Systems and methods for spatial proteomics

Abstract

The present disclosure relates to systems and methods for spatial proteomics in cells or tissues. Particularly, the methods include labeling proteins in a cell or tissue with one or more mass tags at one or more target locations within the cell or tissue, and determining the identity and / or abundance of any or all of the proteins, protein modifications, or protein complexes (e.g., the proteome) in the one or more target locations by mass spectrometry analysis.

Description

SYSTEMS AND METHODS FOR SPATIAL PROTEOMICSTECHNICAL FIELD

[0001] The present disclosure relates to systems and methods for spatial proteomics in cells or tissues. Particularly, the methods include labeling proteins in a cell or tissue with one or more mass tags at one or more target locations within the cell or tissue, and determining the identity and / or abundance of any or all of the proteins, protein modifications, or protein complexes (proteome) in the one or more target locations by mass spectrometry analysis.CROSS REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application Nos. 63 / 655,358, filed June 3, 2024, and 63 / 690,640, filed September 4, 2024, the contents of which are herein incorporated by reference in their entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0003] This invention was made with government support under grant numbers CA274514, CA271079, and CA271895 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND OF THE INVENTION

[0004] Spatial proteomics seeks to understand the spatial organization of proteins, protein modifications, or protein complexes in tissues or at different subcellular localizations in their native environment. Capturing the spatial organization of proteins, protein modifications, or protein complexes, however, is challenging. Histological imaging methods provide histology of the diseased cells but lack detailed molecular information. Immunohistochemistry for specific proteins is limited in depth. Current mass spectrometry-based methods require examination of spatially enriched samples out of their original context, but excel in the deep profiling of the proteomic of the enriched samples. Thus, there is a need for improved methods for understanding the spatial organization of proteome.SUMMARY OF THE INVENTION

[0005] The disclosure provides methods and systems for spatial proteomics.

[0006] In some embodiments, the methods comprise one or more of: labeling proteins at one or more target locations within a cell or tissue sample with one or more mass tags; and determining the identity and / or abundance of any or all of the proteins, protein modifications, or protein complexes in the one ormore target locations by mass spectrometry analysis. In some embodiments, the methods do not comprise isolating any one or all of the one or more target locations.

[0007] In some embodiments, the labeling is manual or automated.

[0008] In some embodiments, the one or more target locations are determined by annotating the cell or tissue sample by cell type, histological patterns, and / or pathological states. In some embodiments, the methods further comprise staining the cell or tissue sample with one or more histological or pathological labeling agents.

[0009] In some embodiments, the one or more target locations comprise one or more cell types, one or more cellular structures, one or more sections of a tissue sample, one or more sites of disease, or a combination thereof. In some embodiments, the methods further comprise comparing the identity and / or abundance of any or all of the proteins between at least two or more target locations.

[0010] In some embodiments, each of the one or more target locations is labeled with a different mass tag or a specific proteolytic treatment. In some embodiments, each of the one or more mass tags are distinguishable from each other by mass spectroscopy.

[0011] In some embodiments, the methods further comprise quenching the labeling step.

[0012] In some embodiments, the methods further comprise lysing and proteolysis the cell or tissue sample.

[0013] In some embodiments, the methods further comprise separating the proteins electrophoretically or chromatographically prior to mass spectrometry analysis.

[0014] In some embodiments, the tissue sample is a fresh tissue sample, a frozen tissue sample, of a fixed or processed tissue sample. In some embodiments, the tissue sample is a tissue slice or section or a tissue microarray. In some embodiments, the tissue sample comprises sequential slices or sections of a tissue. In some embodiments, the methods further comprise assembling a three-dimensional spatial proteomic analysis of the tissue.

[0015] hi some embodiments, the tissue sample is a mammalian tissue sample or a plant tissue sample. In some embodiments, the tissue sample is a diseased tissue sample. In some embodiments, the diseased tissue sample is a cancerous tissue sample. In some embodiments, the methods further comprise comparing altered levels of protein abundance, protein modifications, or protein complexes between the diseased cells or tissue areas and a healthy or non-diseased control.

[0016] Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

[0018] Having thus described the presently disclosed subject matter in general terms, reference will now be made to the accompanying Figures, which are not necessarily drawn to scale, and wherein:

[0019] FIG. 1 is a schematic overview of an exemplary Spatial Proteomics through On-site Tissue Proteins Labeling (SPOT) workflow. Tissue slides are first annotated by cell types, histological patterns, or pathological states, followed by applying tandem mass tags (TMTs) directly onto regions of interest. After on-slide labeling and quenching of the TMTs, the tissue would be lysed, digested, and cleaned up for the downstream proteomic analysis using a mass spectrometer. The proteolysis of proteins could be applied directly onto regions of interests.

[0020] FIGS. 2A-2B show the numbers of identified proteins from each type of sagittal mouse brain slide. FIG. 2A is protein identifications of frozen, untreated Formalin-Fixed Paraffin-Embedded (FFPE), deparaffinized FFPE, and deparaffinized / de-crosslinked slides (all were unstained). FIG. 2B is protein identifications of frozen, hematoxylin and eosin (H&E) stained, hematoxylin (H) stained, and eosin (E) stained slides.

[0021] FIGS. 3A-3B show the spatial proteomic analysis of mouse brain tissues. FIG. 3A is a mouse brain slide in horizontal view. Eight different regions are color-coded as shown and a scale bar to show the size of the brain slide. The scanning image was augmented using the filter “Hematoxylin” and brain regions were marked using QuPath52. FIG. 3B is hierarchical clustering illustrating the proteomic quantification results across 8 brain regions. Protein expressions could be clustered into 8 clusters, each revealing a distinctive spatial trend displayed on the left side of the heatmap.

[0022] FIGS. 4A-4D show on-site TMT labeled frozen prostate cancer tissue slide. FIG. 4A is bright- field scanning of the adjacent prostate cancer H&E slide annotated with normal (yellow), Gleason 3 (cyan), Gleason 4 (blue), and Gleason 5 (purple) regions. FIG. 4B is principal component analysis of Gleason score regions based on the protein expression profiles. FIG. 4C is hierarchical clustering based on the expression profiles of 289 proteins across different Gleason score regions. FIG. 4D is significantly changed proteins (absolute log2 fold change >1, p-value < 0.05) from pairwise comparison of two different Gleason score regions.

[0023] FIGS. 5A-5D show on-site TMT labeled prostate cancer tissue microarray (TMA) slide with paraffin. FIG. 5A is bright-ficld scanning of the adjacent prostate cancer H&E slide annotated withnormal (yellow), Gleason 3 (cyan), Gleason 4 (blue), and Gleason 5 (purple) regions. FIG. 5B is PCA analysis based on the protein expression profiles in different Gleason score regions. FIG. 5C is hierarchical clustering using the expression profiles of 265 proteins across different Gleason score regions. FIG. 5D is significantly changed proteins (absolute log2 fold change >1, p-value < 0.05) from pairwise comparison of two different Gleason score cores.

[0024] FIGS. 6A-6C are representative examples of automated histology recognition using artificial intelligence (Al).

[0025] FIG. 7A is a schematic of an exemplary automated tissue-barcoding platform, referred to herein as SPOTTER. FIG. 7B is a graph of reproducibility of printed dots for various combinations of inner diameter nozzles and flow rates.

[0026] FIG. 8A is a schematic overview of an exemplary workflow. FIG. 8B is a schematic of brain regions. FIG. 8C is principal component analysis (PCA) of proteomic divergence among brain regions. FIG. 8D is a graph showing proteins identified in horizontal mouse brain sections.

[0027] FIG. 9A is volcano plots of protein intensities normalized to the TMT 126 reference channel, log2-transformed, and subjected to median scaling across channels for each brain region. FIG. 9B shows hierarchical clustering illustrating the proteomics results based on region and functional enrichment.

