Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H30/00—ICT specially adapted for the handling or processing of medical images
  • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/25—Fusion techniques
  • G06F18/251—Fusion techniques of input or preprocessed data
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  • 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
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/20—Image preprocessing
  • G06V10/24—Aligning, centring, orientation detection or correction of the image
• • 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
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • 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
  • G06T2207/10024—Color image
• • 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
  • G06T2207/20—Special algorithmic details
  • G06T2207/20021—Dividing image into blocks, subimages or windows
• • 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

Definitions

• the present invention relates to a method for mass spectrometry analysis and histological classification of a tissue sample and the automated analysis thereof.
• the inventors developed a new imaging pipeline which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). It allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution.
• the imaging pipeline of the present invention was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features in order to identify metabolic profiles.
• Histology analysis is generally carried out on a stained tissue sections and concerns the tissue type, differentiation, presence of bacterial or parasitic pathogens, disease statuses, and content of foreign matter, like pesticides or drugs and their metabolites.
• An automated staining read-out remained illusive for a long time, due to different obstacles like staining intensities and cellular and nuclear overlay within the tissue.
• the European patent application EP 2 124 192 A1 pertains to a method for histologic classification of a tissue section but reveals no automated analysis thereof.
• Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry comprises the ionization of a sample or analyte and has been used for the determination of molecular masses, and for the identification and structural characterization of biological substances, particularly proteins and peptides.
• Imaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples.
• automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows.
• the present invention relates to a method of automatically selecting regions of interest, ROIs, for analyzing one complete tissue section, wherein the method comprises
• the method further comprises obtaining one or more custom masks of the one or more second images of (ii), and wherein combining masks in (iii) comprises combining masks selected from the first mask, the one or more second masks, and the one or more custom masks, respectively, based on predetermined set expressions, to obtain final regions of interest, ROIs.
• the method referred to herein may further comprise determining one or more second masks of (ii) associated with respective one or more second images based on predetermined criteria comprises using predetermined thresholds of color values or ranges of color values of pixels in an image of the one or more second images to determine regions containing pixels of similar color values in said image.
• the mass spectrometry data may be obtained by Matrix-assisted Laser Desorption (MALDI), Desorption Electrospray Ionization (DESI), Laser Ablation Electrospray Ionization (LAESI), Secondary ion Mass Spectrometry (SIMS), Matrix-assisted Laser Desorption Electrospray Ionization (MALDESI), Liquid Extraction Surface Analysis (LESA), Mass spectrometry imaging (MSI), or Inductively Coupled Plasma Mass Spectrometry Imaging (ICP-MSI), respectively.
• MALDI Matrix-assisted Laser Desorption
• DESI Desorption Electrospray Ionization
• LAESI Laser Ablation Electrospray Ionization
• SIMS Secondary ion Mass Spectrometry
• MALDESI Matrix-assisted Laser Desorption Electrospray Ionization
• LESA Liquid Extraction Surface Analysis
• MSI Mass spectrometry imaging
• ICP-MSI Inductively Coupled Plasma Mass Spec
• the mass spectrometry data is obtained by any one of MALDI, DESI, LAESI, SIMS, MALDESI, LESA, MSI and ICP-MSI, respectively. Most preferably, the mass spectrometry data is obtained by MALDI.
• the images of the tissue section may be selected from images obtained by multiplex immunohistochemical (IHC) staining, histochemical staining, endogeneous or exogeneous fluorescence staining, respectively, fluorescence in situ hybridization (FISH), or autofluorescence.
• IHC immunohistochemical
• FISH fluorescence in situ hybridization
• the mass spectrometry data may be represented in imzML format, mis format of fleximaging, slx/sbd format of SCiLS Lab, or a tabular format, respectively.
• the tissue section may comprise one tissue sample obtained by formalin-fixed paraffin-embedding (FFPE) or obtained by any other fixation technique, comprising ethanol fixation and cryofixation, respectively.
• FFPE formalin-fixed paraffin-embedding
• determining one or more second masks associated with respective one or more second images comprises determining multiple masks for the same image, said masks pertaining to multiple morphometric data, or data from multiple color components, such as Hematoxylin and eosin (H&E) and other histological stainings.
• H&E Hematoxylin and eosin
• ranges of color values of pixels are based on a magnitude of expression of a diagnostic feature, such as one or more biomarkers.
• the biomarker is a) HER2/neu and/or pan-cytokeratin (PC); b) Insulin and/or glucagon; c) PD1 and/or PD-L1 ; or d) Vimentin and/or pan-cytokeratin.
• the predetermined set expressions may represent semantics of tissue annotation to obtain the final ROIs, based on the first image, the one or more second images and the associated masks of the tissue section.
• analyzing the tissue section may comprise comparing mass spectrometry data and image data, respectively, derived from the final ROIs of the first image and the one or more second images of the tissue section, by statistical analysis.
• the method is performed simultaneously on multiple tissue samples individualized by one or more custom masks, wherein each custom mask is associated with an individual tissue sample.
• the present invention relates to the use of the method for diagnosis or stratification based on a human, animal or plant tissue samples.
• the use of the method may comprise that the samples are human tissue samples used in the diagnosis or stratification of cancer or diabetes.
• the present invention relates to the use of the method for therapy response prediction and prediction of organ rejection based on human or animal tissue.
• FIG. 1 A Graphical Abstract SPACIAL - immunohistochemistry-guided in situ metabolics.
• the inventors developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACIAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC).
• SPACIAL Spatial Correlation Image Analysis
• IHC multiplex immunohistochemistry
• FIG. 2 Workflow of immunohistochemistry-guided in situ metabolomics using the example of an islet of Langerhans.
• B The SPACIAL pipeline integrates molecular MALDI data and immunohistochemical data. The IHC images need to be co-registered to the coordinates of the mass spectra per pixel.
• the MALDI data file is used to generate an image of the measurement region, which can be used for precise co-registration with the tissue image and tissue stainings. Once co-registered, the staining images are scaled to match the resolution of the measurement and color values per pixel are used for the definition of regions or for pixel- accurate analyses of metabolic correlations or heterogeneity.
• FIG. 3 Multi-omics data integration via the SPACIAL method.
• Left Islet of Langerhans with immunohistochemical staining (glucagon in red, insulin in green).
• Middle Spatial distribution of 3-0 Sulfogalactosylceramide (m/z 778.5147).
• Right Data integration via SPACIAL, utilizing the IHC stainings to automatically identify alpha cells (semi-transparent, pixelated staining in red) and correlating metabolites.
• Lateral MALDI resolution (pixel) 15 pm.
• FIG. 4 Metabolic heterogeneity within and between islets of Langerhans in a pancreatic tissue section of one mouse (A-E).
• the column on the left shows multiplex immunostainings after MALDI imaging mass spectrometry.
• Alpha cells (red) and beta cells (green) are stained with glucagon and insulin, respectively.
• a tissue fold in the fifth islet (E) was excluded from analyses (dashed).
• the second and third columns show spatial correlation networks for metabolites in alpha and beta cells, respectively. Nodes and edges represent compounds and their spatial correlation.
• the networks shown here only include direct neighbors of the glucose 6-phosphate node and edges representing a correlation coefficient of at least 0.7. Scale bar, 150 pm.
• adenosine monophosphate AMP
• GMP guanosine monophosphate
• PA phosphatidic acid
• PE phosphatidylethanolamine
• LPA lysophosphatidic acid
• LPC lysophospholipid
• DHAP dihydroxyacetone phosphate
• GroPIns glycerophosphoinositol
• P-DME phosphodimethylethanolamine
• FIG. 5 Multiplex immunohistochemistry-guided imaging mass spectrometry on islets of Langerhans to automatically distinguish morphologically indistinguishable cell types (A-E).
• Alpha and beta cells were stained with glucagon (red) and insulin (green), respectively.
• the spatial distributions of ADP, cholesterol sulfate and 3-O-sulfogalactosylceramide (sulfatide) are visualized (yellow).
• Pixel-wise intensity distributions are shown for alpha (red) and beta cells (green), respectively. See the methods section for details about the statistical analysis. Scale bar, 150 pm.
• FIG. 6 Image processing workflow to define HER2/neu positive and negative tumor regions. Pan-cytokeratin (green) as an epithelial marker to stain tumor cells. HER2/neu positive cells are shown in red. Both stainings are adjusted to match the lateral imaging mass spectrometry resolution (60 pm) and combined to classify HER2/neu positive and negative tumor cells. Scale bar, 3000 pm.
