw 10391_20150820_ DOLORES...FRESH..,FIBROADENOMA..PLASMABLADE_COAG.raw IKB396 IKB396 20150824 DOLORES FRESH FIBROADENOMA CUT.raw Fibroadenoma IKB396 20150824 DOLORES FRESH FIBROADENOMA COAG.raw IKB396 20150824 DOLORES FRESH FIBROADENOMA PLASMABLADE CUT.raw IKB396 20150824 DOLORES FRESH FIBROADENOMA PLASMABLADE COAG.raw Principal component analysis and linear discriminant analysis with cross validation have been done separately for samples run in cut and coagulation modes. See Figures 12 and 13.

Example 4 Breast cancer tumour margins ex vivo via REIMS technology Three margin to tumour tests have been acquired, where a measurement was taken across the sample through normal and tumour human tissue and video was acquired to match the spectra using a GoPro set up. This data provides insight into lipid profiles across the tumour margin. Thus, tumour margins may be analysed ex vivo by analysing tissue samples. Results are shown in Figure 14.

Example 5 Gastrointestinal cancer analysis via REIMS technology 242 samples were collected from 102 human subjects, as shown in Table 5.1. Table 5.1 Total Subjects 102 Total Samples 242 Normal 90 Tumour 62 Adenomatous Polyp Appendix 21 Muscle 20 Submucosa 14 Total Classified Samples 175 The samples were histologically validated and analysed by mass spectrometry (Table 5.2). Table 5.2 Subject number Locati on Sample identifier Sample type JLA079 SMH ±A079 201504"_0 NORMAL MUCOSA CUT.raw NORMA_ SMH iLA079 20150410 NORMAL LAYER MUCOSA CUT.raw NOR MA'_ SMH iLA07920150410NORMALLAYERMLISCLECUT.raw MUSCLE JLA077 SMH iLA079 20150410 TUMOUR1 CUT.raw iLA07920150410TLJ MOUR2CUT.raw JLA077 20150313 NORMAL.raw TUMOUR

SMH TUMOUR

SMH NORMAL

SMH 1LA,077 20150313 TUMOUR.raw TUMOUR SMH JLA077 20150313 APPENDIX.raw APPENDIX JLA082 CXH CXH i LA082 20150421 NORMALraw.iLA082 20150421 TU MOUR.raw NORMAL TUMOUR JLA083 CXH JLA08320150421NORMAL.raw NO AL JLA085 CXH CXH i LA08320150421POLYP.raw iLA08320150421POLYP2.raw POLYP POLYP NORMAL CXH I i LA085 20150421 NORMAL raw CXH iLA085 20150421 POLYP.raw POLYP JLA086 SMH JLA086 20150422 NORMAL.raw NORMAL SMH 114086 20150422 TU MOUR.raw TUMOUR AS13 SNIP AS1320150422APPENDIX.raw APPENDIX 1LA091 CXH 1LA091 20150428 NORMAL.raw iLA091 20150428 POLYP.raw iLA091...20150428....TUMOUR.raw NORMAL POLYP TUMOUR

CXH

CXH

JLA096 SMH iLA09620150429NORMAL.raw NORMAL SMH i LA096 20150429 TUMOUR.raw TUMOUR AS14 SMH SMH AS14 20150501 APPENDIX.raw APPENDIX APPENDIX AS15 AS16 20150502 APPENDIX.raw AS16 SMH AS16 20150503 APPENDIX.raw APPENDIX JLA094M SMH AS16B20150503APPENDIX.raw iLA094M 20150505 NORMAL.raw i LA094M 20150505 TUMOUR.raw APPENDIX

SMH NORMAL

SMH TUMOUR

A517 SMH AS18 20150511 APPENDIX.raw APPENDIX SMH AS18 20150512 A.PPENDIX.raw APPENDIX JLA095 SMH iLA09520150511NORMAL.raw NORMAL SMI I iLA09520150511TUMOUR.raw TUMOUR ASTL8 SMH AS18 20150512 APPENDIX.raw APPENDIX ASI9 SMH AS19 20150512 APPENDIX.raw APPENDIX 11 A096 SMH JIA096 201.50513 NORMAL.raw NORMAL SMH iLA096...20150513....TUMOUR.raw TUMOUR SMH i LA096 20150513 APPENDX.raw APPENDIX JLA097 SMH JLA097 20150513 APPENDIX.raw APPENDIX AS20 SMH AS20 20150513 APPENDIX.raw APPENDIX JLA099 SMH JLA099 20150518 NORMAL.raw NORMAL JLA100M SMH RMH iLA099 20150518 TUMOUR.raw JLA100M 20150522 NORMAL.raw TUMOUR NORMAL RMH iLA100M 20150522 SUBMUCOSA MUSCLE.raw MUSCLE RMH iLA100M20150522TUrv1OUR.raw TUMOUR JLA101 SMH 1LA101 20150528 NORMAL.raw jLA10120150528TUMOUR.raw 1LA101...20150528....APPENDXraw NORMAL

SMH TUMOUR

SMH APPENDIX

JLA104 SMH A.A104 20150615 NORMAL NORMAL SMH il A104 2015061.5 TUMOUR POSTCHEMORAD TUMOUR JLA105 SMH SMH i LA105 20150615 NORMAL.iLA105 20150615 TUMOUR NORMAL TUMOUR JLA106 SMH JLA106_20150615_NORMAL NO AL SMH 1LA10620150615APPEND!X JLA106_20150615TERMINAL ILEUM iLA106...20150615TUMOUR...STE1 APPENDIX

SMH MUSCLE

SMH TUMOUR

SMH I iLA106 20150615 TUMOUR SiTE2 TUMOUR JLA107 CXH iLA107 20150616 NORMAL NORMAL CXH..14107 20150616 POLYP POLYP JLA109 CXI I iLA10920150618NORMAL.raw NORMAL JLA110 CXH 1LA109 20150618 TUMOUR.raw iLA110 20150625 NORMAL iLA110...20150625....NORMAL...02 TUMOUR

SMH NORMAL

SMH NORMAL

SMH iLA11020150625TUMOUR01 TUMOUR SMH i LA110 20150625 TUMOUR 02 TUMOUR JLA111 SMH SMH 1 A111 20150626 NORMAL 01 JLA111 20150626 TUMOUR 01 NORMAL TUMOUR SMH JLA111 20150626 TUMOUR 02 TUMOUR 1LA112 SMH iLA11220150629NORMAL iLA112 20150629 TUMOUR 01 i LA112 20150629 TUMOUR 02 NORMAL

SMH TUMOUR

SMH TUMOUR

JLA119 SMI I JLA119 20150709 NORMAL 01 NORMAL SMH JLA119 20150709 TUMOUR 01 TUMOUR SMH iLA119_20150709_TUMOUR_02 TUMOUR JLA123M SMI I iLA123M_20150720_TUMOUR TUMOUR 1LA124M SMH iLA124M 20150720 TUMOUR 01 TUMOUR SMH 1LA124M201507201UMOUR02 1 UMOUR 1! A125M SMH JLA125M 20150720 NORMAL. NORMAL SMI I iLA125M20150720...TUMOUR TUMOUR JLA128 SMH i LA128 20150728 NORMAL NORMAL SMH iLA128 20150728 TUMOUR TUMOUR 1LA133 SMH 1LA133 20150729 NORMAL NORMAL SMH JLA133 20150729 POLYP POLYP JLA140 SMH SMH JLA140 20150730 NORMAL i LA140 20150730 TUMOUR NORMAL TUMOUR Example 6 Ovarian cancer analysis using REIMS technology In this ex vivo data study, a total of 146 samples were analysed (Table 6.1)

Table 6.1

Total Samples 146 Ovarian Cancer 67 Normal (15 ovary, 15 peritoneum, 15 fallopian tube) 45 Borderline tumour of ovary 15 Benign ovarian lesions 14 Non-ovarian tumours 4 Non-ovarian smooth muscle tumour of uncertain malignant potential 1 (STUMP) The samples were histologically validated and analysed by mass spectrometry. Statistical analysis using supervised linear discriminant analysis showed excellent separation of cancer and borderline tissue on the margins of cancer and normal tissue. Good separation was also seen when including benign lesions. See Figure 15.

Example 7 -neurosurgery At least 28 intraoperative cases for neurosurgery were analysed, with over with 199 in vivo and over 207 ex vivo samples. An example data set is shown in Table 7.1.

Table 7.1

Subject Location Tumour type No. of in- No. of ex-vivo samples vivo samples IKBRA16 CXH Glioblastoma multiforrne 7 3 IKBRA17 CXH TBC 10 10 IKBRA18 CXH Low grade glioma with high grade parts 7 7 IKBRA19 CXH Likely glioblastoma multiforme IKBRA20 CXH Low grade glioma 14 13 IKBRA21 CXH Low grade glioma 6 7 IKBRA22 CXH Low grade glioma 8 9 IKBRA23 CXH Potential glioblastoma 8 10 IKBRA24 CXH Potential glioblastoma multiforme 8 10 Haemangioblastoma specimens were also analysed. Histology data has been matched to previous cases and specimen measurements.

Example 8 -Brain cancer analysis using REIMS technology Analysis was carried out on a subject suffering from glioblastoma multiforme ("GBM"), as discussed with reference to Figure 16.

The left-hand portion of Fig. 16 shows a 3D image of the brain of the subject which has been overlayed with a real time ultrasonic image. Six sampling points were taken with a REIMS technology probe during surgery and are also depicted on the image shown in Fig. 16.

Fig. 16 also shows six corresponding mass spectra which were recorded which each mass spectrum corresponding to a different sampling point.

Fig. 16 also shows a 3D PCA plot of all sampling point taken during the surgery. The 3D PCA plot was labelled by a neuropathologist.

All in vivo and ex vivo sampling points are shown on the PCA plot shown in Fig. 16. It is apparent from Fig. 16 that normal grey and white matter group separately both from the cancerous samples and from each other.

Thus, the method may optionally be used to analyse, e.g. identify or distinguish between, one or more brain tissue types, e.g. selected from grey matter, white matter, and/or cancer, wherein the cancer may, e.g. be glioblastoma multiforme.

Example 9 -Tumour typing and grading using REIMS probe Fig. 17 shows the result of comparing subjects with high grade (grade IV) glioblastoma multiforme (e.g., glioblastoma, giant cell glioblastoma and recurrent gliobastoma) and low grade (grade II and III) tumours (e.g. anaplastic astrocytoma, oligodendroglioma and diffuse astrocytoma).

It is apparent from Fig. 17 that high grade (grade IV) and low grade (grade II and III) tumours separated well on the 3D pseudo LDA plot.

Subjects having intermediate grade III tumours grouped either with the high grade area of the space or with the low grade area of the space.

Thus, the method may optionally be used to analyse, e.g. identify or distinguish between, one or more cancer grades, wherein the cancer may, e.g. be grade I, II, Ill, and/or IV, and/or be selected from, e.g., glioblastoma, giant cell glioblastoma, recurrent gliobastoma, anaplastic astrocytoma, oligodendroglioma, and/or diffuse astrocytoma.

Example 10 -Comparison of healthy and cancerous samples with both Raman spectroscopy and REIMS sampling A Subject was suffering from a low grade (grade II) astrocytoma. The subject was subjected to a combination of Raman spectroscopy sampling and REIMS sampling. Raman data from a total of 32 sampling points were recorded. 13 of these 32 sampling points corresponded with normal tissue, 18 of these 32 sampling points corresponded with cancerous

tissue and 1 corresponded with background.

REIMS sampling was also performed at 14 of the 32 sampling points.

Fig. 18a shows REIMS mass spectra from two sampling points. Sampling point S4 corresponded of tumour tissue with low cellularity. In particular, sampling point S4 corresponded with posterior medial superficial tumour. Fragments of the tumour tissue had low cellularity and some degree of reactive gliosis. Sampling point S14 corresponded with normal white matter have single cell infiltration. In particular, sampling point S14 corresponded with posterior base pot. Multiple fragments of white matter with reactive gliosis and single-cell tumour infiltration are present.

Fig. 18a also shows a 3D PCA plot corresponding to all sampling points taken throughout the surgery.

Fig. 18b shows corresponding Raman spectra from sampling points S4 (tumour) and 514 (normal white matter) together with a 3D PCA plot from all sampling points taken throughout the surgery.

Both the Raman spectra and REIMS technology spectra have a tissue specific "fingerprint" in the phospholipid range. The main differences observed on the PCA plot are due to the lipid vibration region.

There are a number of sulfatides which are very specific for normal white matter of brain. For example, the following sulfatides are specific for normal white matter of the brain:

Table 10.1

ink (calculated) compound formula 888.624 024:1 sulfatide C48H91N011S 906.635 024-OH sulfatide C48hi92NOI2S 916.655 C26:1 sulfatide C501-194NO11S Example 11 Detection of bacteria in human colorectal tissue specimens The inventors attempted to visualise the presence and distribution of bacteria in human colorectal tissue specimens. Bacteria are known to cover the mucosal membranes in the gut and the gut microbial community is arguably most extensively studied and characterised. The analysis was performed by generating single ion images for the taxonomical markers that are listed in Table 14. Bacteria could be visualised in >90% of analysed colorectal specimens, including healthy and cancerous tissue specimens. Among cancerous specimens, bacteria were largely found localised in areas that were identified as necrotic by histopathological examination of the H&E stained tissue sections. However, bacteria were also frequently detected along healthy mucosa. An example of each will be further discussed below.

11 A) Analysis of necrotic tissue Figure 19 shows the tissue type-distribution of a cancerous tissue specimen that originated from the centre of tumour dissected during a right hemicolectomy. Histopathological examination revealed the presence of cancerous and stromal tissue.

Mass spectra of the necrotic tissue area as well as surrounding cancerous and stromal tissue are shown in Figure 19 and display a markedly different phospholipid composition for the necrotic area compared to viable human tissue, namely a significantly reduced glycerophospholipid content and a variety of lower molecular weight sphingolipid-derived taxonomic marker species in the mass range of m/z = 500-700.

When visualising these taxonomical markers, the respective single ion images were found to largely display co-localisation of the taxonomical marker molecules and thus bacterial cells. An array of co-localised single ion images of homologous molecules are displayed in Figure 20 and could be attributed to the Bacteroidetes phylum. Iso-C15:0-substituted phosphoglycerol dihydroceramides were found to be specific for the Porphyromonadaceae family (part of Bacteroidetes phylum), which in this study were only represented by Parabacteroides spp., however, named compounds were reported present in high abundance in Porphyromonas gingival's, suggesting general applicability of this marker for this family. Members of the Bacteroidetes phylum were reported in metagenomic studies to be accountable for up to 50% of the gut microbial community. However, taxon-specific markers for Bacteroidetes fragilis were not detected suggesting that the Bacteroidetes bacteria present do not contain a high amount of the opportunistic pathogen B. fragilis.

Figure 21 shows single ion images of further taxonomical markers which were found to be specific for the Bacteroidetes phylum, among those dihydroceramide and a related compound with two more double-bonds (or equivalents). The compound at m/z = 639.4954 was found to be a homologue of the lipid species at m/z = 653.5113 mentioned earlier. A signal at m/z = 566.4790 indicates the presence of members of the Flavobacteria class. Specific plasmalogen species for Clostridiales and Fusobacteria were additionally found, as well as an odd numbered PE that shows specificity for the Enterobacteriales order. All of these bacterial classes are capable of living under anaerobic conditions and were reported to be major components of the human gut microbiome.

While members of the Bacteroidetes phylum largely cluster around the left hand side of the tissue section where necrotic areas were identified, Clostridiales and Fusobacteria were additionally detected in at a spot more centred within the tissue section, thus confirming the expectation that not all bacterial species show identical localisation. The large bacterial presence observed in the necrotic tissue areas is tentatively associated with the lack of immunoresponse of the human body, which enables bacteria to multiply largely uncontrolled.

11B Detection of bacteria in healthy mucosa Figure 22 shows the tissue type-distribution of a healthy tissue specimen that originated from a right hemicolectomy. It originated from healthy colon tissue 5cm distance from the centre of tumour. Histopathological examination revealed healthy mucosa and submucosa, divided by the muscularis mucosae layer. Additionally, two lymphoid aggregates (inflammation) can be observed.

Figure 22 shows single ion images for those taxon-specific markers that were detected in this sample. Generally, far fewer and less intense signals were observed than for necrotic tissue. This is tentatively attributed to the healthy immune response that restricts unlimited bacterial growth as was observed in the necrotic tissue specimen. However, the two main bacterial components of the commensal human microbiome could still be detected, namely members of the Bacteroidetes phylum and Clostridiaceae family.

Metagenomic characterisations were performed for this sample and confirmed the presence of large amounts of Bacteroidetes, Proteobacteria and Firmicutes which on class level were largely attributable to Clostridia, Bacteroidia, and Gamma-Proteobacteria, respectively. This study demonstrates that molecular species differ significantly between microbial lipidomes and the human tissue lipidome. Taxon-specific markers for a variety of bacterial types were shown to be absent in human lipidomes/metabolome and can thus be used to visualize the presence of bacteria in human samples, as shown for human colorectal tissues. It was further demonstrated that taxonomic markers derived by the REIMS technique can be used in conjunction with other mass spectrometric ionization techniques detecting lipid profiles, such as, DESI.

Example 12 Analysis of necrosis The method may be used to analyse necrosis, e.g., to detect necrotic tissue. This was exemplified in human lung tissue samples of two different patients. Samples were analysed using histopathology, which identified 100% necrotic cancer tissue.

The samples were also analysed using MS and it was possible to distinguish between necrotic and non-necrotic tissue using MS.

The second PC component separates necrosis from the other tissue, this can be seen in Figure 23. Adenocarcinoma, normal lung, cancer border, squamous cell carcinoma and necrotic tissue was analysed and could clearly be distinguished.

Example 13 Analysis of ovarian cancer

Background

Ovarian cancer (OC) is common and five-year survival is 21.9% and 5.6% for stage 3 or stage 4 disease respectively, which is when 60% of women first present. Intra-operative tissue identification typically relies on frozen section histopathological analysis, which is time-consuming and expensive. Macroscopic non-descript lesions, which may be cancer, can be difficult to correctly identify intra-operatively, especially after neo-adjuvant chemotherapy. Methods Fresh frozen ovarian samples (normal, benign, borderline, OC), plus fallopian tube and peritoneum were cut with the Covidien diathermy hand-piece. Surgical smoke was extracted and ionised in a Water's Xevo G2-S mass spectrometer. Resultant mass spectra underwent pre-processing and background subtraction with lock-mass. Processed tissue samples were re-reported by histopathologists to confirm histology. These data were used to create an authentic spectral database, which was histologically ratified. Data were processed with principal component and linear discriminant analyses and leave one patient out cross-validation.