[0028] FIG. 10A is volcano plots of phosphopeptide intensities normalized to the TMT 126 reference channel, log2-transformed, and subjected to median scaling across channels for each brain region.Differential proteomic analysis reveals region-enriched proteins across the murine brain. Volcano plots display the log2 fold change versus statistical significance (-logio adjusted p-value) for each brain region, highlighting proteins that are significantly enriched relative to the median of all other regions. FIG. 10B shows hierarchical clustering illustrating the phosphopeptide results based on region and functional enrichment.

[0029] FIG. 11 are images of an exemplary for use in SPOTTER.

[0030] FIG. 12 is a Venn diagram showing the overlap of identified phosphopeptides across experimental replicates of SPOTTER to murine brain tissues.DETAILED DESCRIPTION OF THE INVENTION

[0031] Disclosed herein are systems and methods for spatial proteomics. Spatial proteomics seeks to understand the spatial organization of proteins in tissues or at different subcellular localization in their native environment. However, capturing the spatial organization of proteins is challenging. Current technologies for exploring tissue cellular landscapes include multiplex imaging or spatial proteomicsthrough mass spectrometry. These techniques, however, suffer from limited proteome coverage, low throughput, poor reproducibility, or arduous procedures. Spatial proteomics through mass spectrometry analysis includes macro-dissection or micro-dissection (e.g., laser microdissection (LMD)) of cells from tissues followed by mass spectrometry analysis suffers from arduous procedure and poor reproducibility. Multiplex imaging, both label (e.g., multiplex ion beam imaging (MIBI), immunofluorescence (IF), and imaging mass cytometry (IMC)) and label-free (e.g., matrix-assisted laser desorption ionization (MALDI) mass spectrometry, secondary ion mass spectrometry (SIMS), and laser-ablation electrospray ionization (LAESI) mass spectrometry) exhibits superior resolution, specificity, and sensitivity, but has low proteome coverage and labeling limitations.

[0032] The systems and methods disclosed herein, termed Spatial Proteomics through On-site Tissueprotein-labeling (SPOT), combine the direct labeling of tissue proteins in situ and quantitative mass spectrometry for the profiling of spatially-resolved proteomics. Thus, the presently disclosed methods achieve deep proteomic profiling of proteins while retaining their spatial context. In representative examples, mouse brain tissue slides demonstrated that SPOT identifies proteins in different cellular compartments. Further, when the application of SPOT was extended to frozen tissues on slides from prostate cancer, a distinct proteomic profile was observed among the regions with different Gleason scores.1. Definitions

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present invention. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

[0034] The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “an” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of,” and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

[0035] The modifier “about” used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (for example, it includes at least the degree of error associated with the measurement of the particular quantity). The modifier “about” should also be considered as disclosing the range defined by the absolute values of the two endpoints. For example, the expression “from about 2 to about 4” also discloses the range “from 2 to 4.” The term “about” may refer to plus or minus 10% of the indicated number. For example, “about 10%” may indicate a range of 9% to 11%, and “about 1” may mean from 0.9-1.1. Other meanings of “about” may be apparent from the context, such as rounding off, so, for example “about 1” may also mean from 0.5 to 1.4.

[0036] For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

[0037] The term “and / or” as used in a phrase such as “A and / or B” herein is intended to include both A and B; A or B; A (alone); and B (alone). Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to encompass each of the following embodiments: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0038] Preferred embodiments of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.2. Spatial Proteomics

[0039] Spatial proteomics includes approaches for mapping protein heterogeneity across cell and tissue samples. However, existing methods for spatial proteomics are challenged by microsampling techniques and small-scale detection and analysis. The methods and system described herein do not require isolation of microsamples of desired areas of interest and allow analysis of full tissue or cell samples by mass spectrometry. Protein identity and abundance is determined in the areas of interest within the sample by the nature and identity of their respective mass tags. The methods and systemdisclosed herein may be used to investigate basic cellular function in normal physiological states or determine changes in the normal physiological state due to diseases or disorders.

[0040] The methods comprise labeling proteins at one or more target locations within a cell or tissue sample with one or more mass tags. The labeling in the one or more target locations may be done manually, e.g., micropipetting or syringe administration of mass tags to the one or more locations, or fully or partially automatically, e.g., by instrumentation which identifies the target locations and applies the mass tags to the one or more locations. For example, the labeling may utilize an automated spatial proteomics system.

[0041] In some embodiments, an automated spatial proteomics system comprises a high-pressure nanoflow pump system and a 3D printing system to spatially apply reagents to a cell or tissue sample. In some embodiments, a non-contact dispensing mechanism, such as a piezoelectric or thermal inkjetbased printing system, is employed to achieve precise and reproducible delivery of mass tags. In some embodiments, the administration of labeling tags involves the utilization of a programmable robotic reagent deposition system.

[0042] In some embodiments, reverse printing is utilized to label proteins at one or more target locations within a cell or tissue sample with one or more mass tags. “Reverse printing” refers to the deposition of labeling reagents onto a surface prior to the application of the biological sample, allowing subsequent labeling through physical contact and diffusion. In select embodiments, one or more mass tags are deposited in defined patterns or regions onto a blank solid support (e.g., a microscope slide). The one or more mass tags can be patterned onto a solid support using any application method, manual or fully or partially automatic, e.g., using an inkjet-based printing system. The tissue section is then overlaid onto the pre -patterned slide, allowing for localized diffusion and labeling of proteins at corresponding positions in the tissue. The reverse printing method ensures precise spatial registration of the labels and is compatible with downstream proteomic workflows such as mass spectrometry

[0043] The one or more locations may be determined by annotating the cell or tissue sample by cell type, histological patterns, and / or pathological states. In some embodiments, the methods further comprise annotating the cell or tissue sample. The annotation may be done manually by an individual examining, mapping, and annotating a particular cell or tissue sample from an image or as seen on a microscopic slide. Alternatively, the annotation may be automated by the use of imaging technologies and / or artificial intelligence, as shown in Figure 6 (See, Chen, Jing, et al., Clinical Proteomics 10 (2013): 1-11 (for Figure 6A and 6B) and Bulten, Wouter, et al., arXiv: 1907.07980 (2019) (for Figure6C)). The annotation may identify one or more target locations based on cell type, cellular structures, sections of a tissue sample, sites of disease, or combinations thereof.

[0044] In some embodiments, the annotations include “grading” a disease or cancerous tissue. Grading systems differ based on the disease or cancer. For example, tumors may be graded depending on the amount of abnormality on a scale of 1 to 5 or G0-G4, breast cancer grading systems include Nottingham (also called the Elston-Ellis modification of the Scarff-Bloom-Richardson) grading system, and prostate cancer grading is based on Gleason scores of 1-10.

[0045] Accordingly, in some embodiments, the methods comprise staining the cell or tissue sample with one or more histological or pathological labeling agents. Histological and pathological agents can include a wide range of dyes, labeled antibodies or other affinity agents, primary and labeled secondary antibody pairs, and the like. Exemplary histological or pathological labeling agents include hematoxylin, eosin, van Gieson stain, toluidine blue, alcian blue, Giemsa, reticulin, nissl stain, orcein stain, Sudan black B, Masson’s trichrome, Mallory’s trichrome, Azan trichrome, Cason’s trichrome, PAS (Periodic acid Schiff) stain, Weigert's resorcin fuchsin (Weigert’s elastic) stain, Wright and Wright Giemsa stain, Aldehyde fuchsin and the like.

[0046] The one or more target locations may comprise one or more cell types, one or more cellular structures, one or more sections of a tissue sample, one or more sites of disease, or a combination thereof, as determined by the annotation described above. Each of the one or more target locations may be labeled with a different mass tag to facilitate determination of which protein is associated with which target location(s). For example, two different target locations, but both with similarity in cell type and / or pathological state may be labeled with the same mass tag. Alternatively, two different target locations, even while sharing similarity in cell type and / or pathological state may be labeled with a different mass tag.