• FIG. 7 Intratumoral heterogeneity of spatially correlating metabolites in three human gastric cancer tissue sections, visualized via spatial correlation networks (A-C). Left: Close-up of the HER2/neu positive (red) and negative (yellow) tumor regions. Middle: Spatial correlation networks for metabolites. Edges represent positive (blue) and negative (red) spatial correlations between metabolites. Line thickness and transparency correspond to the correlation coefficient. Right: Zoom-in to glucose 6- phosphate.
• glucose 6-phosphate G6P
• Gly3P glycerol 3-phosphate
• R5P ribose 5-phosphate
• SAH S-adenosylhomocysteine
• GroPIns glycerophosphoinositol
• ADP adenosine diphosphate
• GMP guanosine monophosphate
• Ade adenine
• PRA 5-phosphoribosylamine
• FDH flavin adenine dinucleotide
• DGN D-glutamine
• C-M cysteinyl-methionine
• Hey phosphatidic acid
• PA phosphatidylglycerol
• C-M cysteinyl-methionine
• Hey phosphatidic acid
• PPA phosphatidylglycerol
• C-M cysteinyl-methionine
• Hey phosphatidic acid
• PPA phosphatidylglycerol
• FIG. 8 Intertumoral heterogeneity of metabolites in five tissue cores from HER2/neu positive patients with gastric cancer, visualized via spatial correlation networks (A-E). Edges represent positive (blue) and negative (red) spatial correlations between metabolites. Line thickness and transparency correspond to the correlation coefficient. Right: Zoom-in to glucose 6-phosphate.
• G6P glucose 6-phosphate
• Gly3P glycerol 3-phosphate
• PC phospholipid
• PI phosphatidylinositol
• CPA cyclic phosphatidic acid
• LPA lysophosphatidic acid
• LPC lysophospholipid
• P-DME phosphodimethylethanolamine
• SA dihydroxyacetone phosphate
• DHAP dihydroxyacetone phosphate
• A-Q alanylglutamine
• A-Q histidinyl-glycine
• N-HG histidinyl-glycine
• FIG. 9 Immunohistochemistry staining of mouse pancreas: glucagon (red), insulin (green), DAPI (blue). The highlighted regions were analyzed with MALDI imaging prior to immunostaining.
• FIG. 10 Immunohistochemistry staining of mouse pancreas superimposed by molecular distribution of adenosine diphosphate (yellow). Glucagon (red), insulin (green), DAPI (blue). The highlighted regions were analyzed with MALDI imaging prior to immunostaining.
• FIG 11 Immunohistochemistry staining of mouse pancreas superimposed by molecular distribution of cholesterol sulfate (yellow). Glucagon (red), insulin (green), DAPI (blue). The highlighted regions were analyzed with MALDI imaging prior to immunostaining.
• Figure 12 Immunohistochemistry staining of mouse pancreas superimposed by molecular distribution of 3-O-sulfogalactosylceramide (yellow). Glucagon (red), insulin (green), DAPI (blue). The highlighted regions were analyzed with MALDI imaging prior to immunostaining.
• FIG. 13 Intensity distribution per islet of Langerhans and cell type for adenosine diphosphate. The 25 th percentile, median and 75 th percentile are highlighted. SD: standard deviation; width: 75 th percentile - 25 th percentile.
• Figure 14 Intensity distribution per islet of Langerhans and cell type for cholesterol sulfate. The 25 th percentile, median and 75 th percentile are highlighted. SD: standard deviation; width: 75 th percentile - 25 th percentile.
• Figure 15 Intensity distribution per islet of Langerhans and cell type for 3-O- sulfogalactosylceramide. The 25 th percentile, median and 75 th percentile are highlighted. SD: standard deviation; width: 75 th percentile - 25 th percentile. The heights of the bars that exceed the y-axis limit of 20 is written per bar.
• FIG. 16 Immunohistochemistry staining of a human gastric cancer tissue slide: DAPI (blue), pancytokeratin (green), HER2/neu (red). The highlighted regions were analyzed with MALDI imaging prior to immunostaining. Tissue folds were excluded (black).
• Figure 17 HER2/neu positive (red) and negative (yellow) tumor region for the tissue depicted in Figure 16.
• FIG. 18 Immunohistochemistry staining of a human gastric cancer tissue slide after MALDI imaging: DAPI (blue), pancytokeratin (green), HER2/neu (red). Tissue folds and artifacts were excluded (black).
• Figure 19 HER2/neu positive (red) and negative (yellow) tumor region for the tissue depicted in Figure 18.
• FIG. 20 Immunohistochemistry staining of a human gastric cancer tissue slide after MALDI imaging: DAPI (blue), pancytokeratin (green), HER2/neu (red). Tissue folds, artifacts and normal epithel were excluded (black).
• Figure 21 HER2/neu positive (red) and negative (yellow) tumor region for the tissue depicted in Figure 20.
• FIG. 22 Immunohistochemistry staining of human gastric cancer TMA cores after MALDI imaging: DAPI (blue), pancytokeratin (green), HER2/neu (red). Tissue folds and artifacts were excluded (black).
• Figure 23 Spatial correlation networks of metabolites in three human gastric tissue sections with gastroesophageal carcinomas. Order corresponding to images in Figure 14.
• Figure 24 Spatial correlation networks of metabolites in five tissue microarray cores human gastric tissue sections with gastroesophageal carcinomas. Order corresponding to images in Figure 15.
• Figure 25 Identification of metabolites detected on islets of Langerhans on consecutive mouse pancreatic tissue section (see MALDI images of Figures 11 and 12) compared to standard compounds: A) ADP, B) cholesterol sulfate, C) 3-O-sulfogalactosylceramide (d18:1/16:0) and D) glucose-6-phosphate.
• the parent ions were isolated and fragmented by MALDI FTICR on-tissue MS/MS using quadrupole collision-induced dissociation (CID).
• Figure 26 imzML-grid image generated from the imzML file. Each red dot represents one pixel/spectrum
• Figure 27 Tissue overview image (co- registered to Figure 26).
• FIG. 28 Immunohistochemical staining: Pan-Cytokeratin (co- registered).
• FIG. 29 Immunohistochemical staining: Pan-Cytokeratin (co- registered and downscaled).
• Figure 30 Custom-defined masked regions. Regions in black are discarded.
• Figure 32 A Measurement of the region e.g. tissue samples
• B masked image
• C automatically identified regions marked by unique colors e.g. separated measurements for tissues samples
• D manually adjusted regions shown as adjusted colors e.g. tissue samples of separate patients.
• FIG. 33 Uniquely colored individual measurement regions from a tissue microarray (TMA). Automatically generated from the imzML file.
• TMA tissue microarray
• Figure 34 Measurement regions from a tissue microarray re-colorized by patient/group.
• Figure 35 Excerpts of the SPACIAL pipeline Python source code (Python 3.7).
• FIG. 36 Python source code (Python 3.7): The Staining class inherits ROI and comprises additional image data/functions. Stainings defined in the config file are created as shown in figure 36.
• SPACIAL Spatial Correlation Image Analysis
• IHC immunohistochemistry
• Figure 1 SPACIAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution.
• MALDI matrix-assisted laser desorption-ionization
• FTICR imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens ( Figure 2 and 3).
• the SPACIAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features in order to identify metabolic profiles ( Figure 26 to 34). Spatial correlation networks were generated to analyse metabolic heterogeneity associated with cellular features.
• the SPACIAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that would not have been distinguishable via morphology alone ( Figure 5, 9 to 15, 25).
• the semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, the inventors demonstrate intra- and intertumoral metabolic heterogeneity within HER2/neu positive and negative gastric tumor cells ( Figure 6, 7, 8, 16 to 24, 25).
• the inventors developed the SPACIAL workflow to provide immunohistochemistry-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity.
• the multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types.
• the SPACIAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice, that is required for diagnostics, biomarker discovery or therapy response prediction.
• a combined analysis of mass spectrometry and tissue stainings is thereby a valuable diagnostic tool which enables the precise assessment of for instance the medication of cell classes in different tumor layers or the pollution load of different tissue cells.
• the images of the tissue section may be selected from images obtained by multiplex immunohistochemical (IHC) staining, histochemical staining, endogeneous or exogeneous fluorescence staining, respectively, fluorescence in situ hybridization (FISH), or autofluorescence.
