WO2016030621A1 - Méthode de caractérisation d'un échantillon par imagerie par spectrométrie de masse - Google Patents
Méthode de caractérisation d'un échantillon par imagerie par spectrométrie de masse Download PDFInfo
- Publication number
- WO2016030621A1 WO2016030621A1 PCT/FR2015/052254 FR2015052254W WO2016030621A1 WO 2016030621 A1 WO2016030621 A1 WO 2016030621A1 FR 2015052254 W FR2015052254 W FR 2015052254W WO 2016030621 A1 WO2016030621 A1 WO 2016030621A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- sample
- ion
- morphometric
- data
- texture
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- H01J49/00—Particle spectrometers or separator tubes
- H01J49/0004—Imaging particle spectrometry
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/693—Acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/695—Preprocessing, e.g. image segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/698—Matching; Classification
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- H01J49/00—Particle spectrometers or separator tubes
- H01J49/0027—Methods for using particle spectrometers
- H01J49/0036—Step by step routines describing the handling of the data generated during a measurement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
- G06T2207/10061—Microscopic image from scanning electron microscope
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
Definitions
- the invention relates to a method for characterizing a sample using mass spectrometry imaging. More particularly, the invention proposes using imaging data obtained by mass spectrometry to extract and measure morphometric and / or texture data associated with the distribution of one or more ions in a sample. The invention also proposes using such morphometric and / or texture data to identify and / or select one or more molecules of interest in a sample.
- the invention has applications in any field where the characterization of a sample or compound in a sample is useful / necessary.
- the invention finds, for example, applications in the pharmaceutical field for identifying molecular markers in different biological tissues, or in the field of medical diagnosis, for identifying a type of tissue and / or cell in a sample.
- the invention can be used in the field of quality control, to verify that parts have the desired characteristics.
- Mass spectrometry is a widely known technique used in chemical and biochemical analysis to detect and identify molecules of interest in a sample.
- molecular imaging by mass spectrometry has developed, allowing to visualize the distribution of molecules of interest directly in a sample.
- Mass spectrometry (ISM) imaging brings together all imaging technologies that use an ionization source to locate molecular ions from a sample.
- Multiple sources of ionization such as laser, ions, gas, liquid, solvent, plasma (single or combined sources), microwaves, electrons, which can be used in imaging mode, can be mentioned.
- Mass spectrometry imaging is currently mainly used for the analysis of biological tissues. Indeed, it is possible thanks to the ISM to directly study the molecular composition of a tissue or a section thereof, without fluorescence labeling and without radioactivity.
- ISM makes it possible to discriminate and identify ions detected directly on the sample.
- ISM makes it possible to discriminate and identify ions detected directly on the sample.
- ACP Principal Component Analysis
- PLSA Long et al., J. Proteome Res, 2008. 7 (3): 969-78, D.
- ISM data analysis methods are currently based on the study of intensity values associated with m / z ratios. More specifically, during the acquisition of an image of a sample by ISM, said sample is analyzed with a beam of the ionization source in order to record at each point of the sample an average of spectrum corresponding to the ions detected.
- the set of recorded data is in the form of a matrix, with for each recording coordinate system a spectrum which contains on a column the different m / z ratios and in a second column the corresponding intensities.
- the measurement of the intensity of this ion makes it possible, through image reconstruction software, to obtain the distribution of this ion (and therefore of the compound corresponding) taking into account the peak intensity with the recording coordinates and assigning to each point a pixel of color and / or defined color intensity.
- it is also known to defocus the ionization source so as to analyze all or part of the sample in a single shot. The location is then obtained by means of a position detector (Luxemleid et al., Anal Chem 2004).
- the invention proposes to use imaging data of a sample (Le., Positions, m / z ratios, intensities), conventionally obtained by mass spectrometry imaging, not only by taking into account the intensity values. associated with the ions of said sample, but taking into account the spatial arrangement of said ions in the sample, and measurements that characterize said spatial arrangement.
- a sample is characterized according to the morphology of the distribution and associated measurements (morphometric data), and / or the texture of one or more ions in the sample.
- the shapes and arrangement of the shapes associated with the presence of said ions in a sample provide additional information (surface, shape, volume, pattern, repetition, quantity, dispersion, and values derived from such information, such as their ratio, etc.) in relation to the information resulting from the spectral intensities. It is then possible to characterize a sample according to the shapes of at least one ion in said sample and / or the dimensions of these shapes and / or their arrangement in the sample. According to the invention, it is thus possible to differentiate two samples having similar or identical molecular profiles.
- the invention proposes to characterize the morphometry and / or the texture of the distribution of one or more ions in a sample and to use these morphometric and texture data in addition to or in place of the spectral imaging data. . According to the invention, this characterization can be combined with methods for processing current imaging data taking into account intensities, for example, and more generally with any method of characterizing a sample, including optical, physical and color treatment. etc.
- the subject of the invention is therefore a method for characterizing a sample by mass spectral imaging (ISM) according to which a spatial arrangement of at least one ion in said sample is characterized from the imaging data associated with said ion in terms of morphometry and / or texture.
- ISM mass spectral imaging
- the spatial arrangement of at least one ion in said sample is measured and / or quantified from ISM data, in order to identify shapes, arrangements of shapes, particular measurements and to characterize the distribution accordingly.
- said ion in the sample by these morphometric characteristics and / or texture.
- the invention also relates to a method for identifying a new molecule of interest in a sample, according to which
- the invention also provides a method of identifying a sample by mass spectrometry imaging in which: i) A database, or model, is established with morphometric and / or texture data associated with a plurality of reference ions, obtained from several reference samples, said morphometric and / or texture data being representative of the spatial arrangement of said reference ions in said reference samples;
- Morphometric and / or texture data associated with at least one ion are recorded in the sample to be identified;
- Morphometric data and / or textures associated with the ions of the sample to be identified are compared with the morphometric data and / or textures associated with the reference ions contained in the database or the model.