In total 144 different samples were collected from 130 individual patients (some patients provided more than one tissue type), which is summarised in Table 13.1. Fresh tissue samples had been snap frozen and stored at -80°c. Data including age of sample, International Federation of Gynaecology and Obstetrics (FIGO) stage and grade of disease, histopathology as reported in medical records and sample site was recorded on a National Health Service (NHS) networked computer and only accessed by clinically authorised personnel.

Batches of tissue were issued from the tissue bank and logged to the study accordingly. 10 The samples were thawed and cut with a Covidien ForceTriadTM energy generator coupled with a modified electrosurgical knife. Samples were processed in cut mode using 25 watts and the resultant smoke analysed with a Water? G2-S TOF mass spectrometer in negative-ion mode.

Table 13.1: Tissue types included in study Organ group Tissue type Sub-type No of s ample s Spectra Ovary Normal 15 64 Benign 8 32 Borderline 8 30 Cancer Serous 32 115 EndomeMoid 9 35 Clear cell 7 24 Mucinous 5 21 No tumour seen 11 37 Inconclusive 15 49 Excluded 5 18 Fallopian tube Normal 14 49 Peritoneum Normal N/A 15 55 144 529 Findings 144 tissue samples were processed, producing 529 spectra. Normal ovary and OC could be distinguished in principal component and linear discriminant analyses. Cross-validation resulted in 100% sensitivity and 100% specificity in the separation of normal ovary from viable OC (n=189). A further analysis comparing OC with fallopian tube, normal ovary and peritoneum resulted in 100% sensitivity and 97-8% specificity with cross validation (n=291). Results are shown in Figure 24.

Interpretation This study has shown that normal ovarian, peritoneal and fallopian tube tissues have unique spectral signatures, which may be used to accurately determine tissue type. The method may be used intra-operatively (in-vivo). The method's ability to rapidly determine tissue type may shorten operations and reduce morbidity and mortality, potentially improving patient care and survival.

Example 14 Faecal analysis using REIMS 1. Take a sample, e.g., a 10 pl loop of fresh or, if frozen, a defrosted sample of stool.

2. If using forceps based REIMS, take a small amount between the forceps and draw the probes together.

3. Perform REIMS analysis, e.g., using previously described parameters for REIMS.

Figure 25 shows a spectrum observed when analysing stool samples using REIMS Example 15 REIMS Analysis of Blood Culture Pellets Objective: This protocol describes a specific example of a procedure for analysing blood culture samples using REIMS analysis.

Initially, inoculate 10 ml of defibrinated horse blood with a single microbial colony. Grow this aerobically at 37 'C for 24 hours. Next, inoculate 1 I of horse blood with lml of the overnight culture. Grow aerobically at 37°C and at time 0 and each hour thereafter remove 25 ml to analyse in the following way: a. Transfer 10 ml into a 50 falcon tube and centrifuge the sample for 10 mins at 3,2000 g. Use REIMS to analyse the pellet as described below.

b. Make a 2.5% Microbiology grade agar solution using HPLC water and heat until the solution reaches 50°C. Leave to stand for 1 minute to remove air bubbles. Next, add 2 ml of this to 8 ml of the blood culture described above and mix gently by pipetting. Pour into a small agar plate and allow to set for 15 minutes. Use this to perform REIMS analysis.

c. With 1 ml of this solution make serial dilutions to 10-6 using molecular grade water, and plate 100 pl of each onto a blood agar plate. Incubate for 24 hours and after count the number of colonies to determine the CFU.

d. Use a further 2 ml of the blood culture and freeze at -80 °C for LC-MS analysis.

REIMS analysis may be performed on the centrifuged pellet and/or the agarose block.

Example 16 Analysis of mucosal specimens using DESI mass spectrometry Medical swabs were analysed by desorption electrospray ionisation ("DESI") mass spectrometry with the intention of extracting chemical information relevant to patient care in a non-invasive procedure. In this context, desorption electrospray ionisation ("DESI") mass spectrometry represents a fast and direct method for metabolomic profiling of different mucosal membrane models or membranes (e.g. nasal, vaginal, oral) by desorbing and analysing molecules from the surface of standard medical cotton swabs.

A study was performed in which vaginal mucosa (n=25 pregnant, n=25 non-pregnant), nasal mucosa (n=20) and oral mucosa (n=15) were sampled with medical ryon swabs from patients. Medical cotton swabs sold as Transwab (RTM) Amies (MWE medical wire, Wiltshire, UK) were used for sampling mucosal membranes which were then transferred to a sterile tube without buffer or storage medium solution and were stored at -80 °C in a freezer.

Fig. 32 highlights the sampling points of analysed mucosal membranes collected from the urogenital tract, oral and nasal cavity with a medical cotton swab 320. As illustrated by Fig. 32, the surface of the medical swab 320 was directly analysed by desorption electrospray ionisation ("DESI") mass spectrometry without prior sample preparation procedures.

Desorption electrospray ionisation ("DESI") mass spectrometry experiments were performed using a Xevo G2-S Q-TOF (RTM) mass spectrometer (Waters (RTM), Manchester, UK). The desorption electrospray ionisation ("DESI") source comprises an electronic spray emitter 321 connected with a gas 322, solvent 323 and power supply 324 and an automatic rotatable swab holder device 325 with adjustable rotation speed.

For the desorption electrospray ionisation ("DESI") mass spectrometry analysis the medical swab 320 was positioned orthogonally to and in front of an inlet capillary 326 connected to the mass spectrometer atmospheric pressure interface 327. A mixed methanol:water solution (95:5) spray solvent was used at a flow rate of around 10 pLimin for desorption of the sample material. Nitrogen gas at around 7 bar and a voltage of around 3.4 kV were also provided to the sprayer 320.

The mucosa was absorbed from the surface of the rotated swabs by gently desorbing molecules with charged droplets of the organic solvent, and desorbed ions (e.g. lipids) were subsequently transferred to the mass spectrometer.

Full scan mass spectra (m/z 150-1000) were recorded in negative ion mode.

Spectrometric data were then imported into a statistical analysis toolbox and processed. For data analysis and extraction of specific molecular ion patterns, an unsupervised principal component analysis ("PCA") as well as a recursive maximum margin criterion ("RMMC') approach were applied to improve supervised feature extraction and class information with leave one out cross validation ("CV") to determine classification accuracy within the data set.

Figs. 33A and 33B show the results of desorption electrospray ionisation ("DES(*) mass spectrometry analysis of swabs, and multivariate statistical analysis including principal component analysis (PGA) and recursive maximum margin criterion (RMMC) in an investigation of metabolic signatures in different mucosal membrane models.

Fig. 33A shows averaged negative-ion mode desorption electrospray ionisation ("DESI") mass spectra from vaginal, oral and nasal mucosa recorded using a Xevo G2-S Q-Tof (RTM) mass spectrometer.

Fig. 33B shows a principal component analysis ("PCA") and a maximum margin criterion ("MMC") score plots for vaginal (n=68), oral (n=15) and nasal (n=20) mucosa acquired with desorption electrospray ionisation ("DESI") mass spectrometry.

As shown in Fig. 33A, unique lipid patterns were observed between different mucosal membrane models. The spectra for vaginal mucosa and oral mucosa featured predominately glycerophospholipids, e.g., [PS(34:1)-Hi having a mass to charge ratio ("m/z") of 760.4, [PS(36:2)-H]-having a m/z of 788.5 and [PI (36:1)-H]' having a m/z of 863.4.

As shown in Fig. 33A, nasal mucosa featured mainly [PC(36:2)-CI] m/z 820.5, [PC(34:2)+Cl]-and [PI(36:2)-H]-m/z 826.4 in the m/z 700-900 range.

An interesting feature was observed predominantly in the vaginal mucosal membrane where the deprotonated cholesterol sulphate peak at a m/z of 465.3 is the most dominant peak 11)3 in the spectrum. Chemical assignment of this peak was confirmed by tandem mass spectrometry experiments. This compound is an important component of cell membranes with regulatory functions including a stabilizing role, e.g., protecting erythrocytes from osmotic lysis and regulating sperm capacitation.

Leave-one-patient-out cross validation of the multivariate model containing spectra obtained by the analyses of three mucosal models resulted in a high classification accuracy. This show that MS based profiling of different mucosal membranes allows stratification of patients based upon bacterial diversity.

Similarly, Fig. 34 shows Fourier transform mass spectrometry ("FTMS") spectrometric data obtained from vaginal, oral and nasal mucosa on medical cotton swabs in negative ion mode in the mass range of m/z 150-1000. Again, different metabolic signatures were observed in each mucosal membrane model.

In total, 300 to 1000 spectral features found without isotopes and adducts including small human primary metabolites such as cholesterol sulphate, bacterial secondary metabolites including lactate as well as glycerophospholipids were tentatively identified by exact mass, isotope cluster distribution and tandem mass spectrometry experiments in the mucosal membrane.

Fig. 35 shows a desorption electrospray ionisation ("DESI") mass spectrum relating to a pregnant vaginal mucosal membrane in more detail which was obtained in negative ion mode using a medical cotton swab. The urogenital mucosa was found to produce cholesterol sulphate [M-Hr at a m/z of 465.41 as the most abundant lipid species as well as a different glycerophosholipids species such as glycerophosphoethanolamine (PE) [PE(40:7)-1-1]-at a m/z of 788.50, glycerophosphoserine (PS) [PS(34:1)-Hy at a m/z of 760.50 and glycerophosphoinositol (PI) [P1(36:1)-Fi]" at a m/z of 863.58. As shown in Fig. 35, chemical assignment of the cholesterol sulphate peak was confirmed by tandem mass spectrometry experiments.

The spectrometric data of Fig. 34 were further processed using median normalization, background subtraction, Savitzky-Golay peak detection, peak alignment and log-transformation. Following data processing, multivariate statistical analysis was applied on the data set to characterise distinct mucosa models based on their metabolic profile. Multivariate statistical analysis tools including principal component analysis (PCA) and maximum margin criterion (MMC) were used to analyse the data set.

As shown in Fig. 34, the PCA score plot as well as the MMC score plot reveal a separation of the different mucosal membrane types within the first two components with a prediction accuracy between 92-100% obtained by leave one out cross validation.

It will be appreciated that analysis according to various embodiments results in characteristic profiles for the various sample types that can be clearly distinguished e.g., by using RCA, MMC and/or leave one out cross validation analyses. These results show the use of desorption electrospray ionisation ("DESI") mass spectrometry to characterise human mucosal membrane models, e.g. based on their metabolic signatures excreted by characteristic bacteria, as a fast bacterial identification method, e.g., compared to 16S rRNA sequencing.

Further embodiments are contemplated wherein chemical biomarkers in human mucosal membranes may be measured, which are reliable predictors e.g. in the cases of dysbiotic, inflammatory, cancerous and/or infectious diseases.

In the case of vaginal mucosa, a clinical set of pregnant (n=22, in a gestational age between 26 and 40 weeks) and non-pregnant mucosal membrane (n=22) were evaluated in more detail in order to reveal metabolic signature differences caused by a change in the vaginal microbiome during pregnancy. Desorption electrospray ionisation ("DESI") mass spectrometry spectra were acquired from both groups in negative ion mode in the mass range of m/z 1501000. A number of different metabolites were detected in the vaginal mucosal membrane.

Fig. 36A shows averaged desorption electrospray ionisation ("DESI") mass spectra from pregnant and non-pregnant group acquired in the negative ion mode in the mass range m/z 150-1000. A comparison of the averaged spectra shown in Fig. 36A shows spectral differences between non-pregnant and pregnant mucosa metabolic profiles, especially in the lipid mass range from m/z 550-900.

Further data analysis comprising unsupervised PCA and RMMC analysis were utilised to visualize differences between both groups.

Figs. 36B and 36C show the results of multivariate statistical analysis of pregnant (n=22) and non-pregnant (n=22) vaginal mucosal membrane using desorption electrospray ionisation ("DESI") mass spectrometry.

Fig. 36B shows principal component analysis and discriminatory analysis using RMMC and Fig. 36C shows analysis with leave-one-out cross-validation.

Fig. 36D shows box plots which indicate significant differences in the abundance of selected peaks between non-pregnant and pregnant vaginal mucosal membrane mainly in the range from m/z 550-1000 obtained by Kruskal-Wallis ANOVA, p<0.005.

As shown in Fig. 36E, using RMMC both groups separate well in the RMMC space with a high (>80%) classification accuracy according to distinct metabolic signatures obtained by leave-one-patient-out cross validation.

Fig. 37A shows desorption electrospray ionisation ("DESI") mass spectrometry analysis of a bacteria (Klebsiella pneumonia) sample on a swab in accordance with an embodiment.

The data illustrated in Fig. 37A shows that bacterial samples can be detected using desorption electrospray ionisation ("DESI") mass spectrometry on swabs, according to various embodiments. Fig. 37B shows for comparison rapid evaporative ionisation mass spectrometry ("REIMS") time of flight ("TOF") mass spectrometry data of a corresponding bacterial sample measured directly from an agar plate. The peaks highlighted by stars were detected with both ionisation techniques.

Desorption electrospray ionisation ("DESI") swab analysis for microorganism detection was further tested on six cultivated species including Candida albicans, Pseudomonas montelk Staphylococcus epidermis, Moraxella catarrhalis, Klebsiella pneumonia and Lactobacillus sp. These are all important bacteria and fungi species that were isolated from vaginal mucosal membranes of pregnant patients and which were identified by sequence analysis such as 16S rRNA gene sequencing. 11)5

A swab was quickly dipped into a solution of diluted biomass from each species in 10 pL methanol, followed by desorption electrospray ionisation ("DESI") mass spectrometry analysis of the swab surface.

Figs. 38A-C show microorganism analysis using desorption electrospray ionisation ("DESI") mass spectrometry on swabs.

Fig. 38A shows averaged desorption electrospray ionisation ("DESI") mass spectra of diverse analysed microorganism species including Candida albicans, Pseudomonas montelli, Staphylococcus epidermis, Moraxella catarrhalis, Klebsiella pneumonia and Lactobacillus sp.

Figs. 38B and 38C show PCA plots showing a separation between the vaginal mucosa (pregnant and non-pregnant group) and the microorganism species within the first two components. In addition, a separation can be observed between the different bacteria and fungi species.

Unique spectral features were observed in the mass spectra as shown in Fig. 38A resulting in the ability to separate between different microorganism classes as well as from the vaginal mucosa in the PCA score plots (Figs. 38B and 38C) within the first two components.

This result shows the potential to characterise microbe, e.g., bacteria-specific and host-response metabolite biomarkers and signatures from specific microbial, e.g., bacterial communities from the animal, e.g., human mucosal membrane using desorption electrospray ionisation ("DESI") mass spectrometry on medical swabs.

Example 17 Example of Data Analysis Raw mass spectrometric files were converted into mzML format and subsequently imported as imzML format (REF) into MATLAB (Mathworks, Natick, MA; http://www.mathworks.co.uk/) for data pre-processing. All REIMS spectra were linearly interpolated to a common sampling interval of 0.01 Da. Recursive segment wise peak alignment was then used to remove small mass shifts in peak positions across spectral profiles. The aligned data were subjected to total ion count (TIC) data normalization and log-based transformation. Pattern recognition analysis and visualization were performed either in Matlab or in RStudio (Boston, MA, USA, see also www.r-project.com). Only the mass range of m/z 150-1000 was used for data analysis. For self-identity experiments, the data set was filtered to keep a reduced set of m/z values: a m/z value was kept, if the difference between the available samples were significantly different at alpha=0.01 threshold level based on the Kruskal-Wallis test.

Ionic species in the mass spectra were identified based on exact mass measurements (mass deviation <3ppm) and MS/MS fragmentation patterns.

Example 18 Imaging liver with metastatic tumour Human liver tumour samples were analysed by ion imaging using REIMS imaging technology or DESI imaging mass spectrometry (as illustrated in Fig. 51). A cutting mode rapid evaporative ionization mass spectrometry image was obtained on a first instrument whilst a pointing mode image was obtained on a Time of Flight mass spectrometer. Spatially resolved mass spectrometric information was co-registered with H&E images to locate mass spectra with the desired histological identity. Supervised multivariate analysis of the tissues revealed clear distinction between healthy and cancerous tissue for both rapid evaporative ionization mass spectrometry imaging and DESI imaging data.

The DESI images show a sharp border between the two tissue types as a result of the high spatial resolution and small pixel size of 100 pm. The upper half of the cutting mode rapid evaporative ionization mass spectrometry image contains pixels of mixed healthy and tumour pattern influences causing a blurred border. A possible explanation is due to the direction of the rapid evaporative ionization mass spectrometry cut that was performed which started at healthy tissue and continued towards the tumour region. This might have caused transport of tumour tissue pieces into the healthy area. Another reason may be inhomogeneous tissue below the surface of the seemingly cancerous area.

Assuming that the mass spectra are to be used as reference data for the iKnife technology, then only pixels with a high class-membership probability should be used for training the multivariate models (i.e. the sample classification model).

Unsupervised principal component analysis (PCA) demonstrates high intra-tissue-type spectral similarity together with spatially distinct clustering of healthy and cancerous data points in PCA space (see Fig. 52).

DESI imaging data acquired at high spatial resolution can also be used to locate histological fine structures and their corresponding mass spectra which can then be co- registered with the rapid evaporative ionization mass spectrometry data. A limiting factor for co-registration of DESI and rapid evaporative ionization mass spectrometry data is the spatial resolution currently achievable with the rapid evaporative ionization mass spectrometry platform. While the cutting mode image was recorded at 500 pm pixel size, the pointing mode image features 750 pm sized pixels. In the case of this liver metastasis sample, the resolution is sufficient. However, in case of tissues with higher heterogeneity, higher spatial resolution images may be advantageous. The spatial resolution may be increased to decrease the diameter of the electrosurgical tip of the sampling probe which would also be accompanied by lower spectral intensities. However, by connecting the sampling probe directly to the mass spectrometer inlet capillary (as is also done in the bipolar forceps approach described above) ion yield improves, thus overcoming the possible sensitivity issue. This also allows less penetration in z-direction, decreasing the probability of ionizing unanticipated tissue types. A resolutions of, for example, 250 pm sized pixels may be achieved.