• IHC immunohistochemical
• FISH fluorescence in situ hybridization
• Histological tissue stain increases the contrast in the optical image and reveals different cells and tissue structures.
• Histological stains are available which differ in their affinity to certain tissue and cell structures and selectively visualize these structures in the optical image.
• Hematoxylin and eosin (H&E) staining is most commonly used in routine and general investigations. Histology is usually a morphologic diagnostic method because the histologic classification is done according to the appearance and staining properties of the tissue and cell structures.
• Another common staining is DAPI, which intercalates into DNA and therefore marks nuclei.
• a classification of histological stainings can be limited to one or more subareas of a tissue section, or can even apply to only one or more individual cells. Histologic classification is generally carried out on a stained tissue section of a few micrometres thickness and concerns the tissue type, differentiation, presence of bacterial or parasitic pathogens, disease statuses, and content of foreign matter like pesticides or drugs and their metabolites.
• the disease progression of human tissue concerns inflammatory disorders, metabolic diseases and the detection of tumours, especially differentiation between benign and malignant forms of tumours.
• a different type of staining is an immunostaining or immunofluorescent staining, where for instance the distribution of proteins in tissues and cells are visualized by the specific binding of antibodies to certain proteins.
• antibody is used herein as a protein comprising one or more polypeptides substantially or partially encoded by immunoglobulin genes or fragments of immunoglobulin genes.
• a classical immunofluorescence staining is further based on the functional affinity of a second antibody to bind to a first antibody. Said second antibody usually carries a (fluorescent) label to mark the structures recognized by the first antibody.
• immunostainings represent a method to precisely label specific cell types. Further stainings may be include one or more dye(s) which is/ are excitable through UV or visible light and known in the art.
• An endogenous or exogeneous staining as used in the present invention refers to genetic reporter systems expressing a reporter molecule.
• reporter molecules include genes that induce visually identifiable characteristics including fluorescent and luminescent proteins. Examples include the gene that encodes jellyfish green fluorescent protein (GFP), which causes cells that express it to emitt green light under blue/ UV light, luciferase, which catalyses a reaction with luciferin to produce light, and the red fluorescent protein from the gene dsRed.
• GFP jellyfish green fluorescent protein
• luciferase which catalyses a reaction with luciferin to produce light
• red fluorescent protein from the gene dsRed any kind of light signal of a tissue sample may be applicable to the method of the present invention.
• endogenous reporter molecules are a fluorescent protein; a bioluminescence-generating enzyme, preferably NanoLuc, NanoKAZ, TurboLuc, Cypridina, Firefly, Renilla luciferase, split luciferase, split APEX2 or mutant derivatives thereof; an enzyme, which is capable of generating a coloured pigment, preferably tyrosinase or an enzyme of a multi-enzymatic process, more preferably the violacein or betanidin synthesis process, a genetically encoded receptor for multimodal contrast agents, preferably Avidin, Streptavidin or HaloTag.
• a bioluminescence-generating enzyme preferably NanoLuc, NanoKAZ, TurboLuc, Cypridina, Firefly, Renilla luciferase, split luciferase, split APEX2 or mutant derivatives thereof
• an enzyme which is capable of generating a coloured pigment, preferably tyrosinase or an enzyme of a multi-enzymatic process, more
• multiplex immunohistochemistry referes to the use of multiple markers.
• Multiplex immunohistochemistry also called multiple immunolabeling, or multiplex immunostaining, can maximize the amount of data acquired from an individual sample. This is critical in instances where sample is limited, such as a tumor biopsy or other clinical specimen. Multiplex immunohistochemistry also allows for examination of spatial arrangement of proteins of interest as well as protein interaction/co-localization.
• Immunostaining and in situ hybridization are usually highly specific, so not only morphologic information but also molecular information can be derived.
• nucleotide sequences fluoresce when hybridized to a nucleic acid containing a target or complementary sequence, but are otherwise non-fluorescent when in a non- hybridized state.
• Such oligonucleotides are disclosed, for example, in U.S. patent application Publication No. 2003/0113765.
• Such staining or in-situ hybridization methods enable the analysis of tissues regarding diseases, differentiation, infection with pathogens, and distribution of foreign matter as compared to a control of normally differentiated or healthy tissue.
• the staining mixture may be formed using fluorescent polyamides, and more specifically polyamides with a fluorescent label or reporter conjugated thereto.
• fluorescent polyamides and more specifically polyamides with a fluorescent label or reporter conjugated thereto.
• Such labels will fluoresce when bound to nucleic acids.
• First optical active monomers were based on optical active amino acid cores, such as L-leucine.
• polyamides with a fluorescent label or reporter attached thereto include, for example, those disclosed in Best et al., Proc. Natl. Acad. Sci. USA, 100(21): 12063-12068 (2003); Gygi, et al., Nucleic Acids Res., 30(13): 2790-2799 (2002); U.S. Pat. No. 5,998,140; U.S. Pat. No. 6,143,901 ; and U.S. Pat. No. 6,090,947.
• Luminescent, color-selective nanocrystals may also be used to label cells in a staining mixture for the method of the present disclosure.
• quantum dots these particles are well known in the art, as demonstrated by U.S. Pat. No. 6,322,901 and U.S. Pat. No. 6,576,29.
• These nanocrystals have been conjugated to a number of biological materials, including for example, peptides, antibodies, nucleic acids, streptavidin, and polysaccharides, (see, for example, U.S. Pat. Nos.
• Tissue stabilization means that the tissue structures, the cells of the tissue itself and even intracellular structures (e.g. cell nuclei, endoplasmic reticulum, mitochondria) remain preserved in the tissue section.
• Common chemicals for tissue stabilization are e.g. paraformaldehyde or ethanol.
• a further mechanical stabilization of the tissue can be achieved by embedding the tissue into paraffin or gelatine.
• the tissue section may comprise one or more tissue samples obtained by formalin-fixed paraffin-embedding (FFPE) or obtained by any other chemical or physical fixation technique, comprising ethanol fixation, cryofixation and heat fixation, respectively.
• FFPE formalin-fixed paraffin-embedding
• the structures of the stained tissue sections are imaged or scanned by routine techniques with the aid of light-optical microscopes and scanners. Thus, by using these routine methods an image is obtained which means in the context of microscopic images a picture is taken with a microscope.
• a light-optical image of the tissue section can have a spatial resolution of about 250 nanometres, which means that structures of the corresponding size are spatially resolved.
• spatial resolution refers to the number of pixels utilized in construction of the image. Images having a higher spatial resolution are composed with a greater number of pixels than those of lower spatial resolution.
• STED Stimulated Emission Depletion
• the present disclosure comprises the use of histology and/or immunostainings as well as in situ hybridization in combination with mass spectrometry data as described elsewhere herein.
• the resulting image information of said stainings and mass spectrometry data may be analysed in combination.
• the present disclosure comprises the use of histology and/or immunostainings, in situ hybridization, fluorescent polyamides, and color-selective nanocrystals in combination with mass spectrometry data as described elsewhere herein.
• the resulting image information of said stainings and/or in situ hybridization, fluorescent polyamides, and color-selective nanocrystals and mass spectrometry data may be analysed in combination.
• 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, which allows visualizing the distribution of molecules of interest directly in a sample.
• Mass spectrometry (ISM or IMS) imaging brings together all imaging technologies that use an ionization source to locate molecular ions from a sample.
• ionization such as laser, ions, gas, liquid, solvent, plasma (single or combined sources), microwaves, electrons, which can be used in imaging mode
• DESI Desorption Electrospray Ionization
• LAESI Laser Ablation Electrospray Ionization
• MALDI Microx Assisted Laser Desorption Ionization
• SIMS Secondary ion Mass Spectrometry
• MALDESI Matrix-assisted Laser Desorption Electrospray Ionization
• LEA Liquid Extraction Surface Analysis
• ICP-MSI Inductively Coupled Plasma Mass Spectrometry Imaging
• the mass spectrometry data is obtained by any one of MALDI, DESI, LAESI, SIMS, MALDESI, LESA, MSI and ICP-MSI, respectively.