- Another object of the invention is a computer readable data medium comprising computer-executable instructions adapted to allow a computer system to execute at least one step of the method of characterizing a sample, and or at least one step of the method of identifying a sample and / or at least one step of the method of identifying a molecule of interest in a sample according to the invention.
- Figure 1 Example of data sets acquired by mass spectrometry imagery for two samples (1A and 1B), where the blank is 0 and the black is a maximum value;
- Figure 2 Example of morphological characterization of mass spectrometry imaging data of pulmonary tissue samples according to the invention, resulting in the identification of pathological pulmonary tissue (Airways fibrosées) compared to healthy tissue (Aiways sain);
- Figure 3 Example morphometric airway enumeration by performing the method according to the invention to mass spectrometry imaging data sets of two airway samples of two fibrotic tissue;
- Figure 4 Analysis and determination of the airway surface of rat lungs by extrapolation of morphological data obtained by the method according to the invention from the imaging data of said samples;
- Figure 5 Representation of the distribution of two ions in a skin sample by mass spectrometry imaging and characterization of their spatial arrangement in the sample for the identification of tissues (eg epidermis and stratum corneum)
- tissues eg epidermis and stratum corneum
- FIG. 6 Schematic representation of the patterns and textures obtained from mass spectrometry imaging data sets of a section of biological tissue on a petri dish, by the method according to the invention applied to the counting and typing of cells in said section of biological tissue;
- Figure 7 Schematic representation of different morphometric criteria for a target ion that can be used to discriminate between two regions of interest in samples A and B having means of identical intensities for said target ion, not allowing them to differentiate them alone. .
- the distribution can thus vary in terms of number of objects, surface of the object, dispersion in the region of interest, shape, variability of the surface of an object to other, etc. ;
- FIG. 8 Schematic representation of certain steps of the characterization and / or identification method according to the invention, resulting in the extraction of morphometric criteria for the ion having an m / z of 91.5895 in a sample: molecular image is subjected to segmentation to obtain a binary image which is processed to characterize the distribution of said ion in number of objects and surface of said objects;
- Figure 9 Comparison for a reference tissue (control tissue) and a tissue of interest (treated tissue) of the m / z 718,505 distribution at the molecular image showing the intensity of the distribution and the image binary highlighting the surface difference of the distribution of said m / z.
- the subject of the invention is therefore a method for characterizing a sample by mass spectral imaging (ISM) according to which a spatial arrangement of at least one ion in said sample is characterized from the imaging data associated with said ion in terms of morphometry and / or texture.
- ISM mass spectral imaging
- the distribution of the ion in the sample is no longer only characterized according to the intensity but also according to the forms / objects that this distribution draws and associated measures (surface, volume, etc.) and / or the arrangement of these forms / objects between them. It is then easily possible to discriminate two samples which, although having an identical average intensity for a given ion, have different morphometric and / or texture characteristics associated with said ion.
- the method according to the invention can be applied to any type of sample that can be analyzed by a vacuum mass spectrometer (MALDI) or ambient atmosphere (LAESI, DESI), whether organic or inorganic, liquid or solid.
- MALDI vacuum mass spectrometer
- LAESI ambient atmosphere
- DESI ambient atmosphere
- the method according to the invention is particularly suitable for the characterization of biological tissues, of animal or vegetable origin.
- tissue is generally meant a set of cells of the same origin and grouped into a functional unit to contribute to the same function.
- a tissue may be an organ, an organ fragment, or a specific region of an organ, possibly comprising several sets of cells.
- the tissue may be a tumor located within an organ.
- the sample may consist of a histological section of tissue (s), of which regions of interest have optionally been characterized beforehand by staining and / or by their molecular signatures. More generally, the method according to the invention can be used to further characterize all or part of regions of interest of a sample previously identified by any method of the state of the art.
- the method according to the invention can also be used to characterize biological fluids, such as blood, plasma, serum, saliva, cerebrospinal fluid, urine, etc.
- the method according to the invention can also be used to characterize environmental samples, such as samples of soil, water, plants, etc.
- the method according to the invention is used to characterize objects, such as electronic components, biomaterials, capsules, high-precision parts, etc.
- characterizing a sample it is intended to associate distinctive / clean properties of said sample, said properties making it possible in particular to discriminate / identify it from other samples.
- imaging data and more particularly spectra, are used to determine for a given ion the spatial arrangement that it takes in said sample.
- the data associated with an m / z ratio characteristic of a given ion are used more particularly.
- the expression "m / z ratio”, or “mass-to-charge ratio” denotes a physical quantity characteristic of an ion, where m represents the mass and z the valence of said ion.
- mass spectrometry imaging a given ion can correspond to several m / z ratios.
- the "spatial arrangement" or “distribution” of an ion or m / z ratio refers to the form (s) that the presence of said ion or m / z ratio in the sample indicates.
- the spatial arrangement of the studied ion (s) is determined in such a way as to associate one or more shapes, dimensions, and so on.
- the imaging data associated with the presence of at least one ion in the sample is used to define morphometric and / or texture data representative of the distribution of said ion in the sample, regardless of the variation of the intensities associated with said sample. ion in said sample.
- the step of characterizing the spatial arrangement of at least one ion in the sample uses a pattern recognition and / or texture analysis method for segmenting the imaging data of said ion into regions of the region. distinct interests and / or draw outlines of patterns.