Multivariate analysis of the liver metastasis samples shows a clear distinction of tissue types based on their molecular ion patterns. While rapid evaporative ionization mass spectrometry and DESI exhibit different ionization mechanisms resulting in mass spectrometric patterns that are not directly comparable to each other, univariate biochemical comparison of single ions provides a comparable measure for DESI and rapid evaporative ionization mass spectrometry co-registration. For certain compounds, the relative intensity difference between two tissue types is similar across all tissue types, ionization techniques and rapid evaporative ionization mass spectrometry analysis modes (cutting and pointing modes). This enables DESI to be used as a fold-change intensity-predictor for rapid evaporative ionization mass spectrometry based on up-and down-regulated compounds, which ultimately represents additional information for unknown tissue type identification. The higher spatial resolution of DESI allows the up-and down-regulated ions to be registered with certain histological features which may not be resolvable by rapid evaporative ionization mass spectrometry. This gives insight to the underlying histological composition of a tissue if certain changes in single ion intensities are observed in low resolution rapid evaporative ionization mass spectrometry.

In the case of metastatic liver comparison, two different phosphatidyl-ethanolamine (PE) species were found to possess opposite relative intensities between healthy and metastatic tissue types as shown in Fig. 53. The represented images are ion images of the two PE ion species. PE(38:4) has a higher abundance in healthy tissue in all four cases, with the rapid evaporative ionization mass spectrometry cutting mode image showing barely any presence of this ion in tumour tissue. However, compared to the DESI images where this lipid is well abundant even in tumour tissue, the absence of intensity has to be associated with the lower sensitivity achieved by rapid evaporative ionization mass spectrometry cutting. Opposite behaviour is seen by the ion [PE(36:1)-H]-showing elevated intensities in tumour tissue.

Example 19 Analysis of healthy submucosa and GI polyps Significant spectral differences were observed between healthy gastric mucosa, healthy gastric submucosa and gastric cancer tissue. Spectra of healthy gastric mucosa (n=32) and gastric adenocarcinoma (n=29) featured phospholipids in the range m/z 600-900 while the gastric submucosa (n=10) featured intensive triglyceride ("TG") and phosphatidyl-inositol ("PI") species in the m/z 900-1000 range as shown in Fig. 54A.

The submucosa in the GI tract represents a connective tissue layer containing arterioles, venules and lymphatic vessels. It is made up of mostly collagenous and elastic fibres with varying amounts of adipose elements. It is hypothesised that the PI and triglycerides species observed in the m/z 900-1000 mass range are associated with these histological features present within the submucosa.

An interesting feature was observed regarding the abundance of phosphatidylethanolamines and corresponding plasmalogen species. While the PEs show higher abundance, the plasmalogens are depleted in the tumour tissue, probably due to the impaired peroxisomal function of the cancer cells.

Fig. 54B shows a number of selected peaks which are significantly different between the healthy tissue layers and cancer tissue in the mass range 600-900. All peaks between m/z 900 to 1000 show significant differences when comparing the gastric submucosa to either adenocarcinoma or gastric mucosa.

Example 20 Analysis of cancer in mucosa Analysis of ex vivo human colonic adenocarcinoma (n=43) and healthy colonic mucosa (n=45) acquired from seven patients was conducted using a LTQ Velos (RTM) mass spectrometer at the University of Debrecen, Hungary.

Adenomatous polyps (n=5) from two patients were also sampled ex vivo and the resulting rapid evaporative ionisation mass spectrometry data was analysed using multivariate statistical tools as shown in Figs. 55A and 55B. The spectra acquired from healthy mucosa and lox adenocarcinoma of both the stomach and colon were discovered to separate well in 3 dimensional PCA space as can be seen from Figs. 55A and 55B. The sampled adenomatous polyps also demonstrate good separation from both healthy mucosa and malignant tissue from the colon as shown in Fig. 55A.

Following the proof of concept analysis of ex vivo samples, the rapid evaporative ionisation mass spectrometry endoscopic method was also tested in vivo on three consecutive patients referred for colonoscopy. Different regions of the colon and rectum were sampled during the colonoscopy procedures. The first and third patients had evidence of colonic polyps and these were confirmed to be benign. The second patient had evidence of a normal colon with no visible polyps. The mucosal layer showed uniform spectral pattern independently from anatomical location. However, colonic polyps showed marked differences from the healthy mucosal layer as shown in Fig. 56B.

The data presented herewith demonstrates the significant advantages in using the rapid evaporative ionisation mass spectrometry technique as a real-time diagnostic tool in endoscopy.

For the experiments described in Examples 19 and 20, a commercially available polypectomy snare (Olympus (RTM) Model No. SD-210U-15) having a working length of about 2300 mm, minimum channel size about 2.8 mm, opening diameter about 15 mm and wire thickness about 0.47 mm was equipped with an additional T-piece in order to establish connection with a 1/8" OD 2 mm ID PFTE tubing between the tissue evaporation point and the atmospheric inlet of a mass spectrometer (Xevo G2-S (RTM) Q-TOF, Waters (RTM), Manchester, UK, and a LTQ Velos (RTM) linear ion trap mass spectrometer, Thermo Fischer Scientific (RTM), Bremen, Germany).

The snare was used with a commercially available endoscope (Olympus (RTM), Tokyo, Japan) and the associated endoscopic stack which was coupled with an electrosurgical generator (Valleylab Surgistat II (RTM)).

The endoscopic plume generated during the removal of polyps was captured through the fenestrations on the rapid evaporative ionisation mass spectrometry snare. The endoscopic plume was then transferred to the mass spectrometer through the endoscope housing and via PFTE tubing which was coupled directly to the inlet capillary of a mass spectrometer using the internal vacuum of the mass spectrometer for plume capturing.

High resolution mass spectrometry was performed in negative ion mode between m/z 150-1500 range.

The data analysis workflow for the separation of healthy, cancerous and adenomatous polyps of the gastrointestinal tract included the construction of a tissue specific spectral database followed by multivariate classification and spectral identification algorithms in a known manner.

Example 21 DESI-MS imaging Specimens, such as tissue sections or microbes smeared onto the surface of a standard glass microscope slide, were subjected to DESI-MS imaging analysis using an Exactive mass spectrometer (Thermo Fisher Scientific Inc., Bremen, Germany). Exactive instrument parameters are listed in the Error! Reference source not found.below.

Thermo Exactive instrumental parameters used for DESI-MS imaging.

Parameter Setting.

Polarity negative Resolution 100,000 Mass range 200-1050 Spray voltage -4.5kV Capillary 250°C temperature Capillary voltage -50V Tube lens -150V voltage Skimmer Voltage -24V Max. injection 1000ms time Microscans 1 AGC target 5e6 Methanol/water (95:5 v/v) was used as the electrospray solvent at a flow-rate of 1.54/min. Nitrogen N4.8 was used as nebulising gas at a pressure of 7bars. All solvents used were of LC-MS grade (Chromasolv. Sigma Aldrich, St Louis, MO, USA). The height distance between the DESI sprayer and the sample surface was set to 2mm with the distance between the sprayer and sniffer set to 14mm. The distance between the sample surface and the inlet capillary of the mass spectrometer was «1 mm. The angle between the sprayer tip and the sample surface was set at 80". The collection angle between inlet capillary and sample was set to 10°.

The general principle underlying imaging processes using DESI MS is that rather than point-by-point sampling. horizontal line scans are performed over the specimen surface by moving the automated sampling platform at a speed that covers the area determined as a pixel (spatial resolution) in the time the mass spectrometer requires to complete one scan (acquire one mass spectrum). This results in each one file per row of the resulting image (number of rows determined by sample height divided by spatial resolution).

For image analysis, individual horizontal line scans were converted into.imzML files using the imzML Converter Version 1.1.4.5 (www.maldi-msi.org). Single ion images and RGB images were generated using MSiReader Version 0.05(146) with linear interpolation (order 1) and 0.005Da bin size.

Table 1: Table of biomarkers: phospholipids and their spectrometric signals Identified phospholipids detected in the mass range m/z = 600-900 for all analysed microbial species. Only phospholipids with relative abundances >5% and only the most abundant acyl chain combination were included. Solid growth media on which bacteria were grown is given in parentheses. ID based solely on exact mass when lipid composition given as sum carbon number rather than individual acyl chains.

Nominal mass C. E. coil K. P. mira-bilis P. aerug nose S. mara-seen S. S. S. koseri pneumo-nlae aureus &vise-gee pyogenes mlz (CBA) (CBA) (LB) (MCC) (LB) (MCC) (CBA) (CBA) (CBA) 645,PA(32:1)* 659 PA(16:0/17: PA(16:0 PA(16:0/1 1) 17:1) 7:1) 661 PA,:33:0 * 665 PG(12:0/16 :0) 671 PA(34:2)* 673 PA(16:0/ PA(13:0/1 PA(16:0/18 18:1) 8:1) :1)* 675 P0(15:0 PG(30:0-H20)* 15-0-H20) 688 PE(16:1 PE(16:1/ /16:0) 16:0) 691 PG(14:0/16 :1) 693 PG(16:0 PG(16:0/14: PG(15:0 PG(15:0 PG(14:0/16 /14:0) 0) 15:0) /15:0) :0) 697 PA(36:3)* 699 PA(18:1/18 :1)* 701 PG(32:1)-H20* PG(32:1)-H2O* 702 PE(16:0 PE(16:0 PE(16:0/17: PE(16:0 PE(16:0/1 /17:1) 17:1) 1) 17:1) 7:1) 707 PG(15:0 16:0) 716 PE(18:1 PE(18:1 PE(' 8:1'1 PE(17:0/1 /16:0) 16:0) 6:0) 7:1) 717 PG(32:2 PG(16:1/16 )' :1) 719 PG(16:1 PG(16:1 PG(16:0/16 PG(16:0 PG(16:6/1 PG(16:0/1 PG(16:0 PG(16:0/16 /16:0) 16:0) 1) 16:1) 8:1) 6:1) /16:1) :1) 721 PG(15:0 PG(15:0 PG(16:0/16 17:0) /17:0) :0) 725 PA(16:1/18 :2) 727 PG(16:1/18:1)-H20 729 P0(16:0 PG(16:0/18 /18:1)-H2O* :1)-H20 730 P=(16 0/ 19:1) 733 PG(16:0 17:1) PG(16:0/PG(16:0/17:PG(16:3/ 17:1) PG(16:0/1 7:1) /17:1) 1) 7:1) PG(1PG(16:0/1 735 PG(15:0/ 18:0), 743 PG(16:0 PG(16:1/18 118:3) :2) 745 PG(16:1 18:1) PG(16:6PG(16:0/PG(16:0/18:P:3(16:0/ PG(16:1/PG(16:1/18: 18:1) PG(16:1/1 PG(16:1/1 PG(16:0 /18:2)* PG(16:0 /18:1) PG(16:1/18 /18:1) 18:1) 1) 8:1) 8:1) :1) /18:1) 1) PG(16:0/1 PG(160/1 PG(16:0/18 8:1) 8:1) :1) 749 PG(15:0/ PG(15:0 PG(16:0/18:ir 19:0) /19:0) 759 18:1) PG(17:1/PG(17:1/18: PG(17:1/1' PG(17:1/1 1) 8:1) 8:1) 761 19:1) PG(16:0/PG(16:0/19:PG(16 19:1) 01PG(16:0/1 PG(16:0/1 1) 9:1) 9:1) , 763, PG(15:0 20:0) 770 PE(38:2)* 771 PG(36:3 PG(18:1/18 )* :1)* 773 PG(18:1 P3(18:1 PG(17:1/19: PG(17:1/1 PG(18:1/1 P3(36:2 PG(18:1118 /18:1) 18:1) 1) 9:1) 8:1) )* :1) 775 PG(36:1 PG(18:0118.

)* :1) 787 PG(18:1/19: 1) 801 PG(19:1/19: 1) * Signal intensity not sufficient to obtain meani-igful MS/MS data; Abbreviations: PG = phosphatidylglyceroi, PE = phosphatidylethanolamine, CBA. = Columbia blood agar, LB = lysogenid broth agar, MCC = McConkey agar.

Table 2 -Table of biomarkers: cardiolipins and their mass spectral signals Cardiolipin species that were identified for Staphylococcus epidermidis ATCC 12228.

Compound Sum Exact Exp. Mass formula mass mass Deviation [M-H] CL(62:0) C71 H 136017 P2 1323.9335 1323.9268 5.0 ppm CL(63:0) C72H f 400f 7P2 1337.9492 1337.9426 4.9 ppm CL(64:0) C73H 142017 P2 1351.9649 1351.9601 3.6 ppm CL(65:0) C741-1144017P2 1365.9806 1365.9758 3.5 ppm CL(66:0) C751-1146017P2 1379.9962 1379.9913 3.5 ppm CL(67:0) C761-1148017P2 1394.0119 1394.0070 3.5 ppm CL(68:0) C77F1150017P2 1408.0275 1408.0238 2.6 ppm CL(69:0) C781-1152017P2 1422.0432 1422.0400 2.3 ppm CL(70:0) 0791-1154017P2 1436.0588 1436.0561 1.9 ppm CL(71:0) C801-1156017P2 1450.0745 1450.0748 0.2 ppm CL(72:0) Cal H 1'68017 P2 1464.0900 1464.0970 4.8 ppm Table 3 Table of biomarkers: mycolic acids and their mass spectral signals Identified mycolic acids as detected in different Corynebacterium species.

Compound um Exact Exp. Mass MS/MS fragments foSrmula mass mass Deviation on [M-H]- alpha-Mycolic acid C281-15503 439.415669 439.4159 0.5 ppm -028:0 alpha-Mycolic acid C301-15903 467.446969 467.4473 0.7 ppm 227 (C14:0), 255 C30:0 (C16:0) alpha-Mycolic acid C32H6103 493.462619 493.4634 1.6 ppm C32:1 alpha-Mycolic acid C32H6303 495.478269 495.4786 0.7 ppm 255 (C16:0) C32:0 alpha-Mycolic acid 034H6303 519.478269 519.4788 1.0 ppm C34:2 alpha-Mycolic acid C34H6603 521.493919 521.4942 0.5 ppm 255 (C16:0), 281 C34:1 (018:1) alpha-Mycolic acid C36H6703 547.509569 547.5102 1.2 ppm 281 (018:1) C36:2 Table 4 Table of biomarkers: mycolic acids and their mass spectral signals Identified mycolic acids as detected in Rhodococcus species.

Compound Sum Exact Exp. Mass formula mass mass Deviation [M-FI] alpha-Mycolic acid C28:0 0281-15503 439.4157 439.4159 0.5 ppm alpha-Mycolic acid 030:1 C30H5803 465.4313 465.4315 0.4 ppm alpha-Mycolic acid C30:0 C301-16003 467.4470 467.4472 0.4 ppm alpha-Mycolic acid C31:1 031 H6003 479.4470 479.4473 0.6 ppm alpha-Mycolic add C31:0 031 H6203 481.4626 481.4630 0.8 ppm alpha-Mycolic acid C32:2 C32H6003 491.4470 491.4475 1.0 ppm alpha-Mycolic acid C32:1 C32H 6203 493.4626 493 4634 1.6 ppm alpha-Mycolic acid C32:0 C32H 6403 495.4783 495.4786 0.6 ppm alpha-Mycolic acid C33:2 C33H6203 505.4626 505.4630 0.8 ppm alpha-Mycolic add 033:1 C33H6403 507.4783 507.4785 0.4 ppm alpha-Mycolic acid C33:0 C331-16603 509.4939 509.4943 0.8 ppm alpha-Mycolic acid C34:3 Csi4H6203 517.4626 517.4632 1.2 ppm alpha-Mycolic acid C34:2 C34H 6403 519.4783 519.4788 1.0 ppm alpha-Mycolic acid 034:1 034H 66O3 521.4939 521.4944 1.0 ppm alpha-Mycolic acid C34:0 C34H6803 523.5096 523.5100 0.8 ppm alpha-Mycolic acid C35:3 C35H 6403 531.4783 531.4784 0.2 ppm alpha-Mycolic acid C35:2 035H 6503 533.4939 533.4946 1.3 ppm alpha-Mycolic acid 035:1 C351-15803 535.5096 535.5100 0.7 ppm alpha-Mycolic acid C35:0 035H 7003 537.5252 537.5259 1.3 ppm alpha-Mycolic acid C36:3 0351-15503 545.4939 545.4944 0.9 ppm alpha-Mycolic add 0362 C36 H 6803 547.5096 547.5102 1.1 ppm alpha-Mycolic acid C36:1 C36H7003 549.5252 549.5260 1.5 ppm alpha-Mycolic acid C36:0 C36 H 7203 551.5409 551.5424 2.7 ppm alpha-Mycolic add C37:3 C371-16803 559.5096 559.5102 1.1 ppm alpha-Mycolic add C37:2 C37h17003 561.5252 561.5257 0.9 ppm alpha-Mycolic acid C37:1 Cs71-17203 563.5409 563.5418 1.6 ppm alpha-Mycolic add C37:0 C37H 7403 565.5565 565.5573 1.4 ppm alpha-Mycolic add C38:4 C381-17403 571.5096 571.5098 0.3 ppm alpha-Mycolic acid C38:3 C38F17403 573.5252 573.5261 1.6 ppm alpha-Mycolic acid C38:2 C381-17403 575.5409 575.5415 1.0 ppm alpha-Mycolic acid C38:1 C381-17403 577.5565 577.5579 2.4 ppm alpha-Mycolic acid C39:2 0351-17503 589.5565 589.5578 2.2 ppm Table 5 Table of biomarkers: mycolic acids and their mass spectral signals Identified mycolic acids as detected in Nocardia species.