• the mass spectrometry data may be obtained by Matrix-assisted Laser Desorption (MALDI), Desorption Electrospray Ionization (DESI), Laser Ablation Electrospray Ionization (LAESI), Secondary ion Mass Spectrometry (SIMS), Matrix-assisted Laser Desorption Electrospray Ionization (MALDESI), Liquid Extraction Surface Analysis (LESA), Mass spectrometra imaging (MSI) or Inductively Coupled Plasma Mass Spectrometry Imaging (ICP-MSI), respectively.
• MALDI Matrix-assisted Laser Desorption
• DESI Desorption Electrospray Ionization
• LAESI Laser Ablation Electrospray Ionization
• SIMS Secondary ion Mass Spectrometry
• SIMS Secondary ion Mass
• MALDI-TOF matrix-assisted laser desorption/ionization time-of-flight
• the m/z value i.e. the mass-to-charge ratio of the ionized molecule
• the relative number of recorded ions (spectral intensity), as a function of the m/z value, represents a mass spectrum. Assuming a single positive ionization of the molecules, the m/z value is identical to the mass of the ionized molecule.
• Matrix-assisted laser desorption and ionization has been used successfully for the determination of molecular masses, and structural characterization of biological substances, e.g. proteins, nucleic acids, lipids, sugars or drugs. If the concentrations of the substances are sufficiently high, the concentration patterns can be detected by a mass spectrometric analysis. Thus, a cell can be characterized by concentration patterns of substances e.g. molecular information. An unusual pattern can result when certain biological substances are under expressed or overexpressed.
• Characteristic concentration patterns can be determined by homogenizing a tissue samples by methods known in the art.
• the substances contained therein are prepared and applied to a sample support together with a solution of a matrix substance.
• the solvent is evaporated and the matrix substance crystallizes; the biological substances in the matrix crystals crystallize at the same time in the form of widely spaced individual molecules. Bombarding a homogenized sample thus prepared with short laser pulses of sufficient energy causes the matrix substance to explosively vaporize and the biological substances to be ionized.
• Imaging mass spectrometric (IMS) analysis i.e, acquiring a mass spectrometric image, involves investigating tissue sections instead of homogenized tissue samples.
• Matrix-assisted laser desorption- ionization (MALDI) imaging mass spectrometry (IMS) can be used for in situ imaging of metabolites from frozen or formalin-fixed, paraffin-embedded (FFPE) tissue samples.
• 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. In addition, because of its specificity, ISM makes it possible to discriminate and identify ions detected directly on the sample. Thus, it is now common to use ISM for the study or search for endogenous molecular markers in biological samples of interest. More precisely, it is possible to directly analyze the distribution of a known molecule by targeting an ion or its mass-to-charge ratio (m / z).
• a tissue section is placed on an electrically conductive sample support.
• a suitable method is then employed to apply a matrix solution onto the tissue section.
• Patent specification DE 10 2006 019 530 B4 elucidates different methods of preparing tissue sections for imaging mass spectrometric analysis.
• the matrix solution or a recrystallization solution can be applied to the tissue section by pneumatic spraying, nebulizing by vibration or by the nanospotting of droplets, for example.
• the sample support is introduced into a mass spectrometer.
• the Caprioli raster scan method (US 5,808,300 A) or stigmatic imaging of a small region of the tissue (Luxemteil et al., Analytical Chemistry, 76(18), 2004, 5339-5344: "High-Spatial Resolution Mass Spectrometric Imaging of Peptide and Protein Distributions on a Surface") can be used for the subsequent imaging mass spectrometric analysis. Both techniques produce a mass spectrometric image of the tissue section, i.e, the molecular information in the mass spectra is spatially resolved.
• a mass spectrometry image is obtained when a sample is prepared as mentioned above, a mass spectrometer has scanned the sample and created tissue section image.
• the types of tissue section images obtained are usually superimposed in a graphical representation, in which the spatially resolved mass spectra are often reduced to individual selected masses or to an assignment of certain classes, based on statistical analysis.
• a mass spectrometric image can be acquired first and an optical image later on.
• the matrix layer applied to the tissue section is removed again after the mass spectrometric image has been acquired. Then the tissue section is subjected to routine histologic staining, and an optical image is taken.
• IHC stainings are commonly used for cell type labelling, but their potential for automated, semantic-based, functional tissue annotation and spatially resolved molecular analyses of heterogeneity is not fully utilized.
• imaging mass spectrometry data and immunohistochemical stainings were successfully combined to increase the resolution of MALDI images, or to characterize individual dissociated cells, but no in situ tissue analysis with automatic identification of regions of interest and data integration has been presented.
• tissue image analysis While there is some software available for tissue image analysis, there currently is no method that integrates and analyses the comprehensive molecular data from imaging mass spectrometry in combination with morphological, proteomic, and genetic information from other omics fields.
• the translation of imaging mass spectrometry into experimental clinical applications requires time-efficient data post-processing and comprehensive analyses of spatially resolved molecular information by avoiding expensive manual annotations or loss of resolution due to the generation of mean or representative spectra.
• mass spectrometry data and image data may be compared, respectively, extracted from the final ROIs comprises assessing of molecular composition and heterogeneity expressed between data extracted from final ROIs, wherein molecular composition comprises metabolites, proteins, drugs, toxic agents, carcinogens, glycanes, and lipids, respectively.
• Preferred examples of known anti-cancer drugs are cis-platin, maytansine derivatives, rachelmycin, calicheamicin, docetaxel, etoposide, gemcitabine, ifosfamide, irinotecan, melphalan, mitoxantrone, sorfimer sodiumphotofrin II, temozolmide, topotecan, trimetreate glucuronate, auristatin E vincristine and doxorubicin; and peptide cytotoxins such as ricin, diphtheria toxin, pseudomonas bacterial exotoxin A, DNAase and RNAase; radio-nuclides such as iodine 131 , rhenium 186, indium 111 , yttrium 90, bismuth 210 and 213, actinium 225 and astatine 213; prodrugs, such as antibody directed enzyme pro-drugs; immuno-stimulants, such as
• genes encoding toxic peptides i.e., chemotherapeutic agents such as ricin, diphtheria toxin and cobra venom factor
• tumour suppressor genes such as p53
• genes coding for mRNA sequences, which are antisense to transforming oncogenes, antineoplastic peptides, such as tumour necrosis factor (TNF) and other cytokines, or transdominant negative mutants of transforming oncogenes may be non limiting examples.
• a Spatial Correlation Image Analysis (SPACIAL) pipeline is presented, a computational multimodal workflow to integrate molecular imaging mass spectrometry data with multiplex IHC stainings from the very same tissue section to provide automated and reliable annotations and allow comprehensive and pixel-accurate correlation analyses of heterogeneity to combine data from multi- omics studies.
• the pipeline represents a starting point for the objective analysis of high-throughput data from clinical research and practice, which is required for tissue-based diagnostics and research.
• HER2/neu human epidermal growth factor receptor 2
• SPACIAL pipeline was applied on tissue resection specimens and on a tissue microarray to distinguish HER2/neu positive and negative tumor cells and to investigate the molecular intra- and intertumoral heterogeneity (Example 3 and 4).
• the multimodal approach utilizes pixel-wise molecular information to investigate metabolic heterogeneity via spatial correlation networks from cell populations automatically identified by multiplex immunohistochemical analysis.
• the term high-content analysis is frequently used to describe the combining of approaches from image processing, computer vision, and machine learning to provide fast and objective methods for analyzing large amounts of bioimage data.
• Spatiotemporal events within a cell can be captured by microscopy and quantified through image processing and machine learning methods to produce meaningful conclusions about the data within the experimental context.
• the bioimage data for high- content analysis are typically collected using fluorescent tags or stains to identify points of interest within the cells being imaged.
• Image data processing as part of the SPACIAL pipeline comprises in brief defining local signal maxima, correcting against noise level, selecting maxima, matching the selected maxima with the tissue stainings, combining the data and converting it into a numerical matrix, and optionally calculating a correlation network.
• the mass spectrometry data may be represented in imzML format, mis format of fleximaging, slx/sbd format of SCiLS Lab, or a tabular format, respectively.
• a Bruker fleximaging comprises a software tool for two-dimensional peptide and protein imaging of cells or tissue extracts (Bruker fleximaging (v. 5.0) Software manual) was used to export the mass spectra images raw data as a standardized data format called imzML.
• Pre-Mass Spectrometry formats There are relatively few common Pre-Mass Spectrometry formats (i.e. those not specific to one vendor) for information specifically focused on information prior to MS. There are broadly two categories of such formats: one that describes sample handling, and one that contains formats for mass spectrometer target input.