- a technique of mathematical morphology such as the watershed technique, the Hough transform, especially in its generalized form, spatial dependence matrices of shades of gray, and so on.
- “Morphometric data” or “morphometric characteristics” means data / characteristics relating to the geometric shape or shapes, or patterns, formed by the the presence of the ion in the sample (or the ratio m / z associated with said ion), and / or their mathematical dimensions, such as a surface, a volume, a diameter, a radius, a length, a width , a thickness, etc. In some cases, the patterns may consist of writing elements, such as numbers, letters, words, and so on.
- “texture data” or “texture characteristics” is meant data / characteristics relating to an arrangement of the patterns with each other in the sample, such as the number of repeated patterns, a distance, a dispersion between said patterns, etc. .
- Reference sample means a sample of the same kind and / or origin as the sample of interest.
- the reference sample consists of a biological tissue of the same nature from a control subject.
- m / z ions or ratios of said sample it is possible to characterize the spatial arrangement of one or more m / z ions or ratios of said sample, taken together or separately.
- a given pattern can be associated with the presence and distribution of several m / z ratios simultaneously taken into consideration.
- different patterns for different m / z ratios in the same sample, simultaneously or sequentially According to the invention, these different m / z ratios can be representative of the same ion or of different ions.
- the method according to the invention comprises a prior step of acquiring imaging data from which the morphometric and / or texture characteristics of at least one m / z ratio are determined.
- the method according to the invention can be implemented with any known mass spectrometry imaging technique, and in particular MALDI, LDI, DESI, LESA, LAESI imaging ("Laser Ablation Electrospray Ionization"), DART ("Direct Analysis in Real Time “), SIMS, JEDI (" Jet Desorption Electrospray Ionization "), LAMMA (" Laser Microprobe Mass Analysis "), SMALDI (" Scanning Microprobe Matrix Assisted Laser Desorption Ionization ”) in combination with different types of analyzers, such as TOF ("Flight Time”), Orbitrap, FTICR ("Fourier Transform Ion Cyclotron Resonance"), Quadripole ("single or triple”), ICP MS etc.
- MALDI mass Ablation Electrospray Ionization
- DART Direct Analysis in Real Time
- SIMS SIMS
- JEDI Jet Desorption Electrospray Ionization
- LAMMA Laser Microprobe Mass Analysis
- SMALDI Scanning Microprobe Matrix Assisted
- the morphometric and / or texture characteristics associated with at least one ion directly on an ionic image of the sample, for example using MALDI imaging.
- the morphometric and / or texture characteristics associated with the ions analyzed can be generated in the form of images reproducing the sample, so that their spatial arrangements can be visualized therein.
- it is possible to superpose different images of the sample for example to simultaneously display data of several ions, and even histological or other data obtained by other methods of analysis such as optical microscopy, histological staining etc.
- the method of characterizing a sample according to the invention comprises the steps of: a) acquiring data by ISM, for at least one ion in the sample; then b) Characterizing a spatial arrangement of said at least one ion in the sample from the data relating to the positions of said at least one ion, according to morphometric and / or texture characteristics.
- said ion is characterized by its morphometric and texture characteristics, specific to said sample.
- the same ion in another sample may have different morphometric and textural characteristics, even if the molecular profiles of the two samples are identical.
- the method according to the invention makes it possible to acquire another degree of information for an ion in a sample, which can be taken into account alone or in combination with any other data / characteristic of the sample and / or the ion considered.
- the method according to the invention makes it possible to simultaneously display all of these data / characteristics directly on the same ionic image of the sample.
- the method of characterizing a sample according to the invention comprises the steps of: c) Segmenting sample imaging data into regions of interest representative of the molecular intensity profiles of the sample; and
- Step c) consists in a segmentation of the sample according to the molecular profiles within said sample.
- Each region of interest corresponds to a molecular profile obtained from the mass spectra of the sample.
- Different spectral characteristics can be conventionally used to obtain the molecular profiles of said sample, and in particular the intensity of the peaks of the mass spectra, the signal-to-noise ratio (S / N), the peak area, and so on.
- the image data associated with the selected region of interest are processed to characterize the spatial arrangement of the set of ions of said selected region of interest. It is the data of the set of ions in the region of interest that are taken into account and analyzed to identify the characteristic patterns and textures of said region of interest. Again, according to the invention, it is possible to reconstruct a digital image of the sample from these data so as to display the results directly on an image of the sample.
- steps c) and d) can be implemented independently of steps a) and b), that is to say without implementation of steps a) and b).
- steps a) and b) it is possible to successively implement steps a) and b), then c) and d), or conversely, so as to obtain different degrees of information in terms of morphometry and texture the ion or ions considered in the sample considered.
- step d) it is possible to apply to several or all regions of interest identified in step c).
- the method of characterization comprises the step of: e) Realizing a database (BDD) comprising morphometric and / or texture data of a plurality of ions in said sample.
- BDD database
- the characterization method comprises the step of: f) Creating a model comprising morphometric and / or texture data of a plurality of ions in said sample.
- a plurality means two or more.
- a “model” refers to a set of data, in this case characteristics derived from the sample, including intensity data, morphometric and / or texture data, etc., which have been modeled in particular. to define dependencies and / or relations between said data, and which is representative of the sample considered.
- a “database” refers to a database in which a set of raw data is stored.
- this database (BDD) or this model can be implemented (step g) with the ISM spectral data of at least one ion in said sample, and / or physicochemical, physiological and / or biological characteristics of the sample.
- the database (BDD), or model is implemented with data obtained by a histological, chemical or other study of the sample, or an identical sample, to define different areas of interest .
- Such a database (BDD), or model may be particularly useful for rapidly and automatically identifying a tissue, a region of a tissue, a cell type, a physiological state of a tissue, for example a healthy or pathological tissue, etc.