Compound Sum Exact Exp. Mass formula mass mass Deviation [M H] alpha-Mycolic acid C48:3 C481-19003 713.6817 713.6797 2.8 ppm alpha-Mycolic acid C48:2 C461-19203 715.6974 715.6959 2.1 ppm alpha-Mycolic acid C50:3 C501-19403 741.7130 741.7114 2.2 ppm alpha-Mycolic acid C50:2 C50H9603 743.7287 743.7285 0.3 ppm alpha-Mycolic acid C52:3 C52H9403 769.7443 769.7430 1.7 ppm alpha-Mycolic acid C52:2 C52H 9603 771.7600 771.7588 1.6 ppm alpha-Mycolic acid 053:3 C531-19603 783.7600 783_7596 0.5 ppm alpha-Mycolic acid C53:2 C53H9403 785.7756 785.7754 0.3 ppm alpha-Mycolic acid C54:4 054H 9603 795.7600 795.7594 0.8 ppm alpha-Mycolic acid C54:3 C541-19803 797.7756 797.7739 2.1 ppm alpha-Mycolic acid C54:2 C541-1,0003 799.7913 799.7902 1.4 ppm alpha-Mycolic acid C55:4 054H10203 809.7756 809.7748 1.0 ppm alpha-Mycolic acid C55:3 C541-110403 811.7913 811.7907 0.7 ppm alpha-Mycolic acid C55:2 C541110603 813.8069 813.8061 1.0 ppm alpha-Mycolic acid C56:5 056H10203 821.7756 821.7748 1.0 ppm alpha-Mycolic acid C56:4 C56E1,0403 823.7913 823.7907 0.7 ppm alpha-Mycolic acid C56:3 C561-110603 825.8069 825.8053 1.9 ppm alpha-Mycolic acid C56:2 055H 10803 827.8226 827.8213 1.6 ppm alpha-Mycolic acid 057:4 C571110603 837.8069 837.8050 2.3 ppm alpha-Mycolic acid C57:3 C57h110803 839.8226 839.8215 1.3 ppm alpha-Mycolic acid 058:5 C531-110603 849.8069 849.8068 0.1 ppm alpha-Mycolic acid C58:4 058H10803 851.8226 851.8218 0.9 ppm alpha-Mycolic acid C58:3 C5811,1003 853.8382 853.8375 0.8 ppm alpha-Mycolic acid C59:3 C591-111203 867.8539 867.8537 0.2 ppm alpha-Mycolic acid C60:4 060H11203 879.8539 879.8537 0.2 ppm alpha-Mycolic acid C60:3 C60E111403 881.8695 881.8683 1.4 ppm Table 6 Table of biomarkers: mycolic acids and their mass spectral signals Identified mycolic acids as detected in different Mycobacterium species.

Compound Sum Exact Exp. Mass formula mass mass Deviation [M-Fli-alpha-Mycolic acid C77:2 077H15003 1122.1512 1122.1525 1.2 ppm alpha-Mycolic acid C78:2 C76H 15203 1136.1669 1136.1684 1.3 ppm alpha-Mycolic acid C79:2 0791-115403 1150.1825 1150.1833 0.7 ppm Epoxy/keto-Mycolic acid C79:1 or C79H 15404 1166.1774 1166.1769 0.4 ppm Methoxy-Mycolic acid C79:2 Epoxy/keto-Mycolic acid 080:1 or C80H15604 1180.1931 1180.1897 2.9 ppm Methoxy-Mycolic acid 080:2 Epoxy/keto-Mycolic acid 081:1 or Ce1hl15803 1194.2087 1194.2102 1.3 ppm Methoxy-Mycolic acid C81:2 Table 7 Table of biomarkers: sphingolipids and their mass spectral signals.

Identified sphingolipid species in members of the Bacteroidetes phylum Formula Experimental Phosphorylethanolamine/Phosphoethanolamine Exact mass Mass Observed in Ceramide mass Deviation Dihydroceramides (PE-DHC) Ca6H74N207P- 677.5253 677.5239 2.0 B. fragilis. B. ovatus, B. thetaiotaomicron, B. uniformis, B. vulgatus, P. bivia, P. distonasis C371±6N207P- 691.5411 691.5396 2.2 C381±8N207P- 705.5569 705.5552 2.4 Ceram ides C34H69N104C1 590.49348 590.4921 2.2 B. fragilis, B. ovatus. B. thetaiotaomicron.

B. uniformis, B. vulgatus, P. bivia, P. distonasis C351-1/IN04C1- 604.5090 604.5077 2.1 C36H73N04C1 618.5246 618.5234 1.9 Bacteroides fragilis a-Galactosylceramides C40H7,N0,C1- 752.5465 752.5449 2.1 B. fragilis C411-161N09C1 766.5623 766.5605 2.3 C42H83N09C1 780.5781 780.5762 2.4 015:0 substituted Phosphoglycerol Dihydroceramides (subPG-DHC) C50H100010NP 904.7007 904.7028 2.3 B. fragilis, B. ovatus. B. thetaiotaomicron.

B. uniformis, B. vulgatus, P. distonasis 0511-1102010NP 918.7163 918.7185 2.4 C.52H104010NP 932.7324° 932.7337 1.4 C53H106010NP 946.7481° 946.7484 0.3 0541-1108010NP 960.7637° 960.7624 1.3 Unsubstituted Phosphoglycerol Dihydroceramides (unPG-DHC) C371-17609NP 708.5184 708.5199 2.1 P. distonasis C39H,300,NP 736.5497 736.5484 1.8 Table 8 Table of biomarkers: quorum-sensing molecules and their mass spectral signals Identified quorum-sensing molecules in Psuedomonas aeruginosa.

Compound Sum Exact mass Exp. Mass formula mass Deviation 2-Heptylquinoline-4(1 H)-one C16H2INI0 [M-Hr = 242.1550 242.1552 -0.8 ppm 2-Heptyl-3-hydroxy-4(1H)- C16H2IN02 [M-Hf = 258.1499 258.1502 -1.2 ppm quinolone (PQS) Hydroxynonenylquinoline C18H23NO [M-Hr = 268.1707 268.1711 -1.5 ppm Hydroxynonylquinoline CI8H25N0 [M-Hr = 270.1863 270.1868 -1.9 ppm Hydroxyundecenylquinoline C20H25NO [M-H]-= 296.2020 296.2023 -1.0 ppm Table 9 Table of biomarkers: Rhamnolipids and their mass spectral signals.

Rhamnolipid species commonly produced by P. aeruginosa strains.

Compound Sum Exact Exp. Mass formula mass mass Deviation [M-FI] Rha-C20 C26H 4809 503.3225 503.3224 0.2 ppm Rha-C221 C28H5009 529.3382 529.3384 -0.4 ppm Rha-C22 C28H 520g 531.3539 531.3538 0.2 ppm Rha-Rha-C20 C32H58013 649.3805 649.3804 0.2 ppm Rha-Rha-C22 C34H6201, 677.4118 677.4116 -0.3 ppm Rha-Rha-022:1 C341160013 675.3961 675.3965 -0.6 ppm Table 10 Table of biomarkers: Surfactins and their mass spectral signals.

Surfactin species detected in positive and negative ion mode for Bacillus subtilis.

Negative ion mode Positive ion mode Compound Exp. mass Exact mass Appm Exp. mass Exact mass Appm Surfactin(C13) 1006.6453 [IVIFI]. 1.3 1030.6389 IIVI+Nal+ 2.6 1006.6440 1030.6416 Surfactin(C14) 1020.6604 1020.6597 0.7 1044.6545 1044.6573 2.7 Surfactin(C15) 1034.6754 1034.6753 0.1 1058.6702 1058.6729 2.6 Table 11 Table of biomarkers: Lichenysins and their mass spectral signals Lichenysin compounds detected in Bacillus licheniformis. Exact

Compound Exp. mass mass [M- Appm

HI

Lichenysin (C13) 1005.6594 1005.6600 0.6 Lichenysin (C14) 1019.6748 1019.6756 0.8 Lichenysin (C15) 1033.6906 1033.6913 0.7 Lichenysin (C16) 1047.7055 1047.7070 1.4 Table 12 Table of biomarkers Mass spectrometric signals that show strong positive correlation with the ugcg gene expression for a cell line (NCI60) dataset.

Exp. mass Exact mass Appm Tentative ID Formula Adduct Correlation coefficient 734.5355 734.5343 0.2 GlyCer(d18:1/16:0) C40h177N08 [M+Ci]- 0.552 818.6295 818.6282 0.2 GlyCer(d18:1/22:0) C46h1891\108 [M-FCI]- 0.662 842.6312 842.6332 -0.2 GlyCer(d18:1/24:2) C481-189N08 [M+Cl] 0.602 844.6451 844.6439 0.1 GlyCer(d18:1/24:1) C431-151N08 [M+0]- 0.668 846.6527 846.6595 0.4 GlyCer(d18:1/24:0) C481-93NO8 [M-FCI]- 0.688 872.6733 872.6752 -0.2 GlyCer(d18:1/26:1) C501-195NO [M+CI] 0.707 Table 13 Table of biomarkers for Mycoplasma List of m/z peak that are significantly higher in Mycoplasma infected samples compared to Mycoplasma free samples in both HEK and HeLa cell lines. Column 2 displays the corresponding binned peak, column 2 highlights putative isotope peaks. while column 4 shows the tentative annotation of the binned peak. Phosphatidylglycerol and sphingomyelin species, that are main Mycoplasma constituents are written in bold.

significantly different corresponding Annotation binned m/z m/z signal 687.54 687.5468 722.51 722.5156 PE(P-36:4) PE(P-38:4) 733.53 733.5231 747.52 747.5193 PG(34:1) 748.53 748.5243 Isotope of m/z = 747.52 753.51 753.5090 PG(P-36:4) 764.52 764.5264 PE(38:5) PE(38:5) 764.53 764.5262 766.53 766.5412 PE(38:4) 773.54 773.5359 PG(36:2) 774.54 774.5391 PG(36:2), Isotope of m/z = 773.54 774.55 774.5391 PG(36:2), Isotope of m/z = 773.54 775.56 775.5520 PG(36:1) 776.56 776.5564 PG(36:1), Isotope of m/z = 775.56 776.57 776.5564 PG(36:1), Isotope of m/z = 775.56 819.52 819.5189 PG(40:7) 820.53 820.5268 PG(40:7), Isotope of m/z = 819.52 820.54 820.5268 PG(40:7), Isotope of m/z = 819.52 Table 14: Table of biomarkers: microbial taxon-specific biomarkers Taxon-specific markers obtained for various microbes. No markers were calculated where the size of sample set was insufficient.

Gram Bacteroid Bacteroidetes Bacteroidates etes 616.5094 negat 381.2765 617.5124 ive 393.2764 618.5233 590.4923 619.5273 591.4963 620.5184 592.488.3 627.4883 604.5083 628.4913 605.5113 635.5004 606.5033 636.5044 616.4724 637.5044 623.5024 644.5033 624.5054 648.5003 637.5044 697.5743 639.4954 698.5763 640.4993 711.5902 653.5113 712.5933 654.5143 677.5238 691.5395 705.5562 Bacteroidaceae 576.4764 820.7522 Pa rphyro monad aceae 814.7063 815.7112 828.7232 829.7262 840.6842 841.6942 843.7432 854.7022 858.6972 872.7072 908.7401 909.7431 910.7471 918.7191 921.7912 932.7332 933.7362 934.7422 944.7342 945.7372 946.7472 947.7502 948.7562 949.7592 958.7461 959.7501 960.7611 961.7661 962.7691 Prevotellaceae 661.5283 675.5453 676.5503 870.8002 Bacteroides Parabactero des Prevotell Porabacteroides distasonis Parabacteraides johnsonii Bacteroides acidifaciens Bacteroides curare Bacteroides eggerthil Bacteroides fragiiis Bacteroides helcogenes Bacteroides ovatus Bacteroides pyogenes Bacteroides thetaiotaomicron Bacteroides uniform's Bacteroides vulgatus Prevotefia bivia 908.7401 922.7552 923.7612 953.5113 Rikenellaceae Aiistipes Alistipes onderdonkii 1 Flavobacteria Flavobacterial Flavobacteriace Chryseobacte Chryseobacterium 3 324.2545 es as rium indologenes 1 333.2084 Chryseobacterium sp 390.2324 Elizabethking Elizabethtonal° 4 392.2484 is meningoseptica 393.2504 Myroides Myroides odoratimimus 2 552.4643 553.4674 553.4674 554.4714 556.4034 565.4654 566.4794 567.4834 568.4864 600.4664 601.4723 618.4773 619.4813 620.4883 651.4953 651.4953 891.7411 Fusobacte Fusobacteria Fusobacterial Fusobacteriacea Fusobacteriu Fusobacterium 3 ria es e m gonidiaformans 7 227.2015 Fusobacterium 4 644.4652 necrophorum 1 645.4633 Fusobacterium 646.4833 peridontiam 647.4812 Fusobacterium sp 648.4832 673.4443 696.4953 714.5492 856.6782 865.6632 884.7083 Proteobac Alpha- Caulobacteral Caulobacteracea areyundimon Breyundimonas diminuta 2 teria Proteobacteria es e as 768.5182 769.5502 782.5342 770.5562 783.5293 771.5582 795.5572 797.5723 818.5673 957.6261 Rhizobiales Rhizobiaceae Rhizobium Rhizobium radiobocter 5 439.4155 440.4195 739.5313 784.5902 785.5932 799.5132 Rhodospirillal Acetobacterace Roseomonas Roseomonas mucosa 6 es ae Roseomonas sp 1 662.5393 722.5753 729.5813 733.5752 733.6173 734.5753 747.6283 757.6173 Beta- Burkholderial Alcal igenaceae Achromobact Athromobacter sp 3 Proteobacteria es er Achromobacter 3 xylosoxidons Alcaligenes Alcaligenes faecolis Burkholderiacea Burkholdeda Burkholderia cepacia 7 e complex 589.4013 590.4083 591.4184 592.4214 Comamonadace Acidovorax Acidovorax tempera as 2 ae Comamonas Comamonas kerstersil 2 520.3044 Comamonas sp 1 Delta Delftia acidovorons 4 Delftia dentocariosa 1 Delftio sp 2 Sutterellaceae Sutterella Sutterella 2 Neisseriales Neisseriaceas Eikenella Eikenella corrodens 1 494.3855 Kingella Kingelia kingae 502.3674 Kingello sp 1 526.3673 Neisseria Neisseria cineria 1 527.3704 Neisseria eiongata 2 528.3653 Neisseria flavescens 3 544.3774 Neisseria gonorrhoea 4 Neisseria loctamica 3 Neisseria meningitidis 4 Neisseria mucoso 2 Epsilon- Cam pyl obacte Campylohactera Campylobact Campylobacter coli 1 Proteobacteria ra les ceae er Campylobacter fetus 3 730.5422 867.6582 Campylohacter fefuni 3 731.5452 993.8381 Compylobacter sp 6 867.6582 Helicobacterace Heficobacter Heficobacter pylori 3 993.8381 ae 271.2284 272.2305 299.2595 300.2625 400.2644 543A623 544.4634 Gamma- Aeromonadal Aeromonadacea Aeromonas Aeromonas hydrophila 1 Proteobacteria es e Cardiobacteri Cardiobacteriac Cardiobacteri um Cardiobocterium hominis 4 ales eae 648.4603 649.4623 650.4653 793.4792 794.11802 Enterobacteri Enterobacteriac Citrobacter Citrobacter arnalonaticus 1 ales eae Citrobacter braakii 3 702.5083 Citrobacter freundii 4 703.5092 Citrobacter koseri 4 993.7282 Enterobacter Enterobacter absuriae rr. Cr 994.7272 Enterobacter oerogenes Enterobocter omnigenus Enterobocter cloacae Enterobacter gergovioe Escherichia Eschertchia col; Hafnio Hafnia alvel Hafnia paralvei Hafnia sp Klebsiella Klebsiello oxytoca 5 Klebsiella pneumonioe 5 Morganella Morganella morganii 7 Panthoea Ponthoea sp 1 Proteus Proteus mirabilis 5 Proteus vulgaris 5 Provedencia Provedencia rettgeri 2 Provedencia stuartil 2 Raouitella Raoultelia ornithololytica 1 Raoultella planticola 1 Salmonella Salmonella poona 1 Serratia SerratM figuifaciens 3 Serra tie morcescens 5 Shigella Shigella sonnet 1 Pasteurellales Pasteurellaceae Aggregatibac Aggregotibacter 5 690.4983 ter aphrophilus 746.4503 Haemophilus Haemophilus infiuenzae 5 823.5453 Haemophilus 2 898.6921 parahoemoiyticus 1 915.6902 Haemophilus 977.7282 parainfluenzae Pasteurella Pasteurella multocida 2 Pseudomonad Moraxellaceae Acinetobocte Acinetobacter baurnanii S ales r Acinetobacter twoffil 5 Acinetobacterjohnsonii 2 Acinetobacter junii 1 Pseudomonadac Moraxelia Moraxella catarrhalis 5 eae Pseudomona Moraxelia osloensis 2 286.1805 Pseudomonas 7 490.3304 aearuginosa 1 514.3294 Pseudomonas luteola 2 2 1 5 Pseudomonas monteilii Pseudomonas oryzihabitans Pseudomonas putida Pseudomonas stutzeri Vi b rio na les 605.3823 607.3983 608.4013 633.4134 Vibrionaceae Vibrio Vibrio atginolyticus 1 Vtbrio chola-de 1 Vibrio furnissii 1 Xantho monad Xa nthonn on ad ac eae Stenotropho Stenotrophomonas 7 ales 377.2105 562.3504 619.4353 620.4384 705.4713 706.4743 929.6852 930.6892 942.6912 943.7012 944.7052 men as maltophilia Gram Actinobac Acti nobacteria Actinomytetal Actinomycetace Actinobaculu Actinobaculum schaalli 2 ten a es ae rn paMU 757.5.403 Actinomyces Actinomyces graevenitzii 1 ve 879.6112 Actinomyces israelii 1 Actinomyces 2 odontolyticus 5 Actinomyces oris 1 Actinomyces sp 1 Actinomyces turicensis 2 Actinomyces viscosis Corynebacteriac Corynebacter Corynebocterium 2 eae turn afermentans 3 493.4624 Corynehacterium 2 495.4784 amycolatum 3 497.4845 Corynehacterium 1 521.4934 diphtherthe 5 535.4734 Corynebocterium imitans 3 537.4904 Corynebacterium 538,4934 minutissimum Corynehacterium sp Corynehacterium striatum Microbacteriace Microbacteri Microbacterium sp ae um Mycobacteriace Mycobacteri Mycobacterium OViilin 2 ae um Mycobacterium fortuitum 1 391.3684 427.0965 724.8873 817.4152 850.5592 851.5662 852.5672 Mycobacterium 1 pert grium Nocardiaceae Nocartha Nocardia sp 321.2915 Rhadococcus Rhodococcus equi 1 743.7273 RhOdOCOCCIIS sp 2 771.7592 797.7762 798.7762 800.7962 827.8162 828.8222 970.7871 Propi on 'bacteria ceae Pmpionibacr Propionibacrerium acmes 7 edam 361.2155 617.4564 713.4752 714.4812 779.5072 877.5592 906.5872 Bifidobacteria Bifidobacteriace Bifidobacteri Bifidobacterium 1 les ae um adolescentis 2 789.5293 Bifidobacterium bifidum 3 792.5502 Bifidobacterium breve 1 819.5783 Bifidobacteriurn infantis 3 830.5622 Bifidobacterium longue) 2 855.5272 Bifidobacterium 884.6092 pseudocatenuratum 885.6142 Gardnerella Garcinerella vaginalis 2 Micrococcales Micrococcaceae Arthrohacter Arthrobacter 1 913.5682 913.5682 creatinolyticus 1 914.5711 Arthrobacter sp 915.5671 Kokuria Kokuria kristina 2 Kokuria rhizophila 2 Kokuria various 1 Micrococcus Micrococcus luteus 5 Micrococcus iylae 2 Rothia Rothia (zebu 3 Rothia omarne 1 Rothia dentocariosa 5 Rothia mud/op/rasa 5 Rothia sp 1 NA icrocaccineae Brevibacteriu Brevibacterium 1 m poucivorans 3 Brevibacterium sp Dermabacter Dermabacter hominis Dermohacter sp Firmicutes Bacilli Bacillales Bacillaceae Lisreriaceae 675.9793 832.5352 Bacillus (steno Bacillus cereus 3 Bacillus clausii Bacillus lichenformis Bacillus pumilus Bacillus sonorensis Bacillus sp Bacillus subtilis Listena monocytogenes 1 1 3 3 7 Paenibacillaceae 271.5892 903.7221 914.7282 915.7282 916.7282 Staphylococcace ae 763.5512 765.5482 Paenibaciflus sp Paenibacillus onalis Staphylococcus aureus Staphylococcus capitis 3 Staphylococcus caprae 1 Staphylococcus cohn/i 4 Staphylococcus epidermis 3 Staphylococcus 3 haemolyticus 3 Staphylococcus hominis 3 Staphylococcus 3 lugdunensis 3 Staphylococcus pasteuri 3 Staphylococcus 3 pettenkoferi Staphylococcus saprophyticus Staphylococcus warner! Paenlbacillus Staphyiococc us Lactobacillale Aerococcaceae Abiotrophia Abiotrophia defectiva 163.0506 898.5391 923.5512 925.5671 926.5701 928.5952 949.5672 950.5692 951.5832 952.5861 953.5981 954.6011 955.5971 956.5971 979.6111 990.6001 Aerococcus Aerococcus sp Aerococcus viridans 2 C nobacteriace Granulicatell Gronulicatello adiocens ae a Enterococcacea Enterococcus Enterococcus (SWUM Enterococcus 2 casseliflavus Enterococcus cecorum 3 Enterococcus faecalis Enterococcus faecium 3 Enterococcus gallinarum 3 Enterococcus raffinosus Lactobacil laceae Lactococcus Lactococcus loctis Loctococcus spp 2 Leuconostocace euconostoc Leuconostoc sp ae Streptococcacea Lactobacillus Lactobacillus gasseri 2 Lactobacillus rhamnasus 3 Clostridia 449.2685 703.4923 704.4953 731.5253 732.5283 925.7262 Clostridiales Streptococcu Clostridium Streptococcus agalactiae Streptococcus anginosus Streptococcus bovis Streptococcus cans Streptococcus constellatus Streptococcus cristatus Streptococcus dysagalactiae Streptococcus gairolyticus Streptococcus gordonii Streptococcus intermedius Streptococcus lutetiensis Streptococcus miller! Streptococcus otitis Streptococcus mutans Streptococcus oralis Streptococcus parosonguinus Streptococcus pneumonioe Streptococcus povas Streptococcus pseudoporcinus Streptococcus pyogenes Streptococcus salivarius Streptococcus sanguinis Streptococcus vestibularis Streptococcus Wildcats Clostridium 1 celerecrescens 4 Clostridium difficile 2 Clostridium histolyticum 3 Clostridium innocuum 2 Clostridium 3 paraputrificum 3 Clostridium perfringens 2 Clostridium ramosum 2 Clostridium septicum 3 Clostridium sporogenes Clostridium tertium 897.5351 Clostridiaceae 649.4453 731.5253 897.6951 925.7262 969.7481 970.7541 Negativicutes 423.3505 425.3644 Selena monad ales Peptostreptococ caceae 496.4124 497.4214 498.4244 635.3944 645.4133 645.4173 681.3923 Acida mi nococca ceae 627.4403 Pananomas Peptoniphilus Acidaminoco ccus Parvinomas micro Peptoniphilus hare! Acidaminococcus fermentans 426.3674 643.4343 461.3394 644.4383 560.4194 730.4652 851.7352 734.5933 831.5902 977.6971 978.6931 Veillonellaceae Diniister Diraister sp 1 218.1855 Veiltanello Vellionella atypica 1 229.1815 Veillonello dispar 1 358.2145 Veillanella paryula 1 364.2495 Veilloneila ratti 1 655.4713 Table 16. Taxon-specific markers as determined on phylum-leVel.