[0047] A variety of mass tags are known in the art and suitable for use with the disclosed methods. In some embodiments, each of the one or more mass tags are distinguishable from each other by amino acid sequences of representative polypeptides from the same protein in different cells or tissue areas. The different polypeptides could be from different proteolysis of proteins from different cells or tissue areas. In some embodiments, each of the one or more mass tags are distinguishable from each other by mass spectrometry, e.g., the peaks derived from individual mass tags can be clearly separated from one another. This allows discrimination between proteins derived from different target locations. The disclosed methods may also use an array of mass tags, comprising two or more sets of mass tags, wherein the aggregate mass of each of the mass tags in any one set is different from the aggregatemass of each of the mass tags in every other set in the array. In some embodiments, the array of mass tags arc chemically identical or substantially chemically identical. The term “substantially chemically identical” means that the mass labels have the same chemical structure, into which particular isotopic substitutions may be introduced or to which particular substituents may be attached.

[0048] In some embodiments, the mass tags are isobaric mass tags or a set of isobaric mass tags. Isobaric mass tags within a set of isobaric mass tags may have substantially the same aggregate mass (total mass of the mass tag, e.g., the sum of the masses of all the components of the mass tag, such as mass marker, the cleavable linker, mass normalization moiety, etc.), as determined by mass spectrometry, but each tag releases a mass reporter ion of unique mass on collision induced dissociation in a mass spectrometer. Thus, in some embodiments, the labeling is isobaric labeling, which involves labeling peptides or proteins with various chemical groups of identical aggregate mass (isobaric). Exemplary isobaric tandem mass tags include, but are not limited to, TMT (tandem mass tags), iTRAQ (isobaric tags for relative and absolute quantification), DiART (deuterium isobaric amine reactive tags), Crr (caltech isobaric tags), CILAT (cleavable isobaric labeled affinity tags), DiLeu (N,N-dimethyl leucines) quantitation tags, IPTL (isobaric peptide termini labelling) tags, QITL (quantitation by isobaric terminal labeling) tags, IVTAL (in vivo terminal amino acid labeling), and EASI-TAG (Easily Abstractable Sulfoxide-based Isobaric tag). Any tag that can realize the isobaric tandem mass labeling can be used in the disclosed methods.

[0049] In some embodiments the methods further comprise quenching the labeling. The labeling reaction may be quenched by adding a competing agent, changing the pH of the sample to outside of the working range for the label, use of a quenching agent, or other known methods depending on the nature of the mass tags being utilized.

[0050] In some embodiments, the methods comprise lysing and / or proteolysis of the cell or tissue sample. For example, the cells in the cell or tissue sample may be lysed using known chemical or physical techniques, releasing the contents of the cells and tissues, including proteins of which were labeled during the labeling step. The protein portion of the lysed sample may be isolated from the remaining contents by centrifugation, density gradients, or precipitation-based methods known in the ail. The isolated proteins or the cell or tissue sample may be proteolyzed using a chemical or enzyme, such as trypsin, chymotrypsin, clostripain, pepsin, rLys-C protease, Glu protease (Glu-C), endopeptidase (Lys-C), and Arg-C protease.

[0051] Tn some embodiments, the methods do not comprise isolating any one or all of the one or more target locations. Thus, the one or more target locations arc processed in bulk following the labeling step.

[0052] In some embodiments, the methods further comprise separating the proteins electrophoretically or chromatographically (e.g., before or after lysing and proteolysis). In some embodiments, the separation (e.g., liquid chromatography or capillary electrophoresis) is directly coupled to the mass spectrometry analysis. The electrophoretic or chromatographic separation may be based according to different hydrophilicity, hydrophobicity, charge, size, or other biochemical characteristics of the peptides. For example, the electrophoretic or chromatographic separation may separate peptides based on one or more post-translational modifications, including but not limited to phosphorylation, glycosylation, lipidation, acetylation, methylation, amidation, and hydroxylation.

[0053] The methods comprise determining the identity and / or abundance of any or all of the proteins in the one or more target locations by mass spectrometry analysis. Mass spectrometry (MS) assays are well known to those of skill in the art. In relation to this invention the term “mass spectrometry” shall include any type of mass spectrometry capable of fragmentation analysis. The mass spectrometers suitable for use in the present invention include instruments that comprise any form of MS / MS analyzer such as an Orbit trap or triple quadrupole mass spectrometer equipped with a collision chamber, an ion trap mass spectrometer capable of fragmenting selected precursor ions by fast atom bombardment, collision induced dissociation, electron transfer dissociation or any other form of parent ion fragmentation, and matrix assisted laser desorption / ionization (MALDI) mass spectrometers fitted with a dual time of flight (TOF / TOF) analyzer and a means of parent ion fragmentation.

[0054] Tandem mass spectrometry (MS / MS) is used to produce structural information about a compound by fragmenting specific sample ions inside the mass spectrometer and identifying the resulting fragment ions. MS / MS assays typically comprise two or more MS steps with some form of fragmentation taking place between the steps. Some MS / MS assays can also be performed on certain single analyzer mass spectrometers such as ion trap and time-of-flight instruments, for example, by using a post- source decay experiment to effect the fragmentation of sample ions.

[0055] Mass spectrometry can be used to identify and / or quantify the proteins in the tissue sample, and those specifically associated with the one or more target areas. For example, the observed peptide masses or fragment ions are analyzed against theoretical digests or amino acid sequences to those from known protein sequences or sequence databases. The level of the identified proteins can be determined using a number of known methods depending on the desired level of quantitation (e.g., semi-quantitativeor precise quantitation). For example, the mass spectrometry intensity value can be correlated with a quantity based on a calibration or across different samples in order to yield a quantification of the identified protein. Alternatively, labeling methods which include isotopic labeling can be used to derive quantitative information about protein abundance based on the ratios of the mass tags labeled on proteins from different cells or tissue areas.

[0056] The methods described herein may further make use of an internal standard. An internal standard is a single type of protein or a mixture of many types of proteins which provides an internal check of identification and / or quantity during the mass spectrometric analysis. Protein(s) used in the internal standard may be similar or close to the protein(s) in the samples. Known amounts of internal standards can be added to multiple samples in such a manner that for a series of analyses ion intensities in the mas spectrometric scan can be normalized based on the ion intensity of the common internal standard, thereby providing more accurate comparisons between the separate analyses, reducing the analytical variability of the study.

[0057] The methods described herein may include analysis of a “reference,” “control,” or “comparator” sample or spectrometric data. For example, the methods may optionally involve comparing the spectrometric data of the one or more target areas to one or more comparator spectrometric data. If the spectrometric data obtained from a one or more target areas or corresponds sufficiently to the comparator spectrometric data, then optionally a positive determination (e.g., cell type, disease state, etc.) may be made.

[0058] The method described herein may include analysis of a calibration sample. A calibration sample may be used to provide a relationship between the quantity of a target protein as measured by mass spectrometry to the actual quantity of the analyte in the test sample. Thus, the calibration sample is usually a sample of various (e.g., two or more) known concentrations of one or more proteins. The quantity of the target protein in the sample is then calculated by the intensity as determined by mass spectrometry against the calibration graph generated from ion intensities of two or more concentrations analyzed of the calibration sample. Alternatively, the calibration sample may include a quantity of a select protein which is representative of a state of the tissue (e.g., healthy, disease, etc.) such that comparison between the calibration sample and the target protein provides a measure of the state of the tissue. The calibration sample may be a natural sample such as a body fluid or a tissue extract or may be synthetic, as for the sample to be assayed. The calibration sample may comprise a recombinantly expressed protein, synthetically manufactured peptide. The calibration sample maycomprise only select target proteins which are being assayed in the sample (e.g., specific biomarkers), and not any other components of the sample.