• Converters may be Thermo: converter for LTQ- and Orbitrap-based instruments is available for download here and an imzML export function is implemented in the new version of ImageQuest by Thermo Fisher Scientific, Waters: imzML files can be generated using an export filter in Waters’ High Definition Imaging (HDI) software, ABI SCIEX: ‘Analyze’ format can be converted by ‘toimzml’ module.
• HDI High Definition Imaging
• ABI SCIEX ‘Analyze’ format can be converted by ‘toimzml’ module.
• the imzML format consists of two separate files: one for the metadata and one for the MS data.
• the metadata is saved in an XML file (TimzML).
• the mass spectral data is saved in a binary file (*.ibd).
• a “slx/s bd format” (SCiLS Lab) is a compressed file that contains model information in XML format. If you want to dig into it, you can open the file in say winzip and extract it. I just renamed the file to filename.zip and extracted it and you can see the model in XML format.
• Preliminary peaks within each spectrum were merged.
• Picked peaks were saved as an imzML file.
• the coordinates from the imzML file were used to generate an image of the measurement region (imzML-grid) ( Figure. 26). The resolution of this image is a multiple of the imzML resolution.
• next step pictures were matched together or co-registered.
• the imzML file of picked peaks was used to create a master image of the MALDI measurement region (imzML-grid).
• a two-step image co-registration can be achieved by first registering the tissue overview image with visible tissue or matrix-specific markers onto the grid-image and then registering all stainings simultaneously onto the tissue overview image. All additional images were precisely co-registered onto this image, allowing an exact integration and correlation of molecular MALDI data with immunostainings.
• the co-registration was done with the Landmark Correspondences plugin of FIJI ImageJ (v. 1 .52p).
• co-registration is also feasible with Adobe Photoshop CC 2019 or the GNU Image Manipulation Program (GIMP, v.2.10.8).
• GIMP GNU Image Manipulation Program
• a grey-scale tissue overview image and measurement points were exported with fleximaging (v. 5.0) and then fitted onto the master image.
• the integration of mass spectra and image data is done by coregistering the tissue scanned subsequent to MALDI imaging mass spectrometry or mapping the matrix ablation marks to the imzML-grid.
• the DAPI staining and all other stainings were finally fitted onto the precisely co-registered tissue image. Once all images are co-registered, they have the same resolution ( Figure. 27 and Figure. 28) as the x,y-grid from the imzML file ( Figure. 29).
• the present invention relates to a method of selecting regions of interest, ROIs, for analyzing a complete tissue section, the method comprises obtaining a first image comprising mass spectrometry data of the tissue section spatially resolved by a region of the first image represented as a first mask, and further obtaining one or more second images comprising any type of images of the tissue section, the one or more second images are in alignment with and scaled to the resolution of the first image, the method also comprises determining one or more second masks associated with respective one or more second images based on predetermined criteria, as well as combining masks selected from the first mask and the one or more second masks, respectively, based on predetermined set expressions, to obtain final regions of interest, ROIs and extracting mass spectrometry data from said first image and image data from said one or more second images, respectively, based on the final ROIs, for analyzing the tissue section.
• a mask is a special value that acts as a data filter. It reveals some parts of the digital information, and conceals or alters others.
• An “expression” refers to the terminology used within and run by a program.
• a regular expression is a pattern that describes a set of strings. Regular expressions are constructed by using various operators to combine smaller expressions.
• determining one or more second masks associated with respective one or more second images comprises determining multiple masks for the same image, said masks pertaining to multiple morphometric data, or data from multiple color components, such as Hematoxylin and eosin (H&E) and other histological stainings.
• the method further comprises obtaining one or more custom masks of the one or more second images, and wherein combining masks comprises combining masks selected from the first mask, the one or more second masks, and the one or more custom masks, respectively, based on predetermined set expressions, to obtain final regions of interest, ROIs.
• the method referred to herein may comprise determining one or more second masks associated with respective one or more second images based on predetermined criteria comprises using predetermined thresholds of color values or ranges of color features of pixels in an image of the one or more second images to determine regions containing pixels of similar color features in said image.
• SPACIAL offers the option to create a blacklist image, where the user can manually label regions that should be excluded from subsequent analyses.
• a black and white image can now be created to mask artefacts (e.g. tissue folds) or split measurement regions. Masked regions were excluded from further analyses ( Figure. 30). Such regions may comprise tissue folds, swept away tissue, artefacts, or regions that are generally of no interest.
• the user can now define a threshold to decide at what colour value a pixel is classified as a positive signal ( Figure. 31). First, preliminary regions of interest are defined based on stainings, imaging mass spectrometry measurement regions and custom-generated images. Subsequently, those regions can be combined to define the final regions of interest.
• Individual imaging mass spectrometry measurement regions may also be considered as preliminary regions. This is important for the analysis of tissue microarrays, where tissue cores of several of patients are measured on one glass slide. Usually each patient sample is measured in a separate measurement region. Nevertheless, if two patient cores are part of the same measurement region, the measurement region can be split by separating them with a black line in the masking image (see paragraph ‘Image masking’) ( Figure. 32).
• the user can define custom regions, either by creating one black and white image per region, similar to the masking image, or by creating a multicolour image, where each colour except black is considered as one region. This is used for the analysis of tissue microarrays, where multiple cores may belong to the same patient. First, an image is generated, where each measurement region is coloured uniquely ( Figure. 33). The user can then manually colorize regions that belong to the same patient/group ( Figure. 34).
• custom mask refers to masks as defined somewhere else herein which are not connected to picture data.
• the predetermined set expressions may represent semantics of tissue annotation to obtain the final ROIs, based on the first image, the one or more second images and the associated masks of the tissue section.
• the term “annotation” relates to metadata linked to e.g. imaging data.
• Function annotations of e.g. Python 3.0 allow adding arbitrary metadata to function parameters and return value. The purpose is to have a standard way to link metadata to function parameters and return value.
• a “tissue annotation” refers to the (meta)data collected by the analysis of a tissue sample by using the method of the present invention.
• the term “semantic” is to be understood in the sense of computer science as language or set of accepted words.
• the mass spectra of each region of interest can now be extracted from the imzML file and used for subsequent calculations and analyses.
• the IHC images are then converted into numerical matrices comprised of values corresponding to the lightness values for each pixel.
• SPACIAL can create images to allow validation of semi-automatically defined ROIs (e.g., HER2/neu positive tumor regions). The user does not have to manually select/circle all the tissue regions belonging to a specific cell type. However, the user still has to select a color threshold to distinguish positive and negative cells.
• threshold refers to segmenting an image. From a grayscale image, thresholding can be used to create binary images. The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity I jj is less than some fixed constant T (that is, I jj ⁇ T), or a white pixel if the image intensity is greater than that constant.
• Resulting from the numerical matrices networks can be presented as graphs, that is, a set of vertices (V) connected by edges (E), and consequently can be analyzed using graph theory.
• graph theory consists of many tens of basic definitions and properties.
• the understanding of the biological networks lies in the nature of the vertices and edges between them; that is, the vertices may represent one of the components of the three major molecular levels: genes, proteins, or metabolites, while the edges between them represent gene coexpression, protein-protein interactions, or biochemical conversions of metabolites, respectively.
• molecular networks are not limited to illustrate single- level component interactions. They can also show cross-level interactions.
• a network may incorporate vertices representing a set of metabolic reactions, where the connection between a pair of vertices is established if the reactions share one or multiple metabolites used or produced by these reactions.
• Correlation networks were created with Cytoscape (v. 3.7.1). In all networks, nodes represent metabolites with node sizes corresponding to the mean intensity. Edges represent spatial correlations with line thickness and opacity increasing with the correlation coefficient. Nodes were coloured red, if their metabolites take part in glycolysis or they were coloured depending on the molecule super class defined in HMDB [lipids and lipid-like molecules (yellow); nucleosides, nucleotides, and analogues (light red); organic acids and derivatives (green); organoheterocyclic compounds (lime green); alkaloids and derivatives (pink); organic oxygen compounds (blue); benzenoids (violet); phenylpropanoids and polyketides (orange); others (grey)].