- the database, or model can be used for identification and counting, or counting, cell in a biological sample.
- the subject of the invention is also a method for identifying a sample by mass spectrometry imaging in which: i) A database or model is created with morphometric and / or texture data associated with at least one reference ion, obtained from several reference samples, said morphometric and / or texture data. being representative of the spatial arrangement of said at least one reference ion in said reference samples;
- Morphometric and / or texture data (surface, volume, shape, pattern, repetition, etc.) associated with at least one ion are recorded in the sample to be identified;
- Morphometric data and / or textures of step ii) are compared with the morphometric data and / or textures contained in the database, or the model, of step i).
- step ii) an ion corresponding to an ion of the database or of the model is analyzed.
- the database, or the model comprises morphometric and / or texture data associated or not with a plurality of reference ions.
- a set of morphological data and textures for several ions is established so as to characterize in the most reliable manner each of the reference samples.
- step iii) is carried out with an analysis of the similarities and / or the differences between the different sets of reference data and the sets of data of the sample to be identified, so as to select the one or more reference samples with similar or identical morphometric characteristics, thereby identifying the sample.
- the method according to the invention may, for example, make it possible to identify the nature and / or origin of a biological tissue, a cell type, the stage of evolution of a disease, etc.
- the invention also relates to a method for identifying a molecule of interest in a sample, according to which i) comparing morphometric and / or texture data associated with a plurality of ions in said sample with morphometric and / or texture data associated with a plurality of ions in a reference sample;
- Such a method makes it possible to discover and identify a new molecule in a sample of interest.
- the method of identifying a new molecule of interest in a sample of interest comprises, beforehand, a step of acquiring ISM data specific to said sample and characterizing the distribution of a sample of interest. or several ions by morphometric and / or texture characteristics.
- the nature of the sample is known and it is desired to identify one or more markers specific to said sample, that is to say which are not present in the reference sample, or then that has morphometric characteristics and / or different textures compared to those they have in the reference sample ( Figure 7).
- the morphometric characteristics taken into account for a given ion may for example be the number of objects, the average of the surface of the objects, the dispersion, the shape, the variability of the surfaces, etc.
- a reference sample of the same nature and / or of the same type as the sample of interest is preferably used.
- step i) can be implemented by querying a database (BDD) compiling a plurality of sets of reference data of the reference sample, or by comparing the data of the sample to a compiling model a plurality of reference data sets of the reference sample. .
- BDD database
- the corresponding ion (s) is associated therewith to go back to the molecule associated with each of the ions.
- This method is particularly useful in the pharmaceutical or medical field, particularly for identifying new biomarkers.
- the invention also relates to a computer readable data medium comprising instructions executable by the computer and adapted to allow a computer system to perform at least one step of the method of characterizing a sample according to the invention. and / or at least one step of the method of identifying a sample or a molecule of interest in a sample according to the invention.
- the invention provides a computer program comprising program code instructions for performing all or part of the steps set forth above, when said program is run on a computer.
- the computer program includes program code instructions for performing at least the step of characterizing the spatial arrangement of one or more ions in a sample of interest.
- the computer-readable data carrier, or program, according to the invention comprises a database, or a model, comprising morphometric and / or texture data of at least one ion in at least one sample, and preferentially of a plurality of ions for a plurality of samples.
- the method according to the invention can be used for the identification of markers, and in particular biomarkers. Indeed, in the case of biological samples, it is possible to identify variations of morphology present between two conditions (for example patient vs. healthy, treated vs. vehicle, exposed vs. unexposed, etc.). In particular, according to the invention, it is possible to study more particularly ions specifically present in a region of interest, previously identified by a macroscopic and / or microscopic study of the structure of the sample for example.
- Filtering and molecular classification The method according to the invention can be used for molecular filtering purposes.
- a particular morphometry for example, a star shape of X mm 2 of minimum area
- a particular morphometry for example, a star shape of X mm 2 of minimum area
- Such filtering can also be used to classify samples.
- a resemblance score based on the intensity and shape information. It is possible to automatically recognize in a sample, such as a biological tissue, the presence of a given form which is for example characteristic of a physiological state. It then becomes possible to establish rankings based on more accurate and reliable scores than those based solely on the intensity criterion, which may vary according to sample preparation (matrix deposition, drying time, freezing, type of sample). tissue, etc.) contrary to the morphometric and texture characteristics that are preserved regardless of the chosen protocol.
- BDD database
- model compiling the specifications of several samples of the same population, such as several samples of the same biological tissue or biological tissues. same origin / nature. From such a database, or from such a model, it is easy to identify a new sample of the same population or to identify with certainty a sample in a population.
- Figure 2 shows more particularly the results obtained from the morphometric characterization of the sets of imaging data obtained for the sample of healthy lung tissue and the fibrosed lung tissue sample.
- two elliptical morphometries are identified, one of which has a ratio between the minor axis and the major axis tending towards a (fibrosed tissue).
- the use of these morphometric signatures makes it possible to characterize two forms of airways, respectively healthy (oval type 1) and fibrotic (oval type 2).
- the method according to the invention can also be used in the field of cell or bacterial counting, especially in the context of an environmental or health assessment.
- cell wall molecules lipids, for example
- the forms are isolated with a molecular factor, whereas up to now this has been done by optical signals, and especially by optical microscopy or flow cytometry.
- the invention makes it possible to exceed the detection sensitivity limits obtained by the optical signals, but also the working speed, the precision (fewer false positives) and to increase the number of parameters simultaneously analyzed.