[0059] The term “tissue” is used herein to denote any structure of cells, which may optionally be, for example, a structure, an organ, or part of a structure of organ. The tissue sample may be derived from a human or a non-human animal or plant tissue. Examples of tissues from which a tissue sample may be derived include: adrenal gland tissue, appendix tissue, bladder tissue, bone, bowel tissue, brain tissue, breast tissue, bronchi, ear tissue, esophagus tissue, eye tissue, endometrioid tissue, gall bladder tissue, genital tissue, heart tissue, hypothalamus tissue, kidney tissue, large intestine tissue, intestinal tissue, larynx tissue, liver tissue, lung tissue, lymph nodes, mouth tissue, nose tissue, pancreatic tissue, parathyroid gland tissue, pituitary gland tissue, prostate tissue, rectal tissue, salivary gland tissue, skeletal muscle tissue, skin tissue, small intestine tissue, spinal cord, spleen tissue, stomach tissue, thymus gland tissue, trachea tissue, thyroid tissue, ureter tissue, urethra tissue, soft and connective tissue, peritoneal tissue, blood vessel tissue and / or fat tissue.

[0060] The tissue sample may be a fresh or frozen tissue sample. The tissue sample may be a processed (e.g., embedded) or fixed tissue sample (e.g., a tissue treated with a fixative selected from an aldehyde, a mercurial, an alcohol, an oxidizing agent, a picrate). For example, the tissue sample may include a hematoxylin and eosin (H&E)-stained tissue sample, a hematoxylin (H)-stained tissue sample, an eosin (E)-stained tissue sample, a formalin-fixed paraffin-embedded (FFPE) tissue sample, a deparaffinized and / or decrosslinked FFPE tissue sample.

[0061] The tissue sample may include one or more tissue slices or sections. The tissue sample may comprise two or more sequential slices or sections of a tissue. The tissue sample may be a tissue microarray. The methods and analysis disclosed herein may comprise analyzing two or more sequential slices or sections to generate or assemble a three-dimensional spatial proteomic analysis of the tissue.

[0062] The analysis may optionally relate to a disease or condition. Thus, in some embodiments, the tissue sample is a diseased tissue sample. Diseased tissue samples may be a tissue sample from any subject known or suspected of having a disease or disorder. Alternatively, the disease tissue sample may be a tissue sample which comprises portion of the sample with a known site of disease. For example, the tissue sample comprises normal or healthy tissue and diseased tissue in a single sample. In some embodiments, the tissue sample is a cancerous tissue sample. The cancerous tissue sample may comprise grade I, grade II, grade III, or grade IV cancerous tissue; metastatic cancerous tissue; mixed grade cancerous tissue; sub-grade cancerous tissue; or any known or suspected cancerous or abnormal tissue. In some embodiments, the tissue sample is a tumor tissue sample.

[0063] The methods and analyses disclosed herein may comprise comparing differentially expressed proteins between the diseased tissue sample and a healthy or non-discascd sample. Thus, the methods disclosed herein may obtain information relevant to: disease diagnosis or prognosis, monitoring progression of a disease, disease treatments and response thereto, disease etiology and / or spread, and or susceptible tissues to disease.6. ExamplesMaterials and Methods

[0064] Tissue Sample Collection and Preparation

[0065] Mouse brain slides (7 pm) were purchased from Zyagen (San Diego, California. MF-201-HS for frozen slides and MP-201-SS for FFPE slides). Briefly, frozen mouse brain slides were air-dried to remove moisture and stained with 0.1% Mayer’s hematoxylin (Sigma, MHS32) for 10 minutes in a 50 mL conical tube, then rinsed in warm running tap water for 15 minutes for the “bluing” of the slides. Following this, slides were then dipped in ddFbO for 30 seconds. For eosin staining, air-dried nonstained or H-stained slides were placed in 95% reagent alcohol (Sigma, R8382) for 30 seconds, then transferred to eosin Y alcoholic solution (Sigma, HT1101) for 60 seconds. Stained slides were dehydrated through two changes each of 95% reagent alcohol, 100% reagent alcohol and xylene for two minutes each. No cover slips were mounted.

[0066] For SPOTTER workflow: Prior to staining, sections were equilibrated to room temperature (20-22°C) and rehydrated by submerging slides in IX phosphate-buffered saline (PBS; Thermo Fisher Scientific, 10010023) for 5 minutes to remove residual embedding medium and restore tissue hydration. Excess PBS was carefully removed using a low-pressure nitrogen stream to avoid tissue detachment. To visualize cellular nuclei and anatomical landmarks, sections were stained with 0.1% Mayer’s hematoxylin (Sigma- Aldrich, Catalog # MHS32), a regressive stain ideal for differentiating nuclear chromatin. Hematoxylin was applied to fully cover the tissue section (500 pL / slide) and incubated for 5 minutes at room temperature. Unbound stain was removed by rinsing slides under running tap water gently for 2 minutes, removing excess dye while preserving tissue integrity. To enhance nuclear contrast and stabilize the hematoxylin stain, slides were immersed in 0.005% ammonium hydroxide (NH4OH) solution (prepared in 50 mL conical tubes) for 1 minute. This step neutralizes acidic residues, converting the hematoxylin’s reddish hue to a permanent blue-black signal. Following bluing, slides were rinsed twice in distilled water to eliminate residual NH4OH and prevent over- alkalization. Stained sections were gently dried under a controlled nitrogen stream (5-10 psi) to minimize oxidation or crystallizationartifacts. To preserve spatial proteomic compatibility, slides were stored at -80°C in airtight, desiccated slide boxes until further use. No coverslip was applied to avoid physical compression of the tissue or interference with downstream SPOTTER workflow.

[0067] Fresh frozen prostate cancer tissue samples were obtained from JHU Pathology Core / Biospecimen Bank with approval from the Institutional Review Board of Johns Hopkins Medical Institutions. A standard tissue collection procedure was used. Briefly, prostate cancer tissue specimens were immediately embedded in optimal cutting temperature (OCT) compound and snap-frozen (as tissue block) in liquid nitrogen. Frozen tissue blocks were stored at -80°C until further processing. The sections (4-5 pm) were cut using a cryostat and mounted onto glass slides. Fresh frozen prostate cancer tissue slides were stained by hematoxylin and eosin (H&E) following aforementioned procedure for morphological evaluations. The targeted areas were identified by a pathologist.

[0068] Prostate cancer tissue microarrays (TMA) were constructed using FFPE tissue blocks obtained from surgically resected prostate cancer. In the TMA, representative cancer areas were extracted as small cores (0.6 mm), and then embedded into a new TMA block. 5 pm sections were cut from the TMA block and used for the experiment. To remove the paraffin from FFPE sagittal mouse brain slides, FFPE slides were first baked in an oven at 60°C for 10 minutes, and soaked in xylene (10 minutes X 2). The slides were then subject to serial washes of 100% ethanol (5 minutes X 1), 70% ethanol (5 minutes X 1), 50% ethanol (5 minutes X 1) and HPLC-grade water (5 minutes X 1). To decrosslink proteins, deparaffinized slides were incubated in pH 8.0 lOOmM Tris buffer at 70°C for 20 minutes, washed with IX PBS buffer (3 minutes X 1) and HPLC-grade water (3 minutes X 1), and dried with nitrogen gas in the end.

[0069] Tissue Sample Annotation

[0070] The hematoxylin- stained mouse brain section was first imaged at 40X magnification using bright-field microscopy to capture baseline histological morphology. Prior to reference dots deposition, the slide was scanned on a GenePix 4000B microarray scanner (Molecular Devices) at two wavelengths (532 nm and 635 nm) with a resolution of 10 pm / pixel to establish pre-deposition spatial compositions. A grid of 19 x 48 reference markers (912 total dots) was robotically deposited onto the tissue using a solution containing 0.1% Mayer’s hematoxylin, acetonitrile, and ddH2O (2: 1:1 v / v) to create spatial reference points. Post-deposition, the slide was re-imaged under identical GenePix 4000B settings to register the reference dots relative to tissue anatomy. Tissue architecture was annotated using data from the Allen Mouse Brain Atlas and pre-existing MRI images to align anatomical regions with the reference dots. Coordinates corresponding to atlas-matchcd regions of interest (ROIs) werecomputationally extracted and converted into individual Geode files using custom scripts. These Geodes defined the spatial coordinates and printing parameters for subsequent TMT deposition.