Phylogenetic Taxonomic level ink value Compound ID information Gram-negatives Badteroidetes (Phylum) 381..2765 653.5113 spingolipid 854.5143 Isotope al/Z=653 623.5024 640.4993 639.4954 393.2764 616.4724 CerP(d34: 1)) 624.5054 isotope m/z=623 637.5044 isotope m/z=635 592.4883 isotope m/z=590 604.5083 Celd18:0/h17:0) 605.5113 isotope m/z=604 606.5033 isotope m/z=604 590.4923 Cer(d34:0(20H) 591.4963 isotope m/z=590 705:5562 PE-DHC 691.5395 PE-DHC 677.5238 PE-DHC Fusobacteria (Phylum) 646.4833 PE plasmalogen 227.2015 648.4832 856.6782 865.6632 696.4953 PE plasmalogen 714.5492 673.4443 644.4652 884.7083 645.4633 combinatorial marker 647.4812 with m/z=227 Proteobacteria 768.5182 782.5342 783.5293 Gram-positives Actinobacteria - Firmicutes -Table 17. Taxon-specific markers as determined on class-level.

Phylogenetic Taxonomic level m/z value Compound ID information Gram-negatives LBacteroidetes Bacteroidetes 635.5004 sphingolipid 616.5094 Cer(d36:1(20H)) 628.4913 636.5044 627.48B3 PE-Cer(33:1) 644.5033 711.5902 CerP(d36:1) 618.5233 Cer(d36:0(20H)) 712.5933 619.5273 isotope 618 697.5743 DG(42:5) 620.5184 698.5763 648.5003 637.5044 617.5124 isotope miz=616 Flavobacteria 333.2084 390.2324 566.4794 567.4834 568.4864 556.4034 600.4664 565.4654 553.4674 392.2484 651.4953 619.4813 324.2545 620.4883 393.2504 891.7411 554.4714 552.4643 553.4674 651.4953 601.4723 Gram-negatives I-Fusobacteria Fusobacteria (class) Gram-negatives L-Proteobacteria Alpha-Proteobacteria 131) Beta-Proteobacteria -Epsilon-Proteobacteria 993.8381 867.6582 731.5452 730.5422 Gamma-Proteobacte-ia - Gram-positives Actinobacteria (class) -LActinobacteria Gram-positives Bacilli -I-Firmicutes Clostridia 731.5253 PG plasmalogen 732.5283 Isotope miz=731 449.2685 703.4923 PG plasmalogen 704.4953 Isotope m.4=703 Negativicutes 560.4194 426.3674 Isotope miz=425 425.3644 461.3394 851.7352 Table 18. Taxon-specific markers as determined on order-level.

Phylogenetic information Taxonomic level rntz value C mpound ID Gram-negatives I-Bacteroidetes Bacteroidales I-Bacteroidetes Gram-negatives I-Bacteroidetes Flavobacteriales LFlavobacteria Gram-negatives I-Fusobacteria Fusobacteriales LFusobacteria Gram-negatives I-Proteobacteria lalpha-Proteobacteria Caulobacterales 795.5572 797.5723 769.5502 770.5562 957.6261 771.5582 818.5673 Rhizobiales 739.5313 784.5902 785.5932 Isotope nili=784 439.4155 440.4195 Isotope mfr-439 799.5132 Rhodospiralles 733.5752 734.5753 729.5813 733.6173 722.5753 662.5393 757.6173 Gram-negatives Burkholderibles -I-Proteobacteria Neisseriales I-Beta-Proteobacteria 526.3673 527.3704 Isotope m/z=526 502.3674 544.3774 494.3855 528.3653 Gram-negatives Cam pylobacterales -I-ProteobaCteria I-Epsilon-Proteobacteria Gram-negatives Aeromonadales I-Proteobacteria I-Gamma-Proteobacteria Cardiobacterales 648.4603 649.4623 793.4792 650.4653 794.4802 Isotope na/z=648 Enterobaderiales 703.5092 702.5083 Isotope ra/z=702 993.7282 994.7272 Pasteurellales 746.4503 915.6902 823.5453 898.6921 690.4983 977.7282 Ptetidomonedales Vibrionales 607.3983 608.4013 Isotope ra/z---607 633.4134 605.3823 Xanthomonadales 562 3504 377.2105 619.4353 620,4384 Isotope ra/z=619 930.6892 Isotope riz=629 929 6852 944 7052 Isotope rilz=643 943 7012 706,4743 Isotope n/z=705 7054713 PG(31:1) Gram-positives Aptinomycetales 1-ActInobacteria Bifidobacteriales I-Actinobacteria 792.5502 819.5783 884.6092 885.6142 789.5293 830.5622 855.5272 Micrococcales 913 5682 Gram-positives BadHales L-Firmicutes Lactobacillales LBacilli 951.5832 954.6011 952.5881 953.5981 925.5671 956.5971 955.5971 928.5701 950.5692 949.5672 928.5952 990.6001 923.5512 898.5391 979.6111 Clostridiales Selemonada les Table 19. Taxon-specific markers as determined on family-level Phylogenetic information Taxonotnic level rniz value Compound ID Gram-negatives I-Bacteroidetes Bacteroidaceae I-Bacteroidetes 820.7522 I-Bacteroidales Porphyromonadazeae 841.6942 isotope m/z=840 840.6842 948.7582 isotope m/z=946 949.7592 isotope mk=946 947.7502 isotope m/z=946 946.7472 SubPG DHC 945.7372 isotope m/z=944 944.7342 SubPG DHC 933.7362 isotope m/z=932 932.7332 SubPG DHC 872.7072 815.7112 isotope m/z=814 814.706.3 858.6972 934.7422 962.7691 isotope m/z=960 960.7611 SubPG DHC 961.7661 isotope m/z=960 828.7232 829.7262 isotope m/z=828 854.7022 959.7501 isotope m/z=958 958.7461 921.7912 918.7191 843 7432 910.7471 908 7401 909 7431 Prevotellaceae 661.5283 908.7401 675.5453 922.7552 923.7612 676.5503 870.8002 Rikenellaceae Gram-negatives I-Bacteroidetes i-Flavobacteria Flavobacteriaceae I-Flavobacteriales Gram-negatives I-Fusobacteria I-Fusobacteria Fusobacteriaceae I-Fusobacteriales Gram-negatives I-Proteobacteria I-Alpha-Proteobacteria Ca ulobacteraceae I-Caubbacterales Gram-negatives I-Proteobacteria LAlpha-Proteobacteria I-Rhizobiales Rhizobiaceae Gram-negatives I-Proteobacteria I-Alpha-Proteobacteria Acetobacteraceae I-Rhodospiralles Gram-negatives I-Proteobacteria I-Beta-Proteobacteria Alcaligenaceae I-Burkholderiales Burkholderiaceae 589.4013 591.4184 590.4083 592.4214 Isotope m/z=589 isotope rritz=591 Comamonadaceae 520.3044 Sutt rellaceae -Gram-negatives I-Proteobacteria I-Beta-Proteobacteria I-Neisseriales Neisseriaceae Gram-negatives I-Proteobacteria I-Epsilon- Campylobacteraceae 993.8381 Proteobacteria I-Campylobacterales 867.6582 Helicobacteriaceae 299.2595 300.2625 272.2305 271.2284 543.4623 C18:0(+0) Isotope m/z=299 Isotope rtt/z=271 C16:0(+0) 400.2644 544.4634 Gram-negatives I-Proteobacteria I-Gamma-Proteobacteria Cardiobacteriaceae I-Cardiobacterales Gram-negatives I-Proteobacteria Enterobacteriaceae I-Gamma-Proteobacteria LEnterobacterales Gram-negatives I-Proteobacteria Pasteureliacese I-Gamma-Proteobacteria I-Pasteureilales Gram-negatives Moraxellaceae -LProteobacteria Pseudomonadaceae I-Gamma-Proteobacteria 514.3294 I-Pseudomonadales 490.3304 266.1805 Gram-negatives I-Proteobacteria Vibrionaceae I-Gamma-Proteobacteria LVibrionales Gram-negatives I-Proteobacteria Xanthomonadaceae.

I-Gamma-Proteobacteria I-Xanthomonadales Gram-positives Actinomyceteae LActinobacteria 757.5403 Comt4ratorial LActinobacteria I-Actinomycetales Si9.6112 markers Corynebacteriaceae 537.4904 Mycolic acid. C35:0 538.4934 Isotope tn/z=537 535.4734 Mycolic acid C35:1 493.4624 Mycolic acid C32:1 495.4784 Mycolic acid C32:0 4,97,4845 laotope m/z=495 521.4934 Mycolic acid C34:1 Microbacteriaceae Mycobacteriaceae 851.5662 P1(35:0) 852.5672 Isotope m/z=851 850.5592 391.3684 724.8873 427.0965 817.4152 Nocardiaceae 798.7762 797.7762 828.8222 970.7871 321.2915 827.8162 800.7962 Isotope m/z=797 743.7273 Mycolic acid C54:3 771.7592 Isotope m/z=827 combinatorial Mycolic acid 056:2 Isotope Mycolic acid C54:2 Mycolic acid 0502 Mycolic acid 052:2 Propionibacteriaceae 617.4564 906.5872 779.5072 361.2155 713.4752 877.5592 Gram-positives Bifidobacteriaceae I-Actinobacteria 792.5502 I-Actinobacteria 819.5783 I-Bifidobacteriales Gram-positives Micrococcaceae LActinobacteria 913 5682 I-Actinobacteria 914 5711 i Isotope rn/z=913 LMicrococcales 915 5871 Micrococcineae i Gram-positives Bacillaceae i LFirmicutes Listeriaceae I-Bacilli 675,9793 I-Bacillales 832.5352 Paenibacillaceae 915.7282 914.7282 871.5692 903.7221 Staphylococcaceae 765.5482 Isotoposm/z=763 763,5512 PG(35:0) Gram-positives Aeroccccaceae I-Firmicutes 163.0506 LBacilli Carnobacteriaceae I-Lactoacillales Enteroc,occace e Lactobacillaceae -Leuconostocaceae Streptococcaceae 897.5351 Gram-positives Clostridiaceee 1Firmicutes 731.5253 I-Clostridia 970.7541 I-Clostridiales 649.4453 897.6951 969.7481 925.7262 Peptostreptococcaceae 497.4214 498.4244 Isotope mk=497 681.3923 635.3944 496.4124 645.4133 646.4173 Isotope m/z=645 Gram-positives Acidarninococcaceae I-Firmicutes 7304652 LNegativicutes 627.4403 I-Selemonadales 831.5902 977.6971 978.6931 643.4343 644.43§3 734:5933 Veillonellaceae 229.1815 218.1855 364.2495 655.4713 358.2145