- the cells can be enumerated and recognized on the basis of their morphology, while indicating the molecular profile as the metabolic activity of the identified cell. It is therefore possible to count multiple cell types, biological or histological and their physiological activity.
- Figure 3 depicts automated morphometric counting on sets of imaging data obtained for airway samples from two fibrosed tissues. Automated detection of oval forms makes it possible to identify and enumerate the histological structures of interest (here the respiratory tract) without correlation with adjacent histological stains.
- Kinetic study of morphometries The method according to the invention can also be used in the kinetic study of forms, in particular for monitoring the evolution over time of the morphometry (shape, surface, volume, etc.) of elements of interest (molecules, ions, m / z) in the sample.
- morphometry shape, surface, volume, etc.
- elements of interest molecules, ions, m / z
- stages of evolution of the forms in a biological sample in agreement with other stage studies of the biological sample.
- FIG. 4 illustrates the application of the method according to the invention to the study of the evolution of the constriction of airways in a fibrosed tissue. As shown, it is possible with the method of the invention to determine the surface of the airways over time and thus to follow the impact of fibrosis on the constriction of the airways.
- Cell or tissue typing The method according to the invention can be used to identify cell types or tissue sub-structures in a plant or animal biological sample. This cell typing is made possible by the morphometries of each cell that are specific to them. Thus, depending on the cellular morphometries obtained, it is possible to determine the cellular type or types contained within one or more samples. Recently, instrumental developments have made it possible to lower the spatial resolution of MALDI imaging below 10 ⁇ . As for the SIMS technology, it is possible to obtain a resolution of less than 1 ⁇ in routine. Considering that the average size of a cell is 10-15 ⁇ , it can be considered that mass spectrometry imaging reaches a cellular resolution making it possible to discriminate them in molecular data sets.
- Figure 5 illustrates the application of the method of the invention in the context of tissue typing on a section of skin tissue and without histological correlation.
- the zones of the tissue (Stratum corneum and Epidermis) are identified thanks to their specific morphometries obtained after acquisition in ISM.
- Figure 6 schematically illustrates the application of the method of the invention in the context of enumeration and cell typing directly on / in a biological sample and without histological correlation.
- the cells of the sample tissue, solution or cell culture
- ISM immunosuppression system
- Example 1 Procedure for characterizing a sample or region of interest by combining spectral and spatial information.
- the purpose of this example is to highlight the differences in information that may arise from the same set of imagery data acquired for a sample, depending on the fact that only the intensities data for a report are taken into account.
- m / z given in said sample or the morphometric and texture characteristics associated with this ratio m / z.
- step 3 can be implemented via the algorithm below: "As long as we have not traversed the entire image: Browse the image line by line
- the test step 7 can for example be implemented by making an opening (Erosion ⁇ , followed by a dilation ⁇ ) by a structuring element (ES) having one or more properties to be tested. If the pixel addition of the opening by TES is non-zero, then the image / region has the property.
- Example 2 Procedure for identifying peaks of interest and associated biomarkers from morphometric data
- lungs of rats Five lungs of rats were used for this study. Three are derived from rats treated with bleomycin (Apollo Scientific, UK) by the airway (oropharyngeal aspiration route) at a dose of 1 mg / kg for seven days and two from saline-treated animals. similar for the same duration. All rats were Sprague Dawley, Cree: CD (SD) males. Both groups were sacrificed twenty-two days after the start of the experiment. The lungs were swollen with agarose, fixed in formalin with 10% neutral buffer solution and frozen at -80 ° C. The animal experiments were consistent with the 1986 Animais (Scientific Procedures) Act.
- the matrix used was a solution of 9AA at 5 mg / mL in a solvent MeOH / H2O (4: 1 v: v); it was deposited in ten successive layers with a Suncollect automated depot (SunChrom GmBH, Friedrichsdorf, Germany).
- the first layer was applied at a flow rate of 10 ⁇ / ⁇
- the second layer at a flow rate of 20 ⁇ / ⁇ and the following at 30 ⁇ / ⁇ .
- a MALDI-FTICR mass spectrometer (7T Solarix, Bruker Daltonics, Bremen, Germany) equipped with a SmartBeam II laser used at 80% output energy and a repetition rate of 1000 Hz was used to acquire mass in "full scan" mode for m / z between 100 and 1000 with a spatial resolution of 30 ⁇ and in negative mode. Each mass spectrum corresponds to the accumulation of 500 consecutive laser shots at the same location.
- An internal calibration was made using the 9AA matrix as well as phospholipids between m / z 200 and 900.
- the mass spectrometer was controlled by the use of FTMS Control 2.0 and Flexlmaging 4.0 software (Bruker Daltonics, Bremen, Germany).
- the areas chosen for acquisition were pulmonary airways because bleomycin should induce changes.
- the peaks of the transformed average spectrum of a condition exceeding a threshold are considered as peaks.
- the transformation used was the subtraction of the median of the signal without the null values divided by the absolute deviation at the median of the signal without the null values multiplied by 1.4826. This gives an approximation of the signal-to-noise ratio at each point. The idea being that the noise follows a roughly normal distribution and that the real signal is present in less than half of the aggregated measurements in the medium spectrum, the elimination of null values was done because the signal appears to have undergone destructive wavelet compression by FTMS Control 2.0.
- the threshold for this study is 1 to have as few false negatives as possible but excluding as much noise as possible.
- the peak intensity image in the peak window is stretched in a grayscale palette having 8 bits of depth and then exported in JPEG format without compression.
- the choice to use only one channel was made to facilitate visual inspection of the results.
- Approximately 2000 ions to be imaged over 12 regions (6 in condition 1 and 6 in condition 2) were obtained.
- Welch t-tests were made between the two conditions, one based on the number of objects per unit area and the other on the average area of objects per unit area.