[0071] For the prostate tissue slides, Gleason scores of the prostate cancer were re -reviewed by the American Board of Pathology certified pathologist, who has experience with prostate cancer. The targeted areas with different Gleason scores were selected and marked on the H&E slides. In addition, the slides were assessed under the light microscope at various magnifications, including low power (e.g., 4x or lOx) for overall tissue architecture assessment and high power (e.g., 20x or 40x) for detailed cytological characterization. The International Society of Urological Pathologycriteria and guidelines for the classification of prostate cancer were used (ISUP40). Herein, all Gleason scores of 3 to 5 were included.

[0072] Tissue Sample Labeling Using TMT

[0073] Each TMT reagent (Thermo Scientific) vial was carefully opened, and the contents were gently suspended using the recommended volume of anhydrous acetonitrile by the manufacturer. The reagent was mixed thoroughly to ensure complete dissolution.

[0074] Suspended TMT reagents were diluted 1:5 using 500mM HEPES (TMT final concentration was I O g / pL in lOOmM HEPES (25 % v / v acetonitrile for SPOTTER) and applied or printed directly to areas of interest using the pipette / SPOTTER. The same procedure would be repeated for a total of 5 times, and between each pipetting / printing, sections with labeling reagent would be left to air-dry. After labeling the sections of interest 5 times, 5% hydroxylamine was applied similarly to quench the labeling.

[0075] Tissue Lysis and Digestion

[0076] Labeled tissue samples were scraped off the slides using a scalpel and subjected to 8 M urea lysis buffer (8 M urea, 75 mM NaCl, 50 mM Tris-HCl, pH8). Enzymatic tryptic digestion was performed as previously described (Zhou, I. Y. et al. J Proteome Res 16, 4523-4530 (2017)). Digested tissue samples were cleaned up by SCX tip, desalted by C18 StageTip, and dried using Speed-Vac.

[0077] IMAC Enrichment for Phosphopeptides

[0078] Dried peptides were suspended in 3%(v / v) ACN, 0.1%(v / v) TFA and peptide concentration was measured using Nanodrop (Thermo Scientific). Aliquots of -300 pg peptides were then reconstituted into 600 pL of 80%(v / v) ACN, 0.1%(v / v) TFA (peptide concentration controlled at -0.5 pg / pL) for the following IMAC enrichment.

[0079] IMAC procedure was performed using Fe3+-NTA agarose beads that were freshly prepared using Ni2+-NTA agarose beads (QIAGEN, cat no. 30210) as previously described34. Samples constituted in 80%(v / v) ACN, 0.1%(v / v) TFA were incubated with 100 pL of 5% (v / v) Fc3+-NTAagarose beads to conjugate for 30 min at RT. After conjugation, the supernatant containing unbounded peptides was collected by centrifugation. The beads with conjugated peptides were carefully transferred onto C18 Stage Tip and washed three times of 200 pL 80% (v / v) ACN, 0.1% (v / v) TFA. The washes were combined with supernatant for subsequent enrichment process. The peptides conjugated to beads were eluted with 100 pL potassium phosphate buffer (500 mM KH2PO4, pH7) for 3 times and 50% (v / v) ACN, 0.1% (v / v) FA. Eluted phosphopeptides and unbounded peptides from IMAC were dried and stored at -80 °C for LC-MS / MS analysis.

[0080] LC-MS / MS Analysis - SPOT

[0081] The analytical column was manufactured in-house using ReproSil-Pur 120 C18-AQ 1.9 pm stationary phase (Dr. Maisch GmbH) and slurry packed into a 28-cm length of 360 pm o.d. x 75 pm i.d. fused silica picofrit capillary tubing (New Objective). The analytical column was heated to 50°C using a column heater (Phoenix-ST). The analytical column was equilibrated to 98% Mobile Phase A (MP A, 3% (v / v) ACN, 0.1% (v / v) FA) and 2% Mobile Phase B (MP B, 90% (v / v) ACN, 0.1% (v / v) FA) and maintained at a constant column flow of 200 nL / min. The sample was injected into a 12 pL loop placed in line with the analytical column which initiated the gradient profile (min:%MP B): 0:2, 1:6, 85:30, 94:60, 95:90, 100:90, 101:50, 110:50. The column was allowed to equilibrate at start conditions for 30 min between analytical runs.

[0082] MS analysis was performed using an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific). Electrospray voltage (1.8 kV) was applied at a carbon composite union (Valeo Instruments) coupling a 360 pm o.d. x 20 pm i.d. fused silica extension from the LC gradient pump to the analytical column and the ion transfer tube was set at 250°C. Following a 25-min delay from the time of sample injection, Orbitrap precursor spectra (AGC 4E5) were collected from 350-1800 m / z for 110 min at a resolution of 60K along with data-dependent Orbitrap HCD MS / MS spectra (centroid) at a resolution of 50K (AGC 1E5) and max injection time of 105 ms for a total duty cycle of 2 s. Masses selected for MS / MS were isolated (quadrupole) at a width of 0.7 m / z and fragmented using a collision energy of 37%. Peptide mode was selected for monoisotopic precursor scan and charge state screening was enabled to reject unassigned 1+, 7+, 8+, and > 8+ ions with a dynamic exclusion time of 45 s to discriminate against previously analyzed ions between ± 10 ppm.

[0083] LC-MS / MS Analysis SPOTTER

[0084] Proteomic analysis was performed using tandem mass tag (TMT) labeling coupled with data- dependent acquisition (DDA) on an Orbitrap Ascend mass spectrometer (Thermo Fisher Scientific) interfaced with an Evoscp One liquid chromatography system (Evoscp). Peptide separation wasachieved using a 15 cm x 150 m inner diameter PepSep Cl 8 reversed-phase column (Bruker, Cat. #1893474) packed with 1.5 pm particles. Chromatographic separation employed the Evoscp One standardized 15 samples-per-day (SPD) method, utilizing an 88-minute gradient. The mobile phase consisted of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile).

[0085] Electrospray ionization was performed at 1.9 kV using a 150 pm outer diameter x 30 pm inner diameter stainless steel emitter coupled to the analytical column, with the ion transfer tube temperature maintained at 300°C to optimize ion desolvation. Full-scan MSI precursor spectra were acquired in profile mode on the Orbitrap Ascend at a resolution of 120,000, covering a mass range of 400-1400 m / z with an automatic gain control (AGC) target of 1.2 x 106 and an automatically optimized maximum injection time over the 88-minute gradient. Data-dependent acquisition triggered HCD- MS / MS spectra (collision energy: 35%; isolation window: 0.7 m / z) for the most intense precursors, with MS2 spectra collected in centroid mode at 30,000 resolution (AGC: 5 x 104 max injection time: 59 ms) within a 3-second duty cycle. Monoisotopic precursor selection (peptide mode) and charge state filtering excluded unassigned 1+, 7+, 8+, and >8+ ions, while dynamic exclusion (45 s; ±10 ppm mass tolerance) minimized redundant fragmentation of previously analyzed ions.

[0086] Database Search and Data Analysis-SPOT

[0087] All raw files were processed through MS-PyCloud that were converted into mzML and searched against Mus musculus (for mouse brain data) and Homo sapiens (for prostate cancer data) protein sequences downloaded from UniProt / Swiss-Prot via MS-GF+ using the following settings: fixed modification of carbamidomethyl at cysteine, dynamic modifications of oxidation at methionine and TMT at lysine and protein N-terminus, precursor mass tolerance of 20ppm, miss cleavages < 2, instrument ID of “High-res LTQ,” and fragmentation method of HCD. A false discovery rate of 1% at the PSM level, a minimum of 1 PSM per peptide, and a minimum of 1 peptide per protein were required.

[0088] Protein abundances were calculated by summing up the abundances of peptides belonging to the same protein. Median normalization was carried out for each TMT channel. Proteins with over 50% missing values were omitted. For differential analysis, median-normalized datasets were further log2 transformed. Pair-wise comparisons were conducted using the Wilcoxon ranked sum test, and proteins with a p-value less than 0.05 and an absolute fold-change greater than 2 were considered significantly differentially expressed.