• HMDB lipids and lipid-like molecules (yellow); nucleosides, nucleotides, and analogues (light red); organic acids and derivatives (green); organoheterocyclic compounds (lime
• the multiplex staining of the complete tissue is shown in Figure 9. All remaining networks were generated by showing metabolites with at least one correlation coefficient larger than 0.5, but without filtering edges. The complete networks are shown in Figures 23 and 24. The multiplex staining of the complete tissues is shown in Figures 16, 17, 20, and 22.
• analyzing the tissue section may comprise comparing mass spectrometry data and image data, respectively, derived from the final ROIs of the first image and the one or more second images of the tissue section, by statistical analysis.
• a statistical analysis may be applicable for any value correction. Examples may be the correlation networks, which may be analysed by using pairwise Spearman rank-order correlations (Python 3.7, SciPy 1.2.0) were calculated between annotated metabolites using their intensities, and the resulting p-values may be adjusted with Benjamini/Hochberg correction (Python 3.7, StatsModels 0.9.0).
• the inventor’s SPACIAL workflow integrates molecular MALDI imaging mass spectrometry data with IHC stainings to facilitate automatic, reliable, and pixel-accurate annotation of specific cell types.
• the phenotypical information provided by immunohistochemistry complements in situ molecular information for cell type specific evaluation.
• the pipeline was demonstrated for both physiological and pathophysiological applications to investigate metabolic heterogeneity in alpha and beta cells from islets of Langerhans of a mouse model and in HER2/neu positive tumor cells from patients with gastric cancer.
• Glucagon releasing alpha and insulin releasing beta cells of different pancreatic islets within one animal were automatically annotated, demonstrating the basic functionality of the SPACIAL pipeline as a tool for objective immunohistochemistry-guided annotation of otherwise histologically indistinguishable cell types.
• the pixel-accurate annotation and analysis of metabolites allows previously infeasible assessments of metabolomics heterogeneity between islets of Langerhans.
• tissue samples from patients with gastric cancer were chosen to demonstrate the methodological advantages of SPACIAL for the analysis of intra- and intertumoral heterogeneity.
• the SPACIAL strategy can be extended by integrating other in situ datasets from tissue analytic platforms, since all spatially resolved information of a tissue section can be integrated in this pipeline (e.g., morphometries, fluorescence in situ hybridization, and imaging mass cytometry).
• the application can also be useful for the automatic readout of regions of interest for metabolite quantification on an absolute, rather than on a relative scale. Quantification is a major topic of investigation in the targeted MALDI IMS field concentrating on the analysis of a subset of metabolites.
• the workflow was demonstrated to be compatible with both frozen and FFPE tissue samples.
• SPACIAL hundreds of distinct samples within tissue microarrays can be analysed simultaneously. In contrast to the traditional analysis of mean spectra per ROI, SPACIAL allows in-depth and full use of available data without loss of resolution.
• the inventors demonstrate one of the many possibilities to utilize MALDI data. Combining the data from multi-omics studies, the pipeline represents an important starting point for the objective analysis of high-throughput data from large-scale clinical cohort studies, which are required for artificial intelligence guided diagnostics, biomarker discovery, or therapy prediction.
• the tissue section comprises multiple tissue samples individualized by one or more custom masks, wherein each custom mask is associated with an individual tissue sample.
• the method of the present invention may be applied to more than one tissue sample of an individual (e.g. tissue microarray) and tissue regions are managed by automatically generating and subsequently customizing masks. Individual tissue samples are automatically represented in the microarray of figure 32 in color code (C) and the correction thereof is done manually (D).
• tissue samples are automatically represented in the microarray of figure 32 in color code (C) and the correction thereof is done manually (D).
• the present invention relates to the use of the method for diagnosis or stratification based on a human, animal or plant tissue samples.
• ranges of color values of pixels are based on a magnitude of expression of a diagnostic feature, such as one or more biomarkers.
• the biomarker is HER2/neu and/or pan- cytokeratin (PC); Insulin and/or glucagon; PD1 and/or PD-L1 ; or Vimentin and/or pan-cytokeratin.
• the use of the method may comprise that the samples are human tissue samples used in the diagnosis or stratification of cancer or diabetes.
• the present invention relates to the use of the method for therapy response prediction and prediction of organ rejection based on human or animal tissue.
• One example of stratification using the method of the invention is the application for prognostic risk stratification of neoadjuvant treated cancer patients, such as esophageal adenocarcinoma patients.
• the methods of the invention can be used to address the question of whether metabolic tumor profiling of neoadjuvantly treated esophageal adenocarcinoma (EAC) can contribute to patient stratification into different prognostic risk groups. It is known that response to neoadjuvant therapy can vary widely between individual patients.
• Tumor regression grading (TRG) and nodal status are standard clinical-pathological prognostic factors used to predict the survival of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery.
• Neoadjuvant chemoradiotherapy or chemotherapy is associated with a significant survival benefit for patients compared to surgery alone and has become the standard of care for most patients with resectable esophageal and gastroesophageal-junction adenocarcinoma.
• Preoperative treatment has the effect of tumor and nodal downstaging, which can increase the prospect of complete resection.
• EAC esophageal adenocarcinoma
• TRG histological tumor regression grading
• the method of the present invention can be employed for detection, diagnosis, prognosis, prevention and/or as control device in the treatment of diseases or disorders.
• treatment in all its grammatical forms includes therapeutic or prophylactic treatment of a subject in need thereof.
• a “therapeutic or prophylactic treatment” comprises prophylactic treatments aimed at the complete prevention of clinical and/or pathological manifestations or therapeutic treatment aimed at amelioration or remission of clinical and/or pathological manifestations.
• treatment thus also includes the amelioration or prevention of diseases.
• stratification refers to the method whereby the sample is subdivided based on the presence or absence of a specific characteristic (unless context dictates otherwise).
• a “stratified” sample is a sample obtained by subjecting a source sample to a selection for specific characteristics. A “stratified” sample is therefore in a certain aspect enriched relative to the source sample, to obtain a sample subdivision having these specific characteristic.
• the tissue sample analysed with the method of the present invention may originate from a human, an animal, especially birds and fishes and mammals, or a plant.
• bird or aves is a group of endothermic vertebrates, characterised by feathers, toothless beaked jaws, the laying of hard-shelled eggs, a high metabolic rate, a four-chambered heart, and a strong yet lightweight skeleton and are to be understood as such in the context of the present invention.
• fish refers to an animal group of gill-bearing aquatic craniate vertebrates that lack limbs with digits. They form a sister group to the tunicates, together forming the olfactores. Included in this definition are the living hagfish, lampreys, and cartilaginous and bony fish as well as various extinct related groups.
• mammals are a preferred part of the animal kingdom donating tissue for use in the method of the present invention.
• mamammal includes for instance cats, dogs, horses, pigs, cows, goats, sheep, rodents (e.g. mice or rats, rabbits), primates (e.g. chimpanzees, monkeys, gorillas, and humans) and endangered mammals (such as non-human primates, elephants, rhinos, bears).
• Table 1 Network metrics for the glucose 6-phosphate node.
• Sample identifiers (A, B and C) correspond to the samples in Figure 7.
• the degree of a node represents the number of neighbors in the network.
• the average shortest path length is the average, minimum number of edges between glucose 6- phosphate node and any other node.
• the clustering coefficient is a measure for the connections between neighboring nodes.
• the clustering coefficient (0-1) describes whether nodes in a network tend to form clusters.
• the centralization (0-1) of a network describes whether the network has a centre (star shaped).
• the network’s characteristic path length is the average of the shortest path length between any pair of nodes.
• the density (0-1) describes how densely the network is populated with edges.
• the heterogeneity (0-1) of a network reflects, whether a network tends to contain hub nodes (i.e. well connected nodes).
• the term “at least” preceding a series of elements is to be understood to refer to every element in the series.
• the term “at least one” refers, if not particularly defined differently, to one or more such as two, three, four, five, six, seven, eight, nine, ten or more.
• the term “about” means plus or minus 10%, preferably plus or minus 5%, more preferably plus or minus 2%, most preferably plus or minus 1%.
• Method of selecting regions of interest, ROIs, for analyzing a complete tissue section comprising: obtaining a first image comprising mass spectrometry data of the tissue section spatially resolved by a region of the first image represented as a first mask, and further obtaining one or more second images comprising any type of images of the tissue section, wherein the one or more second images are in alignment with and scaled to the resolution of the first image; determining one or more second masks associated with respective one or more second images based on predetermined criteria; combining masks selected from the first mask and the one or more second masks, respectively, based on predetermined set expressions, to obtain final regions of interest, ROIs; and extracting mass spectrometry data from said first image and image data from said one or more second images, respectively, based on the final ROIs, for analyzing the tissue section.