- the Welch t-test was chosen because it applies well to unpaired small samples and it is uncertain whether the variances of the two populations are equal.
- a Mann-Whitney-Wilcoxon test could otherwise have been used.
- biomarkers were identified as significantly different between the control condition and the treated condition based on the use of morphometric criteria, some of whose biomarkers are only identified using morphometric criteria.
- a membrane component involved in apoptosis could be identified (m / z 718.505), which was not detectable by considering only intensities (NS: Not significant; *: significant difference)
- a mathematical model was performed based on the identified potential biomarkers of the condition being treated. This model was used for exploratory purposes to classify test samples on the basis of extracted morphometric characters.
- Example 3 Other Applications Numerous applications are possible, and may in particular be implemented using the steps described in Example 1 or Example 2.
- the invention can be used in the medical and pharmaceutical field, to identify new biomarkers, for filtering and molecular classification, cell and morphological enumeration, study of the evolution over time of a disease or a treatment, cell or tissue typing, etc.
- the invention finds applications in other fields, such as quality control, art, comparative and automated analysis of objects, the study of the composition of materials, etc.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
Description
Claims
Priority Applications (9)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP15759917.6A EP3195345B1 (fr) | 2014-08-26 | 2015-08-24 | Méthode de caractérisation d'un échantillon par imagerie par spectrométrie de masse |
KR1020177008286A KR20170047337A (ko) | 2014-08-26 | 2015-08-24 | 질량 분광 이미징에 의한 샘플의 특성화 방법 |
DK15759917.6T DK3195345T3 (da) | 2014-08-26 | 2015-08-24 | Fremgangsmåde til karakterisering af en prøve ved billeddannelse ved massespektrometri |
CN201580046099.1A CN106796197A (zh) | 2014-08-26 | 2015-08-24 | 通过质谱成像表征样品的方法 |
JP2017511235A JP6860475B2 (ja) | 2014-08-26 | 2015-08-24 | 質量分析イメージングによってサンプルを特徴づける方法 |
CA2959111A CA2959111C (fr) | 2014-08-26 | 2015-08-24 | Methode de caracterisation d'un echantillon par imagerie par spectrometrie de masse |
ES15759917T ES2885691T3 (es) | 2014-08-26 | 2015-08-24 | Método de caracterización de una muestra mediante obtención de imágenes por espectrometría de masas |
AU2015308264A AU2015308264A1 (en) | 2014-08-26 | 2015-08-24 | Method for characterising a sample by mass spectrometry imaging |
US15/438,946 US10141168B2 (en) | 2014-08-26 | 2017-02-22 | Method for characterising a sample by mass spectrometry imaging |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR1457993A FR3025317B1 (fr) | 2014-08-26 | 2014-08-26 | Methode de caracterisation d'un echantillon par imagerie par spectrometrie de masse |
FR1457993 | 2014-08-26 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/438,946 Continuation-In-Part US10141168B2 (en) | 2014-08-26 | 2017-02-22 | Method for characterising a sample by mass spectrometry imaging |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016030621A1 true WO2016030621A1 (fr) | 2016-03-03 |
Family
ID=52737144
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/FR2015/052254 WO2016030621A1 (fr) | 2014-08-26 | 2015-08-24 | Méthode de caractérisation d'un échantillon par imagerie par spectrométrie de masse |
Country Status (11)
Country | Link |
---|---|
US (1) | US10141168B2 (fr) |
EP (1) | EP3195345B1 (fr) |
JP (1) | JP6860475B2 (fr) |
KR (1) | KR20170047337A (fr) |
CN (1) | CN106796197A (fr) |
AU (1) | AU2015308264A1 (fr) |
CA (1) | CA2959111C (fr) |
DK (1) | DK3195345T3 (fr) |
ES (1) | ES2885691T3 (fr) |
FR (1) | FR3025317B1 (fr) |
WO (1) | WO2016030621A1 (fr) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108763871A (zh) * | 2018-06-05 | 2018-11-06 | 南京诺禾致源生物科技有限公司 | 基于第三代测序序列的补洞方法及装置 |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB201609747D0 (en) * | 2016-06-03 | 2016-07-20 | Micromass Ltd | Data directed desi-ms imaging |
GB201609743D0 (en) | 2016-06-03 | 2016-07-20 | Micromass Ltd | Mass Spectrometry imaging |
KR101878360B1 (ko) | 2017-04-12 | 2018-07-13 | 두산중공업 주식회사 | 리테이너 조립구조를 포함하는 가스터빈 블레이드 조립체 및 이를 포함하는 가스터빈 |
AU2018282883B2 (en) * | 2017-06-12 | 2024-03-28 | Protein Dynamic Solutions, Inc. | Method and system for analysis of crystals and crystallization |
US10347480B2 (en) * | 2017-09-25 | 2019-07-09 | Bruker Daltonik, Gmbh | Method for evaluating the quality of mass spectrometric imaging preparations and kit-of-parts therefor |
CN110044997B (zh) * | 2018-01-15 | 2023-08-04 | 中国医学科学院药物研究所 | 一种体内药物的离子强度虚拟校正和定量质谱成像分析方法 |
CA3125695A1 (fr) * | 2019-01-22 | 2020-07-30 | Imabiotech | Methode d'evaluation de changements moleculaires lies a un effet de molecule dans un echantillon biologique |