Table 20

m/z IDs CD ANOVA pVal ANOVA qVal Healthy EC HO (Mean) SC (Mean) SA (Mean) MedFC-HO-SC MeanFCHO-SA (Mean) PE(P- 756.5955 38:1) SC 0.03335362 1 0 0.001 2.9186 0.6746 11.51106078 9.397888508 865.5746 P1(36:0) SC 8.99775E-06 0.000181998 4.2331 0.2857 16.469 3.3348 5.849106111 3.545027299 747.4995 PA(40:6) SC 0.000029587 0.000487705 0 0.8051 33.1513 236009 5.363753646 4.836378514 882.5255 PS(44:10) SC 2.09342E-06 4,83105E-05 1.2377 0.7999 17,5562 3.0372 4,456017148 1,924850356 729.5466 PA(36:1) SC 0.000187847 0.00232043 0 0.6001 7.3836 2.2515 3.621049563 1.907611643 8366385 P5(40:5) SO 0,000227757 0.002766326 11.8159 4.1195 50.29 12.0226 3.609730406 1.545207779 907.5386 P1(403) SC 0.001565923 0.01540735 0 0.2976 3.3043 0.5694 3.472898245 0.936067967 721.5045 PG(32:0) SC 2.591E-07 7.14918E-06 6.4138 1.2772 13.6359 2.3595 3.416353581 0.885496713 725.5165 PA(38:3) SC 0.001014647 0.01044902 8.9208 5.815 53.9985 45.8083 3.200258575 2.962948267 890.5915 PS(44:6) SC 8.93408E-05 0.001229222 0.7119 1.1554 10.2085 1.4504 3.143306593 0.32805843 TG(P-5820)! 889.5745 P1(38:2) SC 2.89892E-07 7.87048E-06 5.4933 5.5739 48.3826 176337 3.117729275 1.661576196 PE(P- 720.5005 36:5) SC 3.9936E-05 0.000627725 9.821 3.4014 27:6954 6.804 3.025445795 1.000254466 798.6055 PE(40:2) SC 4.95954E-07 1.28467E-05 0 0.9016 7,193 1.6266 2.996034183 0.851300098 864.5816 PS(42:5) SC 8.48139E-07 2.07019E-05 62.7448 11.6176 91.8789 28.7887 2.983421521 1 1.30919058 816.5585 PE(42:7) SC 1.52034E-10 7.71875E-09 0 1.6337 12.8847 4.1834 2.979443959 1.356532867 881.5234 P1(38:6) SC 3.06222E-07 8.22587E-06 20.1924 5.8436 44.2926 10.5851 2.922136354 0.857105566 909 5536 P1(40.6) SC 2.24965E-12 1.70469E-10 49.0519 15.9328 114.7333 36.869 2.848212447 1.210408461 762.5125 PE(38:6) SC 8.97872E-05 0.001232026 52.1501 10.1699 70.944. 40.8521 2.802375162 2.006104749 796.5915 PE(40:3) SC 3.79122E-06 8.22564E-05 5.1935 4.9805 33.6477 20.3448 2.756145403 2.030297609 818.5755 PE(42:6) SC 0000301686 0.003521058 0.3887 2/777 149853 7.765 2317898322 1769408185 688.4956 PE(32:1) SC 0.004342958 0.03875079 0 1.5169 9,7615 1.7478 2.685976876 0.204414127 PE(P- 698.5165 34:2) SC 2.68247E-05 0.000443613 15.976 10.0868 6D.439 12.0642 2.583011233 0.258263694 730.5425 PE(35:1) SC 9.22832E-07 2,22048E-05 3.0568 3.4431 18.7517 6,1662 2,445241406 0.840673601 863.5705 P1(36:1) SC 7.6787E-07 1.89247E-05 176 9816 37.4607 200 7699 75.9336 2.422093229 1.019360549 671.4685 PA(34:2) SC 0.005222022 0.04586887 1.5406 1.0287 5 4109 2.2209 2.395046269 1.110322124 860.5435 PS(42:7) SC 1.01514E-08 3.60408E-07 11.4123 9.893 48.7253 18.1579 2.3001.91086 0.876117379 862.5576 PS(42:13) SC 2.93214E-09 1.20052E-07 65.6885 24.024 111.5443 49.95 2.215068508 1.056008298 888.5745 P5(44:7) SC 2.70063E-09 1.12386E-07 135.8857 64.5795 280.929 113.4454 2.121057384 0.812849935 859.5395 P1(36:3) SC 1.31586E-08 4.51393E-07 77.7761 26.7501 109.2902 47.0364 2.030547851 0.814233361 752.5645 PE(P- SC 0.000105486 0.001401967 9.7931 17.5824 70.3579 40.0642 2.000580411 1.188181657 38:3) 699.5004 PA(36:2) SC 2.16751E-06 4.93473E-05 21.1742 21.3245 839542 57.5307 1.977090586 1.431820109 697.4845 PA(36:3) SC 0.003052866 0.02802785 2.6456 1.9677 7.6299 2.813 1.955153868 0.515599272 807.5075 P1(32:1) SC 0.0015283 0.01506637 1.2143 3.5478 13.544 2.8181 1.93265729 -0.332201876 724.5235 PE(P- SC 3.34117E-08 1.07361E-06 13.3679 24.4632 90.6672 36.2425 1.889967599 0.567069342 36:3) 728.5635 PE(P- SC 5.35704E-08 1.6893E-06 75.1233 24.7786 90.3378 34.6904 1.866235103 0.485441799 36:1) 772.68913 PE(38:1) SC 6.81536E-05 0.001008793 79.1078 42.7078 147.5674 109.7266 1.788802554 -1.36134182 861.5535 P1(36:2) SC 1.11286E-08 3.86985E-07 116.1198 72.7123 249.9238 138.848 1.781216958 0:93323506 788.5254 PE(40:7) SC 2.1'3379E-06 4.87984E-05 19.535t 17.2225 52.4945 26.0242 1.607871697 0.595559237 820.5906 PE(42.5) SC 0.000846836 0.008846474 0 4.7141 13.8708 7.9687 1.55699673 0.757362022 720..5470 PE(P- SC 2.57574E-07 7.14592E-06 85.966 33.61/3/ 95.8155 34.6192 1.515292862 0.04660619 36.2) 770.5735 PE(38:2) SA 2.30565E-06.5.20258E-06 152.3456 128.0562 325.133 327.1652 1.344252888 1,353242195 690.5105 PE(32:0) SC 0.004089558 0.03681327 1.8308 9.2329 23.0854 13.957 1.322124964 0.596133107 740.5284 PE(36:3) SC 9.15878E-07 2.21424E-05 56.0117 58.7099 143.5809 71,9384 1.290188141 0.293158273 768.5585 PE(38:3) SC 1.58943E-05 0.000287172: 191.6569 129.141 290.4791 253.9433 1.169487262 0,975559307 911.5704 P1(40:5) SC 7.21236E-08 2.24646E-06 52.6973 - 40.8993 85.8625 42.5665 1.069952028 0.0 7642319 723.4995. PA(38:4) SA 0.00037395 0.004275998 7.4888 20.0312 41.4992 62.2382 1.050834675 1.635551486 742.5424 PE(36:2) SC 2.87707E-06 6.29608E-05 395.1418 345.5038 692.7696 581.6404 1.003674045 0.751425992 101.6166 PA(36:1) SA 1.406E-05 0,000272453 104.0595 173.5574 343.5916 393.7826 0.984450877 1.181165475 714.5105 PE(34:2) SC 2.38041E-06 5.3475E-05 21.3/64 38.1989 75.3044 29.4042 0.979203069 -0.377508854 744.56/6 PL(38:1) Sc; i U.0011434216 0.004834484 782.4336 6034562 10196619 1009.082 0.756769896 0.741723593 P6(42:1) SC 01101341361 8 PE(36:0) SC 0.000407838 872.6425 0.0133/935 2.6642 9.756 16.3726 4.596 0.746921779 -1.08591096 746.5755 0.004601323 37.397 47.869 79.349 55.9679 0.729120374 0.225507949 PG(P- 819.5536 41:6) HO 3.34792E-05 0.000543048 41.2026 48.6631 23.5767 17.1004 1.045466427 -1.508798156 PE(381:2) / 816.5805 PS(38:1) HO 1.26476E-08 4.36814E-07 71.9373 73.9657 35.4711 22.4157 1.060212336 -1.722346854 1310.769 1946.645 1068.145 788.5475 PS(36:1) HO 1.24345E-14 1.34319E-12 5 7 887.8471 7 1.132607179 -0.865881879 7495355 PG(34:0) HO 2.26439E-11 1.40199E-09 246.3929 344.2116 150.2673 151.2288 1.195764622 -1.186562805 PE(P- 748.5325 38:5) HO 3.12647E-11 1.84571E-09 511.038 745.9556 301.8235 364.2202 1.305384624 -1.034278825 868.6124 PS(42:3) HO 0.000341653 0.003960214 0 4.6648 1.713 0.4246 1.445290076 -3.457638952 PE(38:3) / - 814.5655 PS(38:2) HO 3.33067E-16 4.57022E-14 45.6966 106.7905 25.2074 25.4809 2.082864087 -2.067295171 Pl(P- 847.5665 36:1) HO 1.09395E-07 3.30593E-06 6.2006 13.3224 3.044 2.4174 -2.12981374 -2.462325888 PS(P- 846.5635 42:6) . HO 5.18541E-12 3.60634E-10 24.6104 30.5856 4.4186 7.6053 2.791191338 -2.007775515. PE(P-

724.5325 36:3) ' ' HO 9.14935E-13 7.25801E-11 32.8176 65.1551 7.4891 8.0302 3.121013851 -3.020370285 PS(P- - 818.53,18 40.8) HO 2.22045E-16 3.22092E-14 37.1244 38.8126 40168 5.1328 3 272406545 -2.918707128 SC= Serous carcinoma; HO= Healthy ovary; SA = StromaA; CD= Class Diff Number of lipids PA 4 PE 14 PI 4 PS 9 PG 3 Class Diff class where the p value is significant HealthyEC (Mean) mean intensity of epithelial cells from Fallopian. tube HealthyOv (Mean) mean intensity of healthy stroma -Serousearcinoma (Mean) mean intensity of cancer cells from Serous adenocarcinomas StromaA (Mean) mean intensity of cancer associated stroma MeanFC-Healthy0v-SerousCarcinomafold change of mean -log(SerousCarcinoma/HealthyOv) MeanFC-HealthyOv-StromaA fold change of mean -log(StromaA/Healthy0v) -141 -Table 20: Cancer-related biomarkers A numbered list of embodiments (E) will now be described: E-1. A method of analysis using mass and/or ion mobility spectrometry comprising: a) using a first device to generate aerosol, smoke or vapour from one or more regions of a first target of biological material; and b) mass analysing and/or ion mobility analysing said aerosol, smoke, or vapour, or ions derived therefrom so as to obtain first spectrometric data, wherein said biological material is a human subject, a non-human animal subject, or a specimen derived from said human or non-human animal subject.

E-2. The method of embodiment 1, further comprising (c) analysing said spectrometric data in order to analyse one or more of the following in relation to the one or more regions of the target biological material: (i) determine the grade, type or subtype of a cancer or tumour; (ii) determine the grade, severity, stage, presence or absence of a disease; (iii) determine the phenotype and/or genotype of one or more cells; (iv) detect the level, type, presence or absence of necrosis; (v) determine the type, level, presence or absence and/or genotype and/or phenotype of one or more microbe; (vi) analyse a microbial interaction with a tissue; (vii) analyse dysbiosis; (viii) determine the type, level, presence or absence of a compound and/or biomarker; (ix) analyse the status of a tissue; and/or (x) identify and/or display a margin between two different tissue types and/or between diseased and healthy tissue.

E-3. The method of embodiment 1 or embodiment 2, wherein the specimen is a surgical resection specimen, a biopsy specimen, a xenograft specimen, a swab, a smear, a body fluid specimen, or a faecal specimen, and/or wherein the biological material is in vivo or ex vivo tissue.

E-4. The method of any one of embodiments 1 to 3, comprising determining the severity, grade, stage, presence or absence of a disease in said one of more regions of the target based upon the spectrometric data.

E-5. The method of embodiment 4, wherein the severity, grade, stage, presence or absence of the disease is determined by analysing said spectrometric data to determine the type, level, presence or absence of a biomarker for said disease.

E-6. The method of any preceding embodiment, wherein a disease, or the disease, is diagnosed based upon the spectrometric data.

-142 -E-7. The method of any preceding embodiment, wherein the prognosis of a disease, or the disease, is determined based upon the spectrometric data and optionally the subjects having the disease are stratified according to said prognosis.

E-8. The method of any preceding embodiment, wherein the likelihood of a disease, or the disease, responding to treatment is predicted based upon the spectrometric data and optionally the subjects having the disease are stratified according to said likelihood E-9. The method of any preceding embodiment, comprising determining the distribution of a disease, or the disease, in the target based upon the spectrometric data.

E-10. The method of any preceding embodiment, wherein the target is, or is from, a margin between healthy and unhealthy tissue.

E-11. The method of any preceding embodiment, comprising determining a margin in the target between a disease, or the disease, and a region of the target not having the disease based upon the spectrometric data.

E-12. The method of any preceding embodiment, comprising determining the severity, grade, stage, presence or absence of a cancer, tumour cell or tumour tissue in said one of more regions of the target based upon the spectrometric data.

E-13. The method of embodiment 12, wherein the severity, grade, stage, presence or absence of the cancer, tumour cell or tumour tissue is determined by analysing said spectrometric data to determine the type, level, presence or absence of a biomarker for said cancer or tumour.

E-14. The method of embodiment 12 or 13, comprising determining whether or not the tumour is benign, malignant, and/or metastatic based on the spectrometric data.

E-15. The method of embodiment 12, 13 or 14, wherein said one or more regions of the target comprises or consists of a tumour stroma; optionally wherein the tumour stroma may be analysed repeatedly and intermittently to determine stromal changes.

E-16. The method of any one of embodiments 12-15, comprising determining the differences between different neoplastic cells within the target based on the spectrometric data.

E-17. The method of any one of embodiments 12-16, comprising determining the type or subtype of said cancer or tumour based on the spectrometric data.

E-18. The method of any one of embodiments 12-17, comprising determining the phenotype and/or genotype of the cancer based on the spectrometric data, optionally by detecting genetic mutations in the tissue or cell.

-143 -E-19. The method of any one of embodiments 2-18, wherein said disease is a cancer selected from Acute Lymphoblastic Leukaemia (ALL), Acute Myeloid Leukaemia (AML), Adrenocortical Carcinoma, adenoma, Anal Cancer, Appendix Cancer, Astrocytoma, Basal Cell Carcinoma, Bile Duct Cancer, Birch-Hirschfield, Blastoma, Bladder Cancer, Bone Cancer, Ewing Sarcoma, Osteosarcoma, Malignant Fibrous Histiocytoma, Brain Stem Glioma, Brain cancer, glioblastoma multiforme ("GBM"), Spinal Cord cancer, Craniopharyngioma, Breast Cancer, Bronchial Tumour, Burkitt Lymphoma, Carcinoid Tumour, Cervical Cancer, Cholangiocarcinoma, Chordoma, Chronic Lymphocytic Leukaemia (CLL), Chronic Myelogenous Leukaemia (CML), Chronic Myeloproliferative Neoplasms, Colon Cancer, Colorectal Cancer, Craniopharyngioma, Childhood, Ductal Carcinoma In Situ (DCIS), Endometrial Cancer, Ependymoma, Esophageal Cancer, Esthesioneuroblastoma, Fibroadenoma, Intraocular Melanoma, Retinoblastoma, Fallopian Tube Cancer, Gallbladder Cancer, Gastric (Stomach) Cancer, Germinoma, Hairy Cell Leukemia, Head and Neck Cancer, Heart Cancer, Heptacarcinoma, Hodgkin Lymphoma, Hypopharyngeal Cancer, Kahler, Kaposi Sarcoma, Kidney cancer, Laryngeal Cancer, Leiomyoma, Lip and Oral Cavity Cancer, Liver Cancer, Lung Cancer (such as, Non-Small Cell or Small Cell), Lymphoma, Lymphoblastoma, Male Breast Cancer, Malignant Fibrous Histiocytoma of Bone, Melanoma, Melanocarcinoma, Medulloblastoma, Merkel Cell Carcinoma, Mesothelioma, Mouth Cancer, Myeloma, Multiple Myeloma, Mycosis Fungoides, Myeloproliferative disorder, Nasal Cavity and Paranasal Sinus Cancer, Nasopharyngeal Cancer, Neuroblastoma, Nephroblastoma, Non-Hodgkin Lymphoma, Oral Cancer, Oropharyngeal Cancer, Osteosarcoma, Ovarian Cancer, Pancreatic Cancer, Papillomatosis, Paraganglioma, Parathyroid Cancer, Penile Cancer, Peritoneal cancer, Pharyngeal Cancer, Pheochromocytoma, Pineoblastoma, Pituitary Tumour, Prostate Cancer, Rectal Cancer, Retinoblastoma, Rhabdomyosarcoma, Salivary Gland Cancer, Sezary Syndrome, Skin Cancer, Seminoma, Teratoma, Testicular Cancer, Throat Cancer, Thyroid Cancer, thoracic cancer, Urethral Cancer, Vaginal Cancer, Vulvar Cancer, Waldenstrom macroglobulinemia, and Wilm's tumour.

E-20. The method of any preceding embodiment, comprising identifying and/or characterising different cell types present in the target from the spectrometric data, optionally determining the genotype and/or phenotype of one or more of said cell types.

E-21. The method of any preceding embodiment, comprising determining the cellular composition of the target based on the spectroscopic data.

E-22. The method of embodiment 20 or 21, comprising determining the numerical proportion of one or more of the cell types within the target tissue from the spectrometric data.

E-23. The method of embodiment 22, comprising determining the numerical proportion of the one or more particular cell types within the tissue from the intensity values of the spectrometric data.

-144 -E-24. The method of any preceding embodiment, comprising analysing necrosis of cellular material in the biological material using said spectroscopic data.

E-25. The method of embodiment 24, wherein said analysing necrosis comprises analysing the level, type, presence or absence of necrosis from the spectroscopic data.

E-26. The method of embodiment 24 or 25, wherein the necrosis is coagulative, liquefactive, caseous, fat necrosis, fibrinoid necrosis and/or gangrenous necrosis.

E-27. The method of any one of embodiments 24-26, wherein the target is at or taken from a margin between healthy and necrotic tissue; and/or is determined to be a margin between healthy and necrotic tissue from said spectrometric data.

E-28. The method of any one of embodiments 24-27, wherein the necrosis is caused by, or associated with, injury, infection, cancer, infarction, toxins, inflammation, lack of proper care to a wound site, frostbite, diabetes, and/or arteriosclerosis.

E-29. The method of any preceding embodiment, wherein the target is in vivo or ex vivo biological tissue or cells derived therefrom and wherein one or more properties of the tissue or cells derived therefrom is determined from the spectroscopic data.

E-30. The method of any embodiment 29, comprising determining the type, level, presence or absence of one or more microbe in the target based upon the spectrometric data; or analysing one or more microbe in the tissue based upon the spectrometric data, wherein the microbe is optionally selected from bacteria, fungi, Achaea, algae, protozoa and viruses.

E-31. The method of any preceding embodiment, comprising analysing a microbial interaction with the target tissue based on the spectrometric data; or analysing a change in a microbial interaction with the target tissue based upon the spectrometric data; or analysing the mucosal microbiome, wherein the microbe is optionally selected from bacteria, fungi, Achaea, algae, protozoa and viruses.

E-32. The method of embodiment 30 or 31, wherein said microbe is selected from Candida albicans, Pseudomonas montelli, Staphylococcus epidermis, Moraxella catarrhalis, Klebsiella pneumonia and Lactobacillus sp.

E-33. The method of any preceding embodiment, comprising determining the type, level, presence or absence of one or more lymphocytes, reactive oxygen species and/or neutrophils in the target based upon the spectrometric data; or analysing one or more lymphocytes, reactive oxygen species and/or neutrophils in the target based upon the spectrometric data.

E-34. The method of any preceding embodiment, comprising determining the type, level, presence or absence of one or more NETs and/or neutrophils generating NETs in the target based upon the spectrometric data; or analysing one or more NETs and/or neutrophils generating NETs in the target based upon the spectrometric data.

E-35. The method of any preceding embodiment, comprising determining the type, level, presence or absence of one or more monocyte chemoattractants and/or neutrophils generating monocyte chemoattractants in the target based upon the spectrometric data; or analysing one or more monocyte chemoattractants and/or neutrophils generating monocyte chemoattractants in the target based upon the spectrometric data.

E-36. The method of any preceding embodiment, comprising analysing the oxygenation status of the target tissue based upon the spectrometric data.

E-37. The method of any preceding embodiment, comprising analysing the functional capacity of the target tissue to process oxygen based upon the spectrometric data.