[0089] Identified differentially expressed proteins from the prostate datasets were matched to the normal prostate proteome (126 genes enriched in prostate, Human Proteome Atlas), prostate cancerproteome (134 genes related to poor prostate cancer prognosis, Human Proteome Atlas), and cell markers in the prostate (199 genes, Cell Marker 2.0).

[0090] Database Search and Data Preprocessing -SPOTTER

[0091] Raw files were processed in Proteome Discoverer 3.0 (Thermo Fisher Scientific) using SEQUEST HT to search against the Mus musculus UniProt proteome (downloaded December 19, 2023), incorporating both standard trypsin cleavage (C-terminal to lysinc / argininc. K / RIX) and arginine- only cleavage (RIX) (to account for inaccessible lysine residues due to TMT blocking), with up to 2 missed cleavages permitted. Fixed modifications included carbamidomethylation of cysteine (+57.021 Da), while dynamic modifications comprised methionine oxidation (+15.995 Da), phosphorylation (+79.966 Da) on serine, tyrosine, and threonine residues (only applied during phosphorylation-related searches), and TMT 16 / 18-plex labeling (+304.207 Da) on lysine residues and peptide N-termini. Precursor and fragment mass tolerances were set to +20 ppm and +0.02 Da, respectively, with HCD fragmentation; identifications were filtered at 1% FDR (PSM, peptide, and protein levels) using Percolator, requiring >1 unique peptide per protein and >1 PSM per peptide. For quantification, protein abundances were derived by summing PSM reporter ion intensities, normalized per TMT channel via median scaling, and expressed as ratios relative to the 126-channel reference after normalization.

[0092] Bioinformatics and Data Analysis-SPOTTER

[0093] Global Proteomics Raw TMT intensities from four biological replicates were normalized by dividing each channel by the TMT126 reference channel and subsequently log2-transformed. To correct for inter-batch variation, ComBat batch correction (via the sva R package) was applied using replicate identifiers as the batch variable. Median normalization across all TMT channels was then performed to adjust for systematic intensity differences.

[0094] Proteins with more than 50% missing values were excluded, and the resulting batch-corrected data matrix was used for differential expression analysis using the limma linear modeling framework. A design matrix was defined based on region- specific TMT channel assignments, and contrasts were constructed to compare each brain region against the mean of all other regions. Empirical Bayes moderation was applied, and significance was assessed using Benjamini-Hochberg FDR correction. Proteins with an adjusted p-value < 0.05 and Ilog2 fold change (FC)I > 1 were considered significantly enriched. Volcano plots were generated for each region, and the top 10 most enriched proteins (based on absolute log2FC) were highlighted.

[0095] Phosphoproteomics Phosphopeptide intensities were processed similarly: values were first normalized to the TMT126 reference channel, log2-transformcd, and median-scaled across all TMTchannels. To retain positional information, phosphosites were mapped to full-length protein sequences using UniProt FASTA entries. Site-level identifiers (c.g., splP12345IGENE@S217) were constructed based on peptide localization and used for downstream tracking.

[0096] Following filtering for missing values (sites present in at least 50% of samples), batch correction was applied using ComBat. Differential phosphosite analysis was performed using the same limma-based linear modeling framework as described above. Volcano plots were generated for each brain region, highlighting significantly enriched phosphosites (adjusted p-value < 0.05, llog2FCI > 1). The top 10 most enriched phosphosites were labeled in each plot to aid biological interpretation.Example 1

[0097] Design of SPOT

[0098] SPOT is designed to provide quantitative deep profiling of spatially-resolved proteomics by direct TMT labeling tissue proteins on slides (Figure 1). Isobaric labeling with TMT is used as a proof- of-principle study, TMT is a well-established robust system for multiplex, relative protein quantitation of up to 18 samples. TMT binds to primary amines (N-terminal and epsilon amino group of lysine residues) in proteins / peptides using NHS chemistry. Particularly, in protein labeling, TMT can be conjugated to accessible lysine residues and the protein N-terminus. SPOT utilizes TMT for the controlled labeling of proteins spatially distributed on a 2D-tissue slide. Subsequently, the entire tissue section would be harvested and subject to standard proteomic analysis workflow.Example 2

[0099] TMT labeling at the protein-level on different tissue slides

[0100] The efficacy of direct TMT labeling at the protein-level was evaluated across various slide types, including frozen tissues without staining, FFPE without staining, deparaffinized FFPE, deparaffinized and decrosslinked FFPE, and tissues with H&E staining, H staining, E staining. A mixture of 18 TMT tags was directly applied to the tissue sections, a pipette was used in this study as the initial applicator for tissue labeling. Subsequently, the entire tissue slide was scraped off, lysed, digested, and cleaned up using SCX followed by C18 STAGE tips and MS analysis. Upon completion of MS data generation, the raw data of each slide was searched and evaluated based on the identifications at PSM, peptide, and protein levels.

[0101] In general, tissue proteins on various types of slides could be labeled with TMT, with varying degrees of labeled protein percentages. Across all types of tissue slides, only 10% or less of the total PSMs were identified to have TMT tags at the protein N-termini. Consistency could be observed for the identifications at PSM, peptide, and protein levels between the two repeats of the same tissue slide type.Frozen sagittal mouse brain slides served as a reference for assessing labeling efficiency, given that minimal treatment was applied to frozen slides. In comparison to frozen slides, where TMT labels were found on over 92% of proteins, a visible decrease in the percentage of labeled proteins could be observed in untreated, deparaffinized, and deparaffinized / de-crosslinked slides (Figure 2A). Remarkably, the labeling of proteins (-64%) is minimally affected by the presence of paraffin compared to the deparaffinized ones (-70%), supposedly attributable to the permeability of acetonitrile through the paraffin. Paraffin-coated surface also has low surface energy (high contact angle over 100°), which limits the lateral spreading of the TMT solution. An adept control of TMT dot size is critical in ensuring precise and reproducible labeling efficiency in more precise settings such as labeling on tissue microarray (TMA) slides (coring size - 0.6 mm). In addition, the decrosslinking step also improved the labeling efficiency by -8%.

[0102] Next, the effects of histology staining on direct tissue protein labeling were also evaluated (Figure 2B). Notably, TMT tags were identified on over 92% of proteins from H-stained tissue slides. H&E stained (88%) and E-stained (84%) tissue slides showed a slightly lower percentage of labeled proteins compared to frozen and H-stained, however, the difference was less than 10%. During the H&E staining procedure, haemalum (oxidized hematoxylin solution) attaches to cell nuclei through covalent bonds between DNA phosphate oxygens and aluminum atoms, as well as between aluminum atoms and haemalum molecules. This covalent interaction between DNA and haemalum might release certain proteins bound to DNA, potentially elucidating the observed increase in protein identification with H- staining only. In contrast, eosin is attracted to tissue proteins by ionic forces (van der Waals forces), and it could form salts with basic compounds like proteins. In turn, the presence of eosin could take up some of the binding capacity of SCX and C18 materials due to the hydrophobic interactions, providing a plausible explanation for the observed decrease in labeled protein percentage in H&E and E-stained slides.Example 3

[0103] On-site labeling of proteins from different brain regions on mouse brain slide

[0104] Following the successful validation of direct labeling of tissue proteins on slides, SPOT's ability to detect proteomic patterns within a spatial context was further evaluated. A horizontal mouse brain slide with eight different regions was clearly outlined and each region was “stained” with a different TMT tag as illustrated in Figure 3A. To enhance the visual recognition of the brain regions, the horizontal mouse brain slide was first stained with hematoxylin only, since hematoxylin did not interfere with the direct TMT labeling of tissue proteins (Figure 2).