DE102019109771B4 (de) | 2019-04-12 | 2022-06-30 | Bruker Daltonics GmbH & Co. KG | Auswertung komplexer Massenspektrometrie-Daten von biologischen Proben |
CN112198215B (zh) * | 2020-09-23 | 2022-07-22 | 湖南农业大学 | 一种分析钩吻内次生代谢产物在组织中分布的方法 |
CN113034491B (zh) * | 2021-04-16 | 2021-10-08 | 北京安德医智科技有限公司 | 一种冠脉钙化斑块检测方法及装置 |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120053447A1 (en) * | 2009-02-06 | 2012-03-01 | Simon Duchesne | Methods and apparatuses for quantitatively determining the likelihood of a disease |
Family Cites Families (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1302465A1 (fr) * | 2001-10-11 | 2003-04-16 | BRACCO IMAGING S.p.A. | Amélioration de l'imagerie IRM par le biais d'une fixation réversible à un complexe paramagnétique |
US6756586B2 (en) * | 2001-10-15 | 2004-06-29 | Vanderbilt University | Methods and apparatus for analyzing biological samples by mass spectrometry |
JP2007226054A (ja) * | 2006-02-24 | 2007-09-06 | Fuji Xerox Co Ltd | 画像形成方法および画像形成装置 |
DE102007059681A1 (de) * | 2007-12-12 | 2009-06-18 | Omya Development Ag | Komposits aus anorganischen Mikropartikeln mit phosphatierter Oberfläche und Nano-Erdalkalikarbonatpartikeln |
FR2935822B1 (fr) * | 2008-09-05 | 2010-09-17 | Commissariat Energie Atomique | Procede de reconnaissance de formes et systeme mettant en oeuvre le procede |
US8321144B2 (en) * | 2008-10-23 | 2012-11-27 | Microsoft Corporation | Non-contiguous regions processing |
US8488863B2 (en) * | 2008-11-06 | 2013-07-16 | Los Alamos National Security, Llc | Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials |
JP5359924B2 (ja) * | 2010-02-18 | 2013-12-04 | 株式会社島津製作所 | 質量分析装置 |
WO2012155134A2 (fr) * | 2011-05-12 | 2012-11-15 | The Johns Hopkins University | Réactifs de dosage pour kit de diagnostic de la neurogranine |
US10386371B2 (en) * | 2011-09-08 | 2019-08-20 | The Regents Of The University Of California | Metabolic flux measurement, imaging and microscopy |
JP6006548B2 (ja) * | 2012-07-04 | 2016-10-12 | 株式会社 資生堂 | 成分分布可視化装置、成分分布可視化方法、及び成分分布可視化プログラム |
US9552649B2 (en) * | 2012-11-20 | 2017-01-24 | Koninklijke Philips N.V. | Integrated phenotyping employing image texture features |
JP6546177B2 (ja) * | 2013-09-13 | 2019-07-17 | ザ ボード オブ トラスティーズ オブ ザ レランド スタンフォード ジュニア ユニバーシティー | 質量タグ及び二次イオン質量分析計を用いた組織の多重化イメージング |
-
2014
- 2014-08-26 FR FR1457993A patent/FR3025317B1/fr active Active
-
2015
- 2015-08-24 WO PCT/FR2015/052254 patent/WO2016030621A1/fr active Application Filing
- 2015-08-24 AU AU2015308264A patent/AU2015308264A1/en not_active Abandoned
- 2015-08-24 ES ES15759917T patent/ES2885691T3/es active Active
- 2015-08-24 KR KR1020177008286A patent/KR20170047337A/ko unknown
- 2015-08-24 CA CA2959111A patent/CA2959111C/fr active Active
- 2015-08-24 DK DK15759917.6T patent/DK3195345T3/da active
- 2015-08-24 EP EP15759917.6A patent/EP3195345B1/fr active Active
- 2015-08-24 JP JP2017511235A patent/JP6860475B2/ja active Active
- 2015-08-24 CN CN201580046099.1A patent/CN106796197A/zh active Pending
-
2017
- 2017-02-22 US US15/438,946 patent/US10141168B2/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120053447A1 (en) * | 2009-02-06 | 2012-03-01 | Simon Duchesne | Methods and apparatuses for quantitatively determining the likelihood of a disease |
Non-Patent Citations (13)
Title |
---|
AHMED S. ATTIA ET AL: "Monitoring the Inflammatory Response to Infection through the Integration of MALDI IMS and MRI", CELL HOST & MICROBE, vol. 11, no. 6, 1 June 2012 (2012-06-01), pages 664 - 673, XP055198570, ISSN: 1931-3128, DOI: 10.1016/j.chom.2012.04.018 * |
D. BONNEL ET AL., ANAL BIOANAL CHEM, 2011 |
J. STAUBER ET AL., J PROTEOME RES, vol. 7, no. 3, 2008, pages 969 - 78 |
JULIA D. WULFKUHLE ET AL: "Early detection: Proteomic applications for the early detection of cancer", NATURE REVIEWS CANCER, vol. 3, no. 4, 1 April 2003 (2003-04-01), pages 267 - 275, XP055091735, ISSN: 1474-175X, DOI: 10.1038/nrc1043 * |
KARL E MAYERHOFER ET AL: "Three dimensional analysis of self-structuring organic thin films using time-of-flight secondary ion mass spectrometry", THIN SOLID FILMS, ELSEVIER-SEQUOIA S.A. LAUSANNE, CH, vol. 519, no. 18, 5 April 2011 (2011-04-05), pages 6183 - 6189, XP028227208, ISSN: 0040-6090, [retrieved on 20110415], DOI: 10.1016/J.TSF.2011.04.018 * |
LU-TAO WENG ET AL: "Morphology and Miscibility of Blends of Ethylene-Tetrafluoroethylene Copolymer/Poly(methyl methacrylate) Studied by ToF SIMS Imaging", MACROMOLECULES, vol. 31, no. 3, 1 February 1998 (1998-02-01), pages 928 - 932, XP055198254, ISSN: 0024-9297, DOI: 10.1021/ma961916m * |
LUXEMBOURG, ANAL. CHEM, 2004 |
PER MALMBERG ET AL: "Subcellular localisation of cholesterol and phosphocholine with pattern-recognition-imaging-TOF-SIMS", SPECTROSCOPY, vol. 18, no. 4, 1 January 2004 (2004-01-01), pages 503 - 511, XP055198349, ISSN: 0712-4813, DOI: 10.1155/2004/928263 * |