E-38. The method of any preceding embodiment, comprising analysing Oxy haemoglobin (OxyHb) and/or deoxyhaemoglobin (DeoxyHb) in the target tissue based upon the spectrometric data.

E-39. The method of any one of embodiments 1-28, wherein the target is a faecal specimen and/or body fluid specimen and wherein one or more properties of the specimen is determined from the spectroscopic data.

E-40. The method of any preceding embodiment, comprising analysing said spectrometric data to determine the type, level, presence or absence of a compound, biomarker, microbe or cell type within the target.

E-41. The method of embodiment 40, wherein the biomarker is a microbial biomarker and/or the microbe is selected from bacteria, fungi, Achaea, algae, protozoa and viruses.

E-42. The method of any preceding embodiment, comprising analysing one or more compound and/or biomarker in the one or more region of the target using the spectrometric data.

E-43. The method of embodiment 42, comprising identifying and/or quantifying and/or detecting the presence of said compound and/or biomarker based on the spectrometric data.

E-44. The method of embodiment 42 or 43, wherein the compound or biomarker is selected from the group consisting of: an intracellular compound; an extracellular compound; a lipid; a carbohydrate; DNA; RNA; a protein; a polypeptide; an oligopeptide; a lipoprotein; a lipopeptide; -146 -an amino acid; a chemical molecule; a primary metabolite; a secondary metabolite; an antibiotic; a quorum sensing molecule; a fatty acid synthase product; a pheromone; and a biopolymer.

E-45. The method of any preceding embodiment, comprising analysing a genotype and/or phenotype of at least some cells in the target based on the spectrometric data; and/or identifying a genotype and/or phenotype of at least some cells in the target based on the spectrometric data.

E-46. The method of any preceding embodiment, comprising analysing one or more microbe in the target based on the spectrometric data; and/or identifying and/or quantifying one or more microbe in the target based on the spectrometric data, wherein the microbe is optionally selected from bacteria, fungi, Achaea, algae, protozoa and viruses.

E-47. The method of embodiment 46, comprising identifying the type of one or more microbes in the target based on the spectrometric data.

E-48. The method of embodiment 46 or 47, wherein the presence and/or quantity and/or type of the one or more microbe is determined by analysing said spectrometric data to determine the level, presence or absence of a biomarker for said one or more microbe.

E-49. The method of any preceding embodiment, comprising identifying and/or quantifying the presence of one or more microbial biomarker for one or more microbe in the target based on the spectrometric data.

E-50. The method of any preceding embodiment, comprising detecting and/or diagnosing an infection based on the spectrometric data.

E-51. The method of embodiment 50, comprising determining a genotype or phenotype of an infection-causing microbe based on the spectrometric data.

E-52. The method of any preceding embodiment, comprising analysing the presence of one or more microbial interaction in the target based on the spectrometric data; and/or identifying and/or quantifying the presence of one or more microbial interaction in the target based on the spectrometric data.

E-53. The method of any preceding embodiment, comprising analysing a microbiome within the target based on the spectrometric data.

E-54. The method of embodiment 53, comprising determining the composition of a microbiome within the target based on the spectrometric data.

E-55. The method of embodiment 53 or 54, comprising analysing dysbiosis in said microbiome based on the spectrometric data.

E-56. The method of any one of embodiments 53 to 55, wherein the target is part or, or from, a pregnant human or animal and the microbiome is analysed based on the spectrometric data to determine one or more properties, such as an abnormality, of the pregnancy.

E-57. The method of any preceding embodiment, wherein the target comprises an ex vivo or in vitro biological sample from a patient or an in vivo region of a patient.

E-58. The method of embodiment 57, comprising performing a surgical procedure on the patient based on or aided by the analysis of the spectrometric data.

E-59. The method of embodiment 57 or 58, comprising using the spectrometric data to determine the condition or one or more properties of the sample, or of the region of the patient.

E-60. The method of embodiment 59, comprising comparing the spectrometric data to previously obtained experimental or theoretical data to determine said condition or one or more properties of the sample or region of the patient.

E-61. The method of any one of embodiments 57-60, comprising determining the type of the tissue in the sample or the region of the patient from the spectrometric data.

E-62. The method of any one of embodiments 57-61, comprising determining if the sample, or the region of the patient, is diseased, or non-diseased from the spectrometric data; and/or comprising determining if the sample, or the region of the patient, comprises dead tissue or live tissue from the spectrometric data.

E-63. The method of any one of embodiments 57-62, comprising determining if tissue in the sample, or the region of the patient, is cancerous or non-cancerous from the spectrometric data.

E-64. The method of embodiment 63, comprising determining the grade of tissue cancer from the spectrometric data.

E-65. The method of any one of embodiments 57-64, comprising determining whether the sample, or the region of the patient, comprises mucosal or submucosal tissue from the spectrometric data.

E-66. The method of any one of embodiments 57-65, comprising selectively resecting, removing, treating or destroying biological material in the patient or sample based on the spectrometric data.

-148 -E-67. The method of any one of embodiments 57-66, comprising providing sonic, visual or haptic feedback, based on the spectrometric data, indicating the condition or one or more properties of the sample, or of the region of the patient.

E-68. The method of embodiment 67, comprising selectively resecting, removing, treating or destroying biological material in the patient or sample based on the feedback.

E-69. The method of embodiment 67 or 68, comprising resecting, removing, treating or destroying biological material in the patient; and selectively continuing or discontinuing the resecting, removing, treating or destroying of the biological material based on the feedback.

E-70. The method of any preceding embodiment, wherein the target is at, or taken from, a margin between healthy and non-healthy tissue from said spectrometric data; and/or is determined to be a margin between healthy and non-healthy tissue from said spectrometric data.

E-71. The method of any preceding embodiment, comprising using the first device to generate aerosol, smoke or vapour from multiple different regions on the target.

E-72. The method of embodiment 71, wherein the aerosol, smoke or vapour is generated from multiple discrete regions in the target.

E-73. The method of embodiment 71 or 72, comprising mass analysing and/or ion mobility analysing said aerosol, smoke, vapour or ions derived therefrom for aerosol, smoke, vapour generated at each of said different regions so as to obtain said spectrometric data for the different regions, and correlating the spectrometric data to its respective region on the target so as to provide ion imaging or map data for the target.

E-74. The method of embodiment 73, comprising converting the spectrometric data for each region into data representative of the type, condition or constituents of the material at said regions in the target; and optionally displaying the representative data as an ion image or map showing the type, condition or constituents of the material as a function of location in the target.

E-75. The method of embodiment 74, wherein the representative data indicates the type, level and/or presence and/or absence of: diseased; cancerous; or necrotic material at each of the regions in the target.

E-76. The method of embodiment 74 or 75, comprising identifying and/or displaying margins of diseased, cancerous, and/or necrotic tissue in the target.

-149 -E-77. The method of embodiment 74, 75 or 76, comprising identifying and/or displaying the location and/or margins of one or more cell or tissue type of interest.

E-78. The method of embodiment 77, wherein the cell or tissue type of interest comprises diseased and/or cancerous and/or necrotic tissue or cells in the target; and/or wherein the cell or tissue type of interest comprises healthy tissue or cells.

E-79. The method of any one of embodiments 74-78, wherein the representative data indicates the different type of cells or constituents in the target.

E-80. The method of any one of embodiments 74-79, comprising identifying and/or displaying the distribution of different microbes within a target, wherein the microbes are optionally selected from bacteria, fungi, Achaea, algae, protozoa and/or viruses.

E-81. The method of any one of embodiments 74-80, comprising identifying and/or displaying the distribution of biomarkers across the target.

E-82. The method of any one of embodiments 73-81, wherein the ion imaging or map data is generated and/or displayed in real-time.

E-83. The method of embodiment 82, comprising displaying on the ion image or map the current position of at least a portion of the first device relative to the target; and/or displaying on the ion image or map the current position of a tool.

E-84. The method of embodiment 83, wherein said portion of the first device comprises a portion that generates said aerosol, smoke or vapour from the target; and/or wherein the tool is a surgical tool such as a tool for resecting or ablating tissue.

E-85. The method of any preceding embodiment, wherein the target is from, or is part of, a human or animal and the method comprises determining a treatment to be provided to the human or animal based upon the spectrometric data.

E-86. The method of any preceding embodiment, comprising administering a drug, therapeutic agent or therapy to the target, or to a subject and then taking the target from the subject, prior to performing steps a) and b) of embodiment 1 on the target; and determining the effect of the drug, therapeutic agent or therapy on the target, or on components within the target, based on the spectrometric data.

E-87. The method of embodiment 86, comprising determining from the spectrometric data an effect, or effectiveness, of the drug, therapeutic agent or therapy on the target or subject.

-150 -E-88. The method of any preceding embodiment, comprising generating said aerosol, smoke or vapour at a first time so as to obtain said first spectrometric data; generating aerosol, smoke or vapour from one or more regions of said first target at a second subsequent time; mass analysing and/or ion mobility analysing the aerosol, smoke or vapour generated at the second time, or ions derived therefrom, so as to obtain second spectrometric data; and comparing the first and second spectrometric data to determine changes in the first target or components thereof.

E-89. The method of embodiment 88, wherein the step of generating aerosol, smoke or vapour and the step of analysing is repeated at one or more further times to obtain third or further spectrometric data, respectively; optionally wherein the third or further spectrometric data is compared with the first and/or second spectrometric data to determine changes in the first target or components thereof.

E-90. The method of embodiment 88 or 89, comprising administering a drug, therapeutic agent or therapy to the first target between said first and second times; and comparing the first and second data to determine the effect, or effectiveness, of the drug, therapeutic agent or therapy on the target.

E-91. The method of any one of embodiments 1-87, comprising generating said aerosol, smoke or vapour at a first time so as to obtain said first spectrometric data; generating aerosol, smoke or vapour from one or more regions of a second different target, at said first time and/or at a second subsequent time; mass analysing and/or ion mobility analysing the aerosol, smoke or vapour generated from the second target at the first and/or second time, or ions derived therefrom, so as to obtain second spectrometric data; and comparing the first and second spectrometric data to determine differences between the first and second targets.

E-92. The method of embodiment 91, wherein aerosol, smoke or vapour is generated from a third or further target and the step of analysing may be performed one or more further times to obtain third or further spectrometric data, respectively; optionally wherein the third or further spectrometric data is compared with the first and/or second spectrometric data to determine differences between the third target and/or the first and second targets.

E-95. The method of embodiment 92, wherein the first and second targets are, or are from, different subjects.

E-96. The method of embodiment 92, wherein the first and second targets are, or are from, different regions of the same subject.

-151 -E-97. The method of embodiment 92, 95 or 96, wherein one of the targets is taken from, or is at, a location known or suspected to be healthy; and another of the targets is taken from, or is at, a location known or suspected to be non-healthy, diseased, a tumour margin, a tumour stroma, or a neoplastic tumour.

E-98. The method of any one of embodiments 92-97, wherein the targets analysed at the first and second times are from, or at, the same region or tissue of the subject(s).

E-99. The method of any one of embodiments 92-98, wherein the targets analysed at the first and second times are from, or at, the same tissue type.

E-100. The method of any one of embodiments 96 or 98-99, wherein the first and second targets are taken from, or are at, the same subject; wherein the aerosol, smoke or vapour is generated from said one or more regions of the second target at said second time; and wherein the method comprises administering a drug, therapeutic agent or therapy to the subject between said first and second times; and comparing the first and second data to determine the effect, or effectiveness, of the drug, therapeutic agent or therapy on the subject.

E-101. The method of embodiment 90 or 100, wherein the step of administering a therapeutic agent comprises administering a therapeutically effective amount of the therapeutic agent.

E-

102. The method of embodiment 90, 100 or 101, wherein the drug, therapeutic agent or therapy is an anti-cancer treatment and/or radiation and/or surgery; and/or wherein the drug, therapeutic agent or therapy is antimicrobial or probiotic treatment.

E-103. The method of embodiment 90, 100, 101 or 102, comprising determining the effect of said drug, therapeutic agent or therapy on one or more microbe or the microbiome within the target.

E-104. The method of embodiment 103, comprising determining one or more virulence factors of the microbe E-105. The method of embodiment 88, 89 or 90, comprising comparing the first and second spectrometric data to determine changes in the microbiome within the target over time.

E-106. The method of embodiment 88, 89, 90 or 105, comprising comparing the first and second spectrometric data to determine the progression or remission of an infection caused by one or more microbe within the target.

E-107. The method of embodiment 88, 89, 90, 105 or 106, comprising administering a vaccine to the target between said first and second times; and comparing the first and second spectrometric data to determine the effectiveness of the vaccination.

-152 -E-108. The method of any one of embodiments 88-90 or 105-107, comprising comparing the first and second spectrometric data to monitor the progression or development of a disease over time; and/or to assess the effectiveness or progress of therapy on the subject; and optionally making a diagnosis, prognosis, and/or stratifying the subjects, based on said monitoring and/or assessing.

E-109. The method of any preceding embodiment, wherein said method comprises analysing said spectrometric data in order to analyse the type, level, presence or absence of a biomarker, wherein the biomarker is a direct biomarker or an indirect biomarker.

E-110. The method of embodiment 109, wherein the biomarker is a lipid biomarker; and/or wherein the biomarker is selected from the group consisting of: fatty acids, glycerolipids, sterol lipids, sphingolipids, prenol lipids, saccharolipids and/or phospholipids; and/or wherein the biomarker is a metabolite, a primary metabolite, a secondary metabolite, an antibiotic, a quorum sensing molecule, a fatty acid synthase product, a pheromone, and/or a biopolymer; and/or wherein the biomarker is a biomarker for a bacteria; and/or wherein the biomarker is an exogenous compound or an endogenous compound.

E-111. A method as embodimented in any preceding embodiment, wherein said biological material is human or non-human animal material.

E-112. A method as embodimented in any preceding embodiment, wherein said biological material comprises or consists of in vivo biological material.

E-113. A method as embodimented in any of embodiments 1-111, wherein said biological tissue comprises or consists of ex vivo biological tissue.

E-114. A method as embodimented in any of embodiments 1-111, wherein said biological tissue comprises or consists of in vitro biological tissue.

E-115. A method as embodimented in any preceding embodiment, wherein the target comprises a surgical resection specimen, a biopsy specimen, a swab, a smear, a faecal specimen, or a body fluid specimen.

E-116. A method as embodimented in any preceding embodiment, wherein said target comprises biological tissue.

E-117. A method as embodimented in embodiment 116, wherein said tissue is selected from the group consisting of: adrenal gland tissue, appendix tissue, bladder tissue, bone, bowel tissue, brain tissue, breast tissue, bronchi, coronal tissue, ear tissue, esophagus tissue, eye -153 -tissue, endometrioid tissue, gall bladder tissue, genital tissue, heart tissue, hypothalamus tissue, kidney tissue, large intestine tissue, intestinal tissue, larynx tissue, liver tissue, lung tissue, lymph nodes, mouth tissue, mucosa, nose tissue, pancreatic tissue, parathyroid gland tissue, pituitary gland tissue, prostate tissue, rectal tissue, salivary gland tissue, skeletal muscle tissue, skin tissue, small intestine tissue, spinal cord, spleen tissue, stomach tissue, thymus gland tissue, trachea tissue, thyroid tissue, soft tissue, connective tissue, peritoneal tissue, blood vessel tissue, fat tissue, ureter tissue. urethra tissue, soft and connective tissue, peritoneal tissue, blood vessel tissue and/or fat tissue; (ii) grade I, grade II, grade Ill or grade IV cancerous tissue; (Hi) metastatic cancerous tissue; (iv) mixed grade cancerous tissue; (v) a sub-grade cancerous tissue; (vi) healthy or normal tissue; or (vii) cancerous or abnormal tissue.

E-118. A method as embodimented in embodiment 116 or 117, wherein the tissue is affected by a condition selected from the group consisting of: a lesion; a diabetic lesion; a wound; an ulcer; an abscess; a tumour; cancer; and necrosis.

E-119. A method as embodimented in any preceding embodiment, wherein the method comprises analysing analyte ions derived from the aerosol, smoke or vapour.

E-120. A method as embodimented in embodiment 119, comprising analysing the ions only in negative ion mode, only in positive ion mode in negative ion mode and then positive ion mode, or positive ion mode and then negative ion mode.

E-121. A method as embodimented in embodiment 119 or 120, wherein said step of analysing said analyte ions comprises: (i) mass analysing said analyte ions; (ii) analysing the ion mobility or differential ion mobility of said analyte ions; (Hi) analysing the ionic cross-sections or collision cross sections of said analyte ions; (iv) separating said analyte ions according to their ion mobility or differential ion mobility; (v) separating said analyte ions according to their ion mobility or differential ion mobility prior to mass analysing said analyte ions; or (vi) excluding or discarding analyte ions based upon their ion mobility or differential ion mobility.

E-122. A method as embodimented in embodiment 119, 120 or 121, comprising analysing the analyte ions with an ion analyser to obtain the spectrometric data, analysing lockmass, lock ion-mobility or calibration ions, and calibrating said ion analyser or adjusting the spectrometric data based upon the data obtained from analysing said lockmass, lock ion-mobility or calibration ions.

E-123. A method as embodimented in any preceding embodiment, wherein said first device comprises or forms part of an ambient ion or ionisation source; or wherein said first device generates said aerosol, smoke or vapour from the target to be analysed and which contains ions and/or is subsequently ionised by an ambient ion or ionisation source or other ionisation source.

-154 -E-124. A method as embodimented in embodiment 123, wherein the target is native or unmodified biological material.

E-125. A method as embodimented in embodiment 124, wherein said native or unmodified target is unmodified by the addition of a matrix or reagent.

E-126. A method as embodimented in any of embodiments 123-125, wherein said first device is used to generate the aerosol, smoke or vapour from the one or more regions of the target without prior preparation of the target.

E-127. A method as embodimented in any of embodiments 123-126, wherein the target is frozen, fixed chemically, chemically stained, or sectioned prior to steps a) and b) of embodiment 1.

E-128. A method as embodimented in embodiment 127, wherein the target is unmodified other than being frozen, fixed chemically, chemically stained, or sectioned prior to steps a) and b) of embodiment 1.