[0105] The mouse brain tissue was prepared similarly for downstream quantitative proteomics evaluation. Each region displayed a distinctive protein expression pattern and eight protein clusters were established using a soft clustering algorithm (Figure 3B). Among the eight clusters, cluster 4 (C4) and cluster 6 (C6) had obvious upward and downward protein expression trends starting from the neocortex to the cerebellum, respectively (Figures 3B). Excitatory amino acid transporter 1 (Eaal), a glutamate transporter localized in the brain, was identified from C4 with the highest abundance in the cerebellum compared to the other regions, correlated well with a previous study showing that Eaal was highly enriched in the Purkinje cell layer in cerebellum. On the other hand, elevated protein expression of V- type proton ATPase subunit al (Vppl) in the neocortex was observed in C6. Vppl is reported to be predominantly expressed in neurons in the cortex and the dentate gyrus, part of the hippocampus. It can be found at low levels in astrocytes, oligodendrocytes, and microglia.

[0106] The results demonstrate that SPOT effectively detected proteomic patterns directly from tissue protein labeling indicating that SPOT is useful in studying spatial proteomics.Example 4

[0107] On-site labeling of different Gleason score regions on the frozen slide and TMA slide

[0108] Prostate Cancer Frozen Slide

[0109] To further test the on-site labeling on frozen tissue slides in discerning smaller regions of interest, an experienced pathologist annotated 4 regions of 0.6 mm in diameter within normal sections, Gleason score 3 sections, Gleason score 4 sections, and Gleason score 5 sections, respectively (Figures 4A). Based on the pathological annotations on the adjacent H&E slides, direct TMT labeling was carried out on the frozen slides.

[0110] In total, 11,214 peptides were identified, corresponding to a set of 1,854 unique proteins. Within this dataset, 1,365 peptides were successfully labeled with TMT tags, corresponding to 289 distinct proteins. Following this identification, hierarchical clustering and principal component analysis (PCA) and hierarchical clustering were conducted to examine the association among different Gleason score regions based on their protein expression profiles (Figure 4A and 4B). Notably, normal regions and Gleason4 regions could be separated completely, whereas Gleason 3 and Gleason 5 regions had a considerable overlap. Regions characterized by normal or the same Gleason score (ranging from Gleason 3 to 5) displayed notably high correlations across diverse tissue sections. Conversely, regions associated with different Gleason scores exhibited relatively lower degrees of correlation.

[0111] Furthermore, differential analysis (Figure 4D) was able to return two proteins specifically enriched in the prostate tissue (Human Protcomc Atlas), two proteins that were found to relate to poorprognosis of prostate cancer (Human Proteome Atlas), and three proteins related to cell markers in the prostate (Cell Marker 2.0). Previous studies have indicated notable clinical relevance associated with microseminoprotein-beta (MSMB) and epithelial cell adhesion molecule (EPCAM) for prostate cancer. In this study, MSMB was found to be overexpressed in Gleason 3 regions relative to normal regions, while EPCAM was found to be elevated in both Gleason 3 and Gleason 5 regions, but higher fold change was observed in Gleason 3 compared to normal (log2 fold change =1.75) than Gleason 5 compared to Gleason 4 (log2 fold change = 1.08) (Figure 4D). MSMB and EPCAM could be prostate cancer-relevant indicators or contributors in various medical and pathological conditions, underscoring the importance of further exploration and validation.

[0112] In summary, these results indicate that SPOT could capture potential correlations and variations in molecular profiles across different Gleason scores from frozen tissue slides even with direct TMT labeling of proteins in the 0.6 mm region.

[0113] Prostate Cancer TMA Slide

[0114] Following the application of TMT direct labeling onto frozen tissue slides derived from prostate cancer specimens, there arises a distinct interest in evaluating the translatability and consistency of this labeling methodology when extended to TMA slides. TMA cores were meticulously assessed by an experienced pathologist who assigned distinct scores to each core, based on the H&E stained adjacent slide (Figure 5A). Eighteen cores of the size 0.6 mm were selected for TMT direct labeling (three for normal, five for Gleason score 3, five for Gleason score 4, and five for Gleason score 5).

[0115] The TMA format involves the systematic arrangement of discrete tissue cores, evenly spaced across the slide, providing a representative sampling of various specimens. Importantly, the deliberate spacing of these cores minimizes the risk of label mixing between different regions, ensuring a more accurate and region- specific evaluation of the TMT labeling method within the TMA framework. This comparative analysis aims to contribute valuable insights into the method's adaptability and reliability across different tissue slide formats, advancing our understanding of its applicability in broader histological contexts.

[0116] In total, 1,873 peptides (corresponding to 560 unique proteins) were identified, out of which 790 were TMT-labeled peptide sequences. These labeled peptides corresponded to 265 distinct proteins. Principal component analysis revealed minimal to no overlaps between each group (Figure 5B), indicating distinct clustering patterns. The subsequent hierarchical clustering analysis (Figure 5C) provided additional insight, revealing varying degrees of mixing between each group. This observationimplies nuanced relationships and molecular heterogeneity within the regions characterized by different Gleason patterns.

[0117] Furthermore, the TMA dataset identified four proteins (PTMA, PPAP, POSTN, AGR2) that exhibited potential in distinguishing different Gleason score regions (Gleason score 3, 4, and 5). Additionally, one cell marker protein (MYH11) demonstrated the capability to differentiate normal regions from different Gleason score regions (Figure 5C), a finding consistent with observations in the frozen dataset. These proteins identified in the TMA dataset, particularly the four proteins demonstrating variations among different Gleason score regions, suggest their potential for clinical applications in prostate cancer detection. Their implications in prostate cancer, as reported in previous studies, further underscore the significance of these proteins in the context of prostate cancer pathology. Understanding the molecular basis of Gleason patterns through these proteins could enhance the precision of prostate cancer grading. In addition, the cell marker protein capable of distinguishing normal from cancerous regions holds diagnostic potential and may serve as a valuable tool for clinicians in accurately identifying cancerous areas within the prostate.

[0118] Based on the correlation analysis result, a pronounced association could be observed for normal cores as well as Gleason score 3 cores. This strong correlation implies a nuanced overlap in molecular characteristics in normal and low-grade prostate cancer. In contrast, the correlations observed among Gleason score 4 and Gleason score 5 cores exhibit greater variability, suggesting potential differences in tumor heterogeneity. The varied correlations observed in the higher-grade cores suggest the intricate nature of prostate tumor heterogeneity. This complexity arises from differences in the types and arrangements of cells, which can influence unique molecular characteristics within the tumor.Example 5

[0119] Design of SPOTTER

[0120] SPOTTER, an automated tissue-barcoding platform tailored for both spatial proteomics and phosphoproteomics. SPOTTER leverages a customizable robotic system that performs micron-scale spatial barcoding directly on intact tissue sections through the programmable deposition of TMT reagents. This automated process bypasses the dependency on antibodies or RNA probes, thereby eliminating one of the major bottlenecks in current spatial profiling methodologies. By encoding the spatial origins of proteins across an entire tissue section, SPOTTER not only enhances reproducibility and throughput but also facilitates the deep mapping of proteomic landscapes without compromising histological integrity.

[0121] SPOTTER integrates two functionally synchronized subsystems: a high-pressure nanoflow pump system for precision reagent delivery and a 3D printing platform for spatially resolved deposition (Figure 7A). The pump module employs an Easy nLC 1200 (Thermo Fisher Scientific), which combines a 48-vial autosampler with temperature-controlled (4°C) storage, dual pressure-resistant pumps (1,200 bar maximum operating pressure; 100-1,000 nL / min flow range), and programmable gradient control. The printing module was engineered by retrofitting an Original PRUSA MK4 3D printer, wherein the thermoplastic extrusion assembly was replaced with a liquid-handling system (Figure 11). Modifications included disabling the print head’s heating element and embedding a fused silica nano-flow liquid chromatography column (75 / 30 / 10 pm inner diameter, 100 mm length; CoAnn Technologies) as a microfluidic deposition nozzle. The printer’s native motion control system, featuring precise XY stepper motors (0.9° step angle, 1 / 16 microstepping) and polished steel rod linear bearings, was utilized without modification. The two subsystems were connected with a stainless steel mixing tee (150 pm inner diameter; Thermo Fisher Scientific).