• mass spectrometry data are obtained by Matrix-assisted Laser Desorption (MALDI), Desorption Electrospray Ionization (DESI), Laser Ablation Electrospray Ionization (LAESI), Secondary ion Mass Spectrometry (SIMS), Matrix-assisted Laser Desorption Electrospray Ionization (MALDESI), Liquid Extraction Surface Analysis (LESA), Mass spectrometra imaging (MSI), or Inductively Coupled Plasma Mass Spectrometry Imaging (ICP-MSI), respectively.
• MALDI Matrix-assisted Laser Desorption
• DESI Desorption Electrospray Ionization
• LAESI Laser Ablation Electrospray Ionization
• SIMS Secondary ion Mass Spectrometry
• MALDESI Matrix-assisted Laser Desorption Electrospray Ionization
• LESA Liquid Extraction Surface Analysis
• MSI Mass spectrometra imaging
• the light-optical images of the tissue section are selected from images obtained by multiplex immunohistochemical (IHC) staining, histochemical staining, endogeneous or exogeneous fluorescence staining, respectively, fluorescence in situ hybridization (FISH), or autofluorescence.
• IHC immunohistochemical
• FISH fluorescence in situ hybridization
• tissue section comprises one or more tissue samples obtained by formalin-fixed paraffin-embedding (FFPE) or obtained by any other fixation technique, comprising ethanol fixation and cryofixation, respectively.
• FFPE formalin-fixed paraffin-embedding
• determining one or more second masks associated with respective one or more second images comprises determining multiple masks for the same image, said masks pertaining to multiple morphometric data, or data from multiple color components, such as Hematoxylin and eosin (H&E) and other histological stainings.
• determining one or more second masks associated with respective one or more second images based on predetermined criteria comprises using predetermined thresholds of color values or ranges of color values of pixels in an image of the one or more second images to determine regions containing pixels of similar color values in said image.
• ranges of color values of pixels are based on a magnitude of expression of a diagnostic feature, such as one or more biomarkers.
• the biomarker is a) HER2/neu and/or pan-cytokeratin (PC); b) Insulin and/or glucagon; c) PD1 and/or PD-L1 ; or d) Vimentin and/or pan-cytokeratin.
• PC pan-cytokeratin
• the predetermined set expressions represent semantics of tissue annotation to obtain the final ROIs, based on the first image, the one or more second images and the associated masks of the tissue section.
• analyzing the tissue section comprises comparing mass spectrometry data and image data, respectively, derived from the final ROIs of the first image and the one or more second images of the tissue section, by statistical analysis.
• comparing mass spectrometry data and image data, respectively, extracted from the final ROIs comprises assessing of molecular composition and heterogeneity expressed between data extracted from final ROIs, wherein molecular composition comprises metabolites, proteins, drugs, toxic agents, carcinogens, glycanes, and lipids, respectively.
• the tissue section comprises multiple tissue samples individualized by one or more custom masks, wherein each custom mask is associated with an individual tissue sample.
• Tissue preparation steps for MALDI imaging analysis was performed as previously described.
• frozen (12 pm, Leica Microsystems, CM1950, Germany) and FFPE sections (4 pm, Microm, HM340E, Thermo Fisher Scientific, USA) were mounted onto indium-tin-oxide (ITO)-coated glass slides (Bruker Daltonik, Bremen, Germany) pretreated with 1 :1 poly-L-lysine (Sigma Aldrich, Kunststoff, Germany) and 0.1% Nonidet P-40 (Sigma).
• the air-dried tissue sections were spray-coated with 10 mg/ml 9- aminoacridine hydrochloride monohydrate matrix (Sigma-Aldrich, Kunststoff, Germany) in 70% methanol using the SunCollectTM sprayer (Sunchrom, Friedrichsdorf, Germany).
• Prior matrix application FFPE tissue sections were incubated additionally for 1 h at 70°C and deparaffinised in xylene (2x8 min).
• Spraycoating of the matrix was conducted in eight passes (ascending flow rates 10 pl/min, 20 pl/min, 30 pl/min for layers 1-3, and layers 4-8 with 40 pl/min), utilizing 2 mm line distance, and a spray velocity of 900 mm/min.
• Metabolites were detected in negative-ion mode on a 7T Solarix XR Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer (Bruker Daltonik) equipped with a dual ESI-MALDI source and a smartbeam-ll Nd:YAG (355 nm) laser. Data acquisition parameters were specified in ftmsControl software 2.2 and fleximaging (v. 5.0) (Bruker Daltonik). Mass spectra were acquired in negative-ion mode covering m / z 75-1100, with a 1M transient (0.367 sec duration), and an estimated resolving power of 49,000 at m/z 200,000.
• FTICR Fourier-transform ion cyclotron resonance
• pancreatic islets were analyzed by double staining for insulin [Insulin-monoclonal rabbit anti-insulin (1 :800), catalogue no. 3014, Cell Signaling Technology, Germany; AF750-goat anti-rabbit (1 :100), catalog no. A21039, Thermo Fisher Scientific, US] and glucagon [polyclonal guinea pig anti-glucagon (1 :3000), catalogue no. M182, Takara, USA; biotinylated goat anti-guinea pig IgG (1 :100), catalogue no. BA-7000, Vector Laboratories, US; streptavidin-Cy3, catalogue no. SA1010, Thermo Fisher Scientific]
• Double staining of human gastric cancer tissue specimens and a tissue microarray was performed using HER2 [polyclonal rabbit anti-human c-erbB-2 oncoprotein (1 :300), catalogue no. A0485, DAKO, CiteAb Ltd, UK] and pan-cytokeratin [monoclonal mouse pan cytokeratin plus [AE1/AE3+8/18] (1 :75), catalogue no. CM162, Biocare Medical, US]
• Signal detection was conducted using fluorescence- labeled secondary antibodies [goat anti-rabbit IgG (H+L)-Cross-Adsorbed Secondary Antibody-DyLight 633 (1 :200), catalogue no.
• the Bruker software fleximaging (v. 5.0) was used to export all root mean square normalized mass spectra as processed imzML files.
• An in-house python 3 pipeline was written to perform pixel-wise and parallelized peak picking. For each coordinate (i.e. , spectrum), the peak picking pipeline began by resampling the mass ( ⁇ Vz) and intensity values between 75 and 1100 Dalton (Da) with a step size of 0.0005 Da. Intensity values were resampled by choosing the maximum intensity per window. Noise levels were estimated for windows of 10 Da and all peaks falling below their respective noise level were filtered. The noise level was calculated as 2.2 times the 85 th percentile of the intensity values within the window.
• HMDB Human Metabolome Database
• v. 4.0 Human Metabolome Database
• the metabolite XML file was downloaded for offline use and a local PostgreSQL (v. 11) database was set up. Molecules were annotated by allowing M-H, M-H20-H, M+Na-2H, M+CI and M+K-2H as negative adducts with a mass tolerance of 4 ppm.
• a keyword search was performed on the description text to filter compounds with multiple annotations. Specifically, compounds with indications of being drug-, plant-, food-, or bacteria-specific were filtered stringently.
• the integration of mass spectra and image data is done by coregistering the tissue scanned subsequent to MALDI imaging mass spectrometry and mapping the matrix ablation marks to the imzML-grid.
• the DAPI staining and all other stainings were finally fitted onto the precisely co-registered tissue image.
• SPACIAL now offers the option to create a blacklist image, where the user can manually label regions that should be excluded from subsequent analyses. Such regions may comprise tissue folds, swept away tissue, artefacts, or regions that were generally of no interest.
• FFPE tissue sections of human gastric cancer samples were used to analyse the metabolic heterogeneity within HER2/neu positive tumor regions. Tumor cells were annotated via the pan- cytokeratin staining. They were then classified as HER2/neu positive, if they also exhibited a positive signal in the HER2/neu staining. Otherwise, they were classified as HER2/neu negative.