RITA CASADONTE ET AL: "Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues", PROTEOMICS, vol. 14, no. 7-8, 5 April 2014 (2014-04-05), pages 956 - 964, XP055198420, ISSN: 1615-9853, DOI: 10.1002/pmic.201300430 * |
STEPHEN E. REICHENBACH ET AL: "Interactive spatio-spectral analysis of three-dimensional mass-spectral (3DxMS) chemical images", SURFACE AND INTERFACE ANALYSIS, vol. 43, no. 1-2, 9 January 2011 (2011-01-09), pages 529 - 534, XP055198365, ISSN: 0142-2421, DOI: 10.1002/sia.3553 * |
T. ALEXANDROV ET AL., J CANCER RES CLIN ONCOL, vol. 139, no. 1, 2012, pages 85 - 95 |
T. ALEXANDROV ET AL., J PROTEOMICS, vol. 75, no. 1, 2011, pages 237 - 45 |
THEODORE ALEXANDROV ET AL: "MALDI-imaging segmentation is a powerful tool for spatial functional proteomic analysis of human larynx carcinoma", JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, SPRINGER, BERLIN, DE, vol. 139, no. 1, 6 September 2012 (2012-09-06), pages 85 - 95, XP035157149, ISSN: 1432-1335, DOI: 10.1007/S00432-012-1303-2 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108763871A (zh) * | 2018-06-05 | 2018-11-06 | 南京诺禾致源生物科技有限公司 | 基于第三代测序序列的补洞方法及装置 |
Also Published As
Publication number | Publication date |
---|---|
EP3195345A1 (fr) | 2017-07-26 |
CN106796197A (zh) | 2017-05-31 |
ES2885691T3 (es) | 2021-12-15 |
FR3025317A1 (fr) | 2016-03-04 |
EP3195345B1 (fr) | 2021-06-16 |
KR20170047337A (ko) | 2017-05-04 |
FR3025317B1 (fr) | 2022-09-23 |
US20170221687A1 (en) | 2017-08-03 |
AU2015308264A1 (en) | 2017-03-09 |
DK3195345T3 (da) | 2021-09-13 |
US10141168B2 (en) | 2018-11-27 |
JP2017529527A (ja) | 2017-10-05 |
JP6860475B2 (ja) | 2021-04-14 |
CA2959111C (fr) | 2023-12-12 |
CA2959111A1 (fr) | 2016-03-03 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP3195345B1 (fr) | Méthode de caractérisation d'un échantillon par imagerie par spectrométrie de masse | |
US11842483B2 (en) | Systems for cell shape estimation | |
Buchberger et al. | Mass spectrometry imaging: a review of emerging advancements and future insights | |
US20200302603A1 (en) | Method of computing tumor spatial and inter-marker heterogeneity | |
EP3218843B1 (fr) | Classement des noyaux dans des images histologiques | |
US20240273719A1 (en) | Image enhancement to enable improved nuclei detection and segmentation | |
Tuck et al. | MALDI-MSI towards multimodal imaging: challenges and perspectives | |
Boskamp et al. | A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples | |
JP6517788B2 (ja) | 適応的病理組織画像分解のためのシステム及び方法 | |
CN112703531A (zh) | 生成组织图像的注释数据 | |
Cordero Hernandez et al. | Targeted feature extraction in MALDI mass spectrometry imaging to discriminate proteomic profiles of breast and ovarian cancer | |
Xin et al. | Expected affine: a registration method for damaged section in serial sections electron microscopy | |
Blanc et al. | In tissue spatial single-cell metabolomics by coupling mass spectrometry imaging and immunofluorescences | |
FR3029671A1 (fr) | Procede pour l'analyse assistee par ordinateur d'une ou plusieurs coupes de tissu du corps humain ou animal | |
EP2834628A1 (fr) | Methode de determination de la nature tumorale ou non tumorale ou du type de tumeur d'un fragment d'organe solide humain ou animal | |
Soans et al. | Automated protein localization of blood brain barrier vasculature in brightfield IHC images | |
Safa'a et al. | Histopathological prostate tissue glands segmentation for automated diagnosis | |
US7502693B1 (en) | Spectral feature-based identification of substances | |
Castro | High-throughput matrix-assisted laser desorption/ionization mass spectrometry for single-cell and single-organelle measurements | |
Ando et al. | Detection of Cell Nuclei using LadderNet | |
Sharman | Heterogeneous Molecular Signatures in Staphylococcus aureus Infection Assessed by Multimodal Imaging | |
YOUSRA | Segmentation des lésions COVID-19 à l'aide d'images de tomodensitométrie pulmonaire: étude comparative basée sur des méthodes de seuillage et des modèles de contours actifs |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 15759917 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2959111 Country of ref document: CA |
|
ENP | Entry into the national phase |
Ref document number: 2017511235 Country of ref document: JP Kind code of ref document: A |
|
REEP | Request for entry into the european phase |
Ref document number: 2015759917 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2015759917 Country of ref document: EP |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
ENP | Entry into the national phase |
Ref document number: 2015308264 Country of ref document: AU Date of ref document: 20150824 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 20177008286 Country of ref document: KR Kind code of ref document: A |