E-129. A method as embodimented in any of embodiments 123-128, wherein said first device comprises or forms part of an ion source selected from the group consisting of: (i) a rapid evaporative ionisation mass spectrometry ("REIMS") ion source; (ii) a desorption electrospray ionisation ("DESI") ion source; (iii) a laser desorption ionisation ("LDI") ion source; (iv) a thermal desorption ion source; (v) a laser diode thermal desorption ("LDTD") ion source; (vi) a desorption electro-flow focusing ("DEFFI") ion source; (vii) a dielectric barrier discharge ("DBD") plasma ion source; (viii) an Atmospheric Solids Analysis Probe ("ASAP") ion source; (ix) an ultrasonic assisted spray ionisation ion source; (x) an easy ambient sonic-spray ionisation ("EASI") ion source; (xi) a desorption atmospheric pressure photoionisation ("DAPPI") ion source; (xii) a paperspray ("PS") ion source; (xiii) a jet desorption ionisation ("JeDI") ion source; (xiv) a touch spray ("TS") ion source; (xv) a nano-DESI ion source; (xvi) a laser ablation electrospray ("LAESI") ion source; (xvii) a direct analysis in real time ("DART") ion source; (xviii) a probe electrospray ionisation ("PESI") ion source; (xix) a solid-probe assisted electrospray ionisation ("SPA-ESP') ion source; (xx) a cavitron ultrasonic surgical aspirator ("CUSA") device; (xxi) a hybrid CUSA-diathermy device; (xxii) a focussed or unfocussed ultrasonic ablation device; (xxiii) a hybrid focussed or unfocussed ultrasonic ablation and diathermy device; (xxiv) a microwave resonance device; (xxv) a pulsed plasma RF dissection device; (xxvi) an argon plasma coagulation device; (xxvi) a hybrid pulsed plasma RF dissection and argon plasma coagulation device; (xxvii) a hybrid pulsed plasma RF dissection and JeDI device; (xxviii) a surgical water/saline jet device; (xxix) a hybrid electrosurgery and argon plasma coagulation device; and (xxx) a hybrid argon plasma coagulation and water/saline jet device.

E-130. A method as embodimented in any of embodiments 123-129, wherein said step of using said first device to generate aerosol, smoke or vapour from one or more regions of said target comprises contacting said target with one or more electrodes.

E-131. A method as embodimented in embodiment 130, wherein said one or more electrodes comprise either: (i) a monopolar device, wherein said method optionally further comprises providing a separate return electrode, (H) a bipolar device; or (iii) a multi-phase RF device, wherein said method optionally further comprises providing a separate return electrode or electrodes.

E-132. A method as embodimented in embodiment 130 or 131, wherein said one or more electrodes comprise or forms part of a rapid evaporation ionization mass spectrometry ("REIMS") device.

E-133. A method as embodimented in any of embodiments 130-132, further comprising applying an AC or RF voltage to said one or more electrodes in order to generate said aerosol, smoke or vapour.

E-134. A method as embodimented in embodiment 133, wherein the step of applying said AC or RF voltage to said one or more electrodes further comprises applying one or more pulses of said AC or RF voltage to said one or more electrodes.

E-135. A method as embodimented in embodiment 133 or 134, wherein said step of applying said AC or RF voltage to said one or more electrodes causes heat to be dissipated into said 25 target.

E-136. A method as embodimented in any of embodiments 123-129, wherein said step of using said first device to generate aerosol, smoke or vapour from one or more regions of the target further comprises irradiating the target with a laser.

E-137. A method as embodimented in any of embodiments 123-136, wherein said first device generates aerosol from one or more regions of the target by direct evaporation or vaporisation of target material from said target by Joule heating or diathermy.

E-138. A method as embodimented in any of embodiments 123-137, wherein said step of using said first device to generate aerosol, smoke or vapour from one or more regions of the target further comprises directing ultrasonic energy into said target.

E-139. A method as embodimented in any of embodiments 123-138, wherein said aerosol comprises uncharged aqueous droplets, optionally comprising cellular material.

-156 -E-140. A method as embodimented in any of embodiments 123-139, wherein said first device comprises a point of care ("POC"), diagnostic or surgical device.

E-141. A method as embodimented in any of embodiments 123-140, comprising ionising at least some of said aerosol, smoke or vapour, or analyte therein, so as to generate analyte ions.

E-142. A method as embodimented in any of embodiments 123-141, comprising directing or aspirating at least some of said aerosol, smoke or vapour into a vacuum chamber of a mass and/or ion mobility spectrometer; and/or ionising at least some said aerosol, smoke or vapour, or the analyte therein, within a, or said, vacuum chamber of said mass and/or ion mobility spectrometer so as to generate a plurality of analyte ions.

E-143. A method as embodimented in embodiment 141 or 142, comprising causing said aerosol, smoke or vapour, or analyte therein, to impact upon a collision surface, optionally located within a, or the, vacuum chamber of said mass and/or ion mobility spectrometer, so as to generate the plurality of analyte ions.

E-144. A method as embodimented in any preceding embodiment, comprising adding a matrix to said aerosol, smoke or vapour, optionally prior to the aerosol, smoke or vapour being ionised and/or impacted on a collision surface.

E-145. A method as embodimented in embodiment 144, wherein said matrix is selected from the group consisting of (i) a solvent for said aerosol, smoke or vapour or analyte therein; (ii) an organic solvent; (iii) a volatile compound; (iv) polar molecules; (v) water; (vi) one or more alcohols; (vii) methanol; (viii) ethanol; (ix) isopropanol; (x) acetone; (xi) acetonitrile; (xii) 1-butanol; (xiii) tetrahydrofuran; (xiv) ethyl acetate; (xv) ethylene glycol; (xvi) dimethyl sulfoxide; an aldehyde; (xviii) a ketone; (xiv) non-polar molecules; (xx) hexane; (xxi) chloroform; and (xxii) 1-propanol.

E-146. A method as embodimented in any one of embodiments 141-145, comprising mass and/or ion mobility analysing said analyte ions or ions derived from said aerosol, smoke or vapour in order to obtain the spectrometric data.

E-147. A method as embodimented in embodiment 146, comprising analysing said spectrometric data in order either: (i) to distinguish between healthy and diseased tissue; (H) to distinguish between potentially cancerous and non-cancerous tissue; (Hi) to distinguish between different types or grades of cancerous tissue; (iv) to distinguish between different types or classes of target material; (v) to determine whether or not one or more desired or undesired substances are present in said target; (vi) to confirm the identity or authenticity of said target; (vii) to determine whether or not one or more impurities, illegal substances or undesired substances are present in said target; (viii) to determine whether a human or animal patient is at an increased risk of suffering an adverse outcome; (ix) to make or assist in the making a diagnosis or prognosis; and (x) to inform a surgeon, nurse, medic or robot of a medical, surgical or diagnostic outcome.

E-148. A method as embodimented in any preceding embodiment, comprising analysing the spectrometric data, wherein analysing the spectrometric data comprises analysing one or more sample spectra so as to classify the aerosol, smoke or vapour sample.

E-149. A method as embodimented in embodiment 148, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises unsupervised analysis of the one or more sample spectra (e.g., for dimensionality reduction) and/or supervised analysis of the one or more sample spectra (e.g., for classification).

E-150. A method as embodimented in embodiment 148 or 149, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises using one or more of: univariate analysis; multivariate analysis; principal component analysis (PCA); linear discriminant analysis (LDA); maximum margin criteria (MMC); library-based analysis; soft independent modelling of class analogy (SIMCA); factor analysis (FA); recursive partitioning (decision trees); random forests; independent component analysis (ICA); partial least squares discriminant analysis (PLS-DA); orthogonal (partial least squares) projections to latent structures (OPLS); OPLS discriminant analysis (OPLS-DA); support vector machines (SVM); (artificial) neural networks; multilayer perceptron; radial basis function (RBF) networks; Bayesian analysis; cluster analysis; a kernelized method; and subspace discriminant analysis.

E-151. A method as embodimented in embodiment 148, 149 or 150, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises developing a classification model or library using one or more reference sample spectra.

E-152. A method as embodimented in any of embodiments 148-151, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises performing linear discriminant analysis (LDA) (e.g., for classification) after performing principal component analysis (PCA) (e.g., for dimensionality reduction).

E-153. A method as embodimented in any of embodiments 148-152, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises performing a maximum margin criteria (MMC) process (e.g., for classification) after performing principal component analysis (PCA) (e.g., for dimensionality reduction).

E-154. A method as embodimented in any of embodiments 148-153, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises defining one or more classes within a classification model or library.

-158 -E-155. A method as embodimented in any of embodiments 148-154, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises defining one or more classes within a classification model or library manually or automatically according to one or more class or cluster criteria.

E-156. A method as embodimented in embodiment 155, wherein the one or more class or cluster criteria for each class are based on one or more of: a distance between one or more pairs of reference points for reference sample spectra within a model space; a variance value between groups of reference points for reference sample spectra within a model space; and a variance value within a group of reference points for reference sample spectra within a model space.

E-157. A method as embodimented in embodiment 154, 155 or 156, wherein the one or more classes are each defined by one or more class definitions.

E-158. A method as embodimented in embodiment 157, wherein the one or more class definitions comprise one or more of: a set of one or more reference points for reference sample spectra, values, boundaries, lines, planes, hyperplanes, variances, volumes, Voronoi cells, and/or positions, within a model space; and one or more positions within a class hierarchy.

E-159. A method as embodimented in any of embodiments 148-158, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises using a classification model or library to classify one or more unknown sample spectra.

E-160. A method as embodimented in any of embodiments 148-159, wherein analysing the one or more sample spectra so as to classify the aerosol, smoke or vapour sample comprises classifying one or more sample spectra manually or automatically according to one or more classification criteria.

E-161. A method as embodimented in embodiment 160, wherein the one or more classification criteria comprise one or more of: a distance between one or more projected sample points for one or more sample spectra within a model space and a set of one or more reference points for one or more reference sample spectra, values, boundaries, lines, planes, hyperplanes, volumes, Voronoi cells, or positions, within the model space being below a distance threshold or being the lowest such distance; a position for one or more projected sample points for one or more sample spectra within a model space being one side or other of one or more reference points for one or more reference sample spectra, values, boundaries, lines, planes, hyperplanes, or positions, within 40 the model space; a position for one or more projected sample points for one or more sample spectra within a model space being within one or more volumes or Voronoi cells within the model space; and -159 -a probability or classification score being above a probability or classification score threshold or being the highest such probability or classification score.

E-162. A method of determining the severity, grade, stage, presence or absence of a disease in biological material comprising the method of any preceding embodiment, wherein said target is said biological material.

E-163. A method of determining the severity, grade, stage, presence or absence of a cancer, tumour cell or tumour tissue in a target comprising the method of any preceding embodiment.

E-164. A method of analysing the cellular composition of a biological material comprising a method as embodimented in any one of embodiments 1-163, wherein the biological material is said target and wherein the cellular composition is determined from the spectroscopic data.

E-165. A method of analysing necrosis of cellular material in a biological material comprising a method as embodimented in any of embodiments 1-163, wherein the biological material is said target, and wherein said analysing necrosis comprises using said spectroscopic data.

E-166. A method of analysing a biological tissue comprising a method as embodimented in any of embodiments 1-163, wherein the biological tissue is said target and wherein one or more properties of the tissue is determined from the spectroscopic data.

E-167. A method of analysing a faecal specimen and/or body fluid specimen comprising the method of any of embodiments 1-163, wherein the target is the faecal specimen and/or body fluid specimen and wherein one or more properties of the faecal specimen and/or body fluid specimen is determined from the spectroscopic data.

E-168. A method of analysing one or more compound and/or biomarker comprising the method of any one of embodiments 1-163, wherein the one or more region of the target comprises the compound and/or biomarker, and wherein the method analyses said one or more compound and/or biomarker based on the spectrometric data.

E-169. A method of analysing a genotype and/or phenotype of cells comprising a method as embodimented in any of embodiments 1-163, wherein the target comprises the cells, and wherein the method analyses the genotype and/or phenotype of the cells based on the spectrometric data.

E-170. A method of analysing a microbe comprising a method as embodimented in any of embodiments 1-163, wherein the target comprises the microbe, and wherein the method analyses the microbe based on the spectrometric data, wherein the microbe is optionally selected from bacteria, fungi, Achaea, algae, protozoa and viruses.

-160 -E-171. A method of surgery on a patient comprising the method of any of embodiments 1-163, wherein the target comprises a biological sample from the patient or a region of the patient.

E-172. A method of treatment of a human or animal subject comprising the method of any of embodiments 1-163, wherein the target comprises a biological sample from the subject or a region of the subject, and a determination of the treatment to be provided is based upon the spectrometric data, and an appropriate treatment is administered to said subject E-173. A method of analysis comprising the method of one of embodiments 1-163, comprising using said spectrometric data to: (i) diagnose said disease or a disease; (ii) monitor the progression or development of said disease, or a disease, over time; (iii) determine prognosis of said disease, or a disease; (iv) predict the likelihood of said disease, or a disease, responding to treatment; (v) monitor the response of said disease, or a disease, to treatment; (vi) stratify subjects; (vii) determine the distribution of said disease, or a disease, within the target; or (viii) determine the margin in the target between said disease, or a disease, and a region of the target not having the disease.

E-174. A method of analysing a cancer in a biological material comprising a method as embodimented in any of embodiments 1-163, wherein the biological material is a tumour comprising or consisting of human cancer cells that were injected or xenografted into an animal, wherein said biological material is said target, and wherein said analysing cancer comprises using said spectroscopic data.

E-175 A method according to embodiment 174, wherein said human cancer cells comprise a transgene and/or a silenced gene.

E-176. Apparatus arranged and configured for performing the method of any preceding embodiment, comprising: said first device for generating said aerosol, smoke or vapour from one or more regions of the first target; a mass analyser and/or ion mobility analyser arranged and configured to analyse said aerosol, smoke or vapour or ions derived therefrom so as to obtain the first spectrometric data.

Claims (18)

1. -161 -Claims 1. A method of analysis using mass spectrometry and/or ion mobility spectrometry comprising: using a first device to generate aerosol or vapour from one or more regions of a first target of biological material; mass analysing and/or ion mobility analysing said aerosol or vapour, or ions derived therefrom so as to obtain spectrometric data; and determining the presence or absence of one or more microbe in said target based upon said spectrometric data; wherein said first device comprises a desorption electrospray ionisation ("DESI") ion source or a desorption electro-flow focusing ("DEFFI") ion source; wherein said biological material is specimen derived from a human or nonhuman animal subject.
2. 2. A method as claimed in claim 1, wherein said method comprises directing a spray of electrically charged droplets onto said target to generate analyte ions.
3. 3. A method as claimed in claim 1 or claim 2, wherein said method comprises identifying said microbe at the species or strain level.
4. 4. A method as claimed in any preceding claim, further comprising analysing said spectrometric data in order to analyse or determine one or more of the following in relation to the one or more regions of the target biological material: (i) determine the grade, type or subtype of a cancer or tumour; (ii) determine the grade, severity, stage, presence or absence of a disease; (iii) determine the phenotype and/or genotype of one or more cells; (iv) detect the level, type, presence or absence of necrosis; (v) determine the genotype and/or phenotype of one or more microbe; (vi) analyse a microbial interaction with a tissue; (vii) analyse dysbiosis; (viii) determine the type, level, presence or absence of a compound and/or biomarker; (ix) analyse the status of a tissue; and/or (x) identify and/or display a margin between two different tissue types and/or between diseased and healthy tissue.
5. 5. A method as claimed in any preceding claim, further comprising determining the severity, grade, stage, presence or absence of a disease in said one of more regions of the target based upon said spectrometric data.
6. -162 - 6. A method as claimed in claim 5, wherein the severity, grade, stage, presence or absence of the disease is determined by analysing said spectrometric data to determine the type, level, presence or absence of a biomarker for said disease.
7. 7. A method as claimed in any preceding claim, wherein the microbe is optionally selected from bacteria, fungi, Achaea, algae, protozoa and viruses.
8. 8. A method as claimed in any preceding claim, further comprising detecting an infection based on said spectrometric data.
9. 9. A method as claimed in claim 8, further comprising determining a genotype or phenotype of an infection-causing microbe based on said spectrometric data.
10. 10. A method as claimed in any preceding claim, wherein said method comprises analysing said spectrometric data in order to analyse the type, level, presence or absence of a biomarker, wherein the biomarker is a direct biomarker or an indirect biomarker.
11. 11. A method as claimed in claim 10, wherein: said biomarker is a lipid biomarker; and/or said biomarker is selected from the group consisting of: fatty acids, glycerolipids, sterol lipids, sphingolipids, prenol lipids, saccharolipids and/or phospholipids; and/or said biomarker is a metabolite, a primary metabolite, a secondary metabolite, an antibiotic, a quorum sensing molecule, a fatty acid synthase product, a pheromone; and/or said biomarker is a biopolymer; and/or said biomarker is a biomarker for a bacteria; and/or said biomarker is an exogenous compound or an endogenous compound.
12. 12. A method as claimed in any preceding claim, wherein said target comprises a surgical resection specimen, a biopsy specimen, a tissue specimen, a swab, a smear, a faecal specimen, or a body fluid specimen.
13. 13. A method as claimed in any preceeding claim, wherein the biological material is native or unmodified biological material.
14. -163 - 14 A method as claimed in any preceding claim, wherein said biological material is a sample of a mucosal membrane.
15. 15. A method as claimed in claim 14, wherein said mucosal membrane comprises a endometrium, intestinal, gastric, oral, vaginal, esophageal, gingival, nasal, buccal or bronchial membrane.
16. 16. A method as claimed in claim 15, wherein said mucosal membrane is the vaginal mucosa of a pregnant female, and wherein said method further comprises analysing said spectrometric data in order to analyse pregnancy or to diagnose or predict the risk of spontaneous preterm birth.
17. 17. A method as claimed in any one of claims 12 to 16, wherein said swab is a standard medical swab.
18. 18. Apparatus comprising: a first device arranged and adapted to direct a spray of charged droplets onto a first target of biological material in order to generate a plurality of analyte ions, wherein said first device comprises a Desorption Electrospray Ionisation ("DESI") ion source or a desorption electroflow focusing ionisation ("DEFFI") ion source; and a mass analyser and/or ion mobility analyser for mass analysing and/or ion mobility analysing said analyte ions or ions derived from said analyte ions in order to obtain mass spectrometric and/or ion mobility data.wherein said apparatus is further arranged to determine the presence or absence of one or more microbe in said target based upon said spectrometric data; and wherein said biological material is specimen derived from a human or nonhuman animal subject.