• Correlation networks were created with Cytoscape (v. 3.7.1). In all networks, nodes represent metabolites with node sizes corresponding to the mean intensity. Edges represent spatial correlations with line thickness and opacity increasing with the correlation coefficient. Nodes were coloured red, if their metabolites take part in glycolysis or they were coloured depending on the molecule super class defined in HMDB [lipids and lipid-like molecules (yellow); nucleosides, nucleotides, and analogues (light red); organic acids and derivatives (green); organoheterocyclic compounds (lime green); alkaloids and derivatives (pink); organic oxygen compounds (blue); benzenoids (violet); phenylpropanoids and polyketides (orange); others (grey)].
• HMDB lipids and lipid-like molecules (yellow); nucleosides, nucleotides, and analogues (light red); organic acids and derivatives (green); organoheterocyclic compounds (lime
• Example 1 The SPACIAL workflow for immunohistochemistrv-quided imaging mass spectrometry
• the SPACIAL pipeline comprises a series of MALDI data and image processing steps to combine molecular data with morphological and immunophenotypic information from immunohistochemistry (IHC) or other imaging data.
• IHC immunohistochemistry
• Immunostaining following MALDI imaging has previously been shown to be feasible, hence the entire workflow works on the very same tissue section.
• the inventors demonstrate that even multiplex immunostainings were entirely possible after MALDI imaging of the very same tissue section, which allows automatic data integration of morphological and spatially resolved in situ data of thousands of molecules via the SPACIAL method.
• the entire tissue and data pre-processing workflow preceding the application of the SPACIAL algorithm includes matrix coating of tissue sections, MALDI imaging, peak picking, matrix removal, IHC staining and image digitalization, which is shown schematically for an islet of Langerhans with glucagon, insulin and DAPI stainings ( Figure 2A).
• SPACIAL then uses MALDI imaging files to create a reference image for subsequent co-registration of the molecular data with other image information ( Figure 2B).
• the digitized and co-registered immunostaining images were scaled to match the exact MALDI resolution and then converted into numerical data without loss of spatial resolution.
• the SPACIAL pipeline paves the way for further statistical calculations and for the analysis of tissue heterogeneity and previously infeasible molecular in situ analyses of cell subpopulations within intact tissue sections. To illustrate the versatility and analytical power of the SPACIAL pipeline, it was applied on two datasets; i.e. , a physiological and a pathophysiological use case.
• Example 2 SPACIAL analysis of metabolic heterogeneity within and between islets of Langerhans
• Correlation networks were created to identify functional relationships of metabolites with glucose 6-phosphate and to assess metabolic heterogeneity within and between individual islets of Langerhans ( Figure 4, Table 2).
• Glucose 6- phosphate was chosen as a relevant example, because it is an important intermediate in the glycolysis, gluconeogenesis and pentose phosphate pathways.
• lipid-associated compounds such as palmitic acid, stearic acid, lysophosphatidylinositol (LPI), and lysophosphatidic acid (LPA) were found to be correlated almost consistently.
• Other compounds such as phosphodimethylethanolamine (P-DME) or glycerophosphoinositol (GroPIns), were found to inconsistently correlate with glucose 6-phosphate.
• Metabolic signatures related to specific cell types and subpopulations can now easily be extracted with SPACIAL.
• Alpha and beta cells were defined automatically as ROIs and metabolic differences between alpha and beta cells were assessed. Significant differences were detected for adenosine diphosphate (ADP), cholesterol sulfate and 3-O-sulfogalactosylceramide ( Figure 5).
• ADP adenosine diphosphate
• cholesterol sulfate and 3-O-sulfogalactosylceramide Figure 5
• the presence of ADP, cholesterol sulfate and 3-O-sulfogalactosylceramide was validated via MALDI FTICR on-tissue MS/MS using quadrupole collision-induced dissociation and comparison to standard compounds (Figure 25).
• Not all islets reveal similar significant changes, also reflecting inter- and intra-islet metabolic heterogeneity.
• Example 3 Intratumoral metabolic heterogeneity in gastric cancer
• the SPACIAL strategy has been shown to be powerful for close-to single-cell analyses of the metabolome in tissues of animal models, but it is also valuable for clinically relevant tissue analyses regarding diagnostics, prognosis, and therapy response prediction. For this reason, the inventors applied the SPACIAL pipeline for the analysis of intra- and intertumoral heterogeneity in gastric cancer. While the inventors used glucagon and insulin to stain alpha and beta cells within a frozen pancreatic tissue section, here the inventors used pan-cytokeratin as an epithelial marker to stain tumor cells and HER2/neu for tumor cell classification within human FFPE tissue sections.
• Regions displaying both pan-cytokeratin and HER2/neu signals were defined as HER2/neu positive tumor regions, while regions displaying only a pan-cytokeratin signal were classified as HER2/neu negative.
• Whole slide immunohistochemical stainings and regions defined as HER2/neu positive (red) and negative (yellow) were shown in Figures 16-21.
• the pixel- accurate annotation allows an unprecedented analysis of metabolic heterogeneity within tumor cells based on metabolic correlation networks that were calculated for annotated metabolites detected and stringently filtered from gastric cancer tissue sections ( Figure 7 and 8). For visualization purposes, a zoom-in of HER2/neu positive and negative tumor regions of Figures 17, 19, and 21 is shown.
• LPI lysophosphatidylinositole
• the intratumoral heterogeneity is most prominent in tumor sample C, reflected by the difference in degree, average shortest path length and clustering coefficient of HER2/neu positive and negative metabolic networks ( Figure 7, 8 and Table 1). Overall, the degree and clustering coefficient of the glucose 6-phosphate node varies more strongly between patient samples, than between HER2/neu positive and negative tumor regions within individual patient samples - reflecting intertumoral heterogeneity (Table 1).
• Example 4 Intertumoral metabolic heterogeneity in gastric cancer [00198]
• metabolic correlation networks were created for gastric cancer patient tissues from an FFPE tissue microarray ( Figure 8A-E). Networks on the extracted HER2/neu positive tumor regions of five gastric cancer patients comprise 30 to 39 metabolites and exhibit diverse correlation patterns. Similar to the results from whole gastric cancer resection specimens, most of the correlating metabolites were lipids. An altered lipid metabolism has been described previously in a HER2/neu positive breast cancer model. Thus, a changed lipid metabolism may be associated with a high positive correlation of individual lipids to glucose 6-phosphate in human gastric cancer patients.
• Example 5 Data preprocessing and image co-registration
• the coordinates from the imzML file were used to generate an image of the measurement region (imzML-grid) ( Figure. 26).
• the resolution of this image is a multiple of the imzML resolution.
• a two- step image co-registration can be performed via existing software (e.g. Fiji ImageJ, Gimp or Photoshop) and can be achieved by first registering the tissue overview image with visible tissue or matrix-specific markers onto the grid-image and then registering all stainings simultaneously onto the tissue overview image. Once all images were co-registered, they all have the same resolution (Figure. 27 and Figure. 28).
• the immunostaining images were then scaled to the exact same resolution as the x,y-grid from the imzML file ( Figure. 29). Downscaling is done by calculating the mean colour value per square pixel region. Optionally, pixels with no other colour pixel within x ⁇ 2 and y ⁇ 2, can be removed.
• a black and white image can now be created to mask artefacts (e.g. tissue folds) or split measurement regions (see paragraph ‘Regions of interest’). Masked regions were excluded from further analyses ( Figure. 30).
• the user can now define a threshold to decide at what colour value a pixel is classified as a positive signal ( Figure. 31).
• the image mask When the image mask is imported, it is also converted into coordinates.
• the previously determined colour thresholds were used to generate two preliminary regions (lists of x,y-coordinates) per staining: e.g. staining X positive and staining X negative pixels ( Figure. 31).
• Individual IMS measurement regions may also be considered as preliminary regions. This is important for the analysis of tissue microarrays, where tissue cores of several of patients were measured on one glass slide. Usually each patient sample is measured in a separate measurement region. Nevertheless, if two patient cores are part of the same measurement region, the measurement region can be split by separating them with a black line in the masking image (see paragraph ‘Image masking’) ( Figure. 32).
• the user can define custom regions, either by creating one black and white image per region, similar to the masking image, or by creating a multicolour image, where each colour except black is considered as one region. This is used for the analysis of tissue microarrays, where multiple cores may belong to the same patient. First, an image is generated, where each measurement region is coloured uniquely ( Figure. 33). The user can then manually colorize regions that belong to the same patient/group ( Figure. 34).
• HMDB 4.0 the human metabolome database for 2018. Nucleic Acids Research 46(November 2017): 608-17, Doi: 10.1093/nar/gkx1089.

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