[0122] G-code scripts were developed to precisely control the spatial deposition of labeling reagents onto tissue sections, ensuring alignment with predefined printing patterns. To minimize crosscontamination between labeling reagents, the printing nozzle was flushed with 25% v / v acetonitrile after each deposition cycle. Each designated pattern was printed five consecutive times to ensure efficient labeling efficiency, with a 3-minute drying phase at ambient temperature incorporated between cycles to facilitate solvent evaporation. Following TMT labeling and subsequent quenching, the entire labeled tissue was carefully lifted from the slide using a scalpel and subjected to lysis in 8M urea, followed by tryptic digestion to generate peptides.

[0123] SPOTTER’S printing performance was accessed by depositing a 10 x 10 grid pattern under consistent printing parameters using a fluorescent solution (1% rhodamine B in 25% v / v acetonitrile). Nozzles with varying inner diameters (i.d.: 75 pm / 30 pm / 10 pm) and flow rates (1 pE / min, 0.2 pE / min, 0.1 pL / min) were tested to evaluate dot deposition uniformity and reproducibility. Postprinting, slides were imaged with a GenePix 4000B Microarray Scanner (Molecular Devices) to capture fluorescence signals. Acquired images were analyzed using Fiji / ImageJ (NIH) with the “Analyze Particles” tool to quantify morphological features of the printed dots, including their area, circularity, and diameter.

[0124] A 75 pm-i.d. nozzle at 1 pL / min yielded printed dots with a median diameter of - 888 pm and high reproducibility (coefficient of variation, CV = 2.0%; Figure 7B). Switching to a 30 pm-i.d. nozzle at the same flow rate reduced the median dot size to - 632 pm while maintaining low variability (CV =2.6%). To further minimize the diameter of the printed dots, a 10 pm-i.d. nozzle was tested: at 0.2 pL / min, the median dot diameter decreased to 362 pm (CV = 2.1%), achieving a 59% reduction compared to the 30 pm nozzle. When the flow rate was further reduced to 0.1 pL / min using the 10 pm nozzle, the median dot size decreased to 169 pm, but variability increased significantly (CV = 10.6%), suggesting instability in gravity-induced droplet formation at lower flow rates.Example 6

[0125] Quantitative Mapping of Over 2300 Proteins on Murine Brain Using SPOTTER

[0126] To establish SPOTTER’S capacity for sub-tissue spatial proteomics in complex mammalian tissues, four 7 pm-thick horizontal murine brain sections were selected as a model system offering well- defined anatomical segmentation of brain regions. The sections were first imaged and their regions were annotated referencing the Allen Brain Atlas. Next, region-specific printing patterns tailored to the anatomical boundaries were generated, deploying a 75 pm-i.d. nozzle (1 pL / min flow rate) to accommodate the scale of individual brain regions. After performing efficient labeling and quenching of the designated regions, the entire section was lifted off the slide, lysed with 8M urea lysis buffer, and processed for proteomic analysis. In addition, to address potential interference from unlabeled peptides, an additional post-labeling step was performed at the peptide level, involving the usage of another TMT channel that is not used during the on- slide printing process. A small aliquot (5% of total peptides) was reserved for immediate LC-MS / MS analysis to generate proteomic data. The remaining peptide pool was conditioned to enrich for phosphopeptides using immobilized metal affinity chromatography (IMAC) for phosphoproteomic profiling (Figure 8A). SPOTTER successfully resolved ten distinct brain regions, capturing layer- specific protein gradients across spatially segregated domains, highlighting the ability to uncover region- specific proteomic heterogeneity at sub-tissue resolution.

[0127] To map the quantitative proteomic landscape of the brain and identify region-specific protein markers critical for understanding functional specialization, the method focused on ten brain regions: cerebellum, midbrain, thalamus, medial habenula, hippocampus, dentate gyrus, basic cell groups and regions (grey), lateral septal nucleus, caudate putamen and neocortex (Figure 8B). Region- specific proteomic profiling of the ten brain regions was followed by comparative analysis of protein expression patterns. From the horizontal mouse brain sections, a total of 16,019 TMT-labeled peptides were identified, corresponding to 2,337 proteins (Figure 8D). Proteomic divergence among regions was assessed using principal component analysis (PCA) (Figure 8C), which revealed strong intra-replicate consistency across the four biological repeats and clear separation of all ten brain regions in PCA space.

[0128] Recognizing the distinct proteomic signatures across brain regions, differential expression analysis was conducted to identify rcgion-cnrichcd proteins across four biological replicates. To resolve region- specific protein enrichment, volcano plots were generated for each brain region (Figure 9A): protein intensities were normalized to the TMT 126 reference channel, log2-transformed, and subjected to median scaling across channels. To correct for batch effects across replicates, ComBat was applied, yielding a batch-corrected protein abundance matrix. For each brain region, a linear modeling approach using the Umma package was performed to compare protein abundance in that region against the median of all other regions. Volcano plots were generated to visualize region- specific enrichment, with proteins ranked by log2 fold change (log2FC) and statistical significance (-log 10 adjusted p-value). The top ten most significantly enriched proteins per region were highlighted, representing reproducible spatial markers derived from the SPOTTER workflow.Example 7

[0129] Spatially Resolved Phosphoproteomics Unveils Regional Signaling Complexity in the Brain

[0130] Spatial proteomic profiling of intact tissues has long been hindered by a combination of technical limitations: the limited multiplexing capacity of antibody- or probe-based methods, which struggle to map thousands of proteins across a tissue section due to reagent availability and signal overlap, and the low throughput of microdissection-based approaches, which fragments tissue into analyzable microsamples. These constraints have left researchers forced to choose between either mapping a handful of proteins with spatial precision or analyzing global proteomes at the expense of tissue architecture. SPOTTER bridges this divide through an innovative whole-tissue molecular tagging strategy. By embedding spatially encoded “zip codes” into proteins during intact tissue processing, SPOTTER eliminates reliance on antibodies for multiplexing. This approach bypasses both tissue disruption (avoiding microdissection) and antibody limitations, enabling unbiased, high-plex protein mapping across entire tissue sections. By preserving spatial context while retaining full tissue volume, SPOTTER dramatically increases the sample input available for downstream analysis. This enhanced input allows robust enrichment of low-abundance, modified protein subgroups, such as phosphorylated species, that are often lost in fragmented samples. The result is unprecedented depth in detecting post- translational modifications (PTMs) alongside precise spatial localization, adding functional and regulatory dimensions to proteomic maps.

[0131] Applying SPOTTER to murine brain tissues, 1,714 phosphopeptides were identified from 4 experimental replicates, which can be mapped to 686 phosphoprotcins. 386 out of 686 phosphoprotcinscan be identified in the global-level replicates’ results. Figure 12 is a Venn diagram of the identification results of the phosphopcptidcs across the 4 experimental replicates. Strikingly, phosphorylation varied dramatically across brain regions, even in the same phosphoprotein, underscoring how microenvironmental cues shape signaling dynamics.

[0132] Together, these results demonstrate how SPOTTER bridges the gap between spatial biology and phosphoproteomics. By resolving posttranslational modification dynamics across tissue architectures, SPOTTER opens new avenues for exploring how signaling networks adapt to anatomical and functional niches.

[0133] It is understood that the foregoing detailed description and accompanying examples are merely illustrative and are not to be taken as limitations upon the scope of the invention, which is defined solely by the appended claims and their equivalents.

[0134] Various changes and modifications to the disclosed embodiments will be apparent to those skilled in the art. Such changes and modifications, including without limitation those relating to the chemical structures, substituents, derivatives, intermediates, syntheses, compositions, formulations, or methods of use of the invention, may be made without departing from the spirit and scope thereof.

Claims

CLAIMSWhat is claimed is:

1. A method for spatial proteomics, comprising: labeling proteins at one or more target locations within a cell or tissue sample with one or more mass tags; and determining the identity and / or abundance of any or all of the proteins in the one or more target locations by mass spectrometry analysis.

2. The method of claim 1, wherein the method does not comprise isolating any one or all of the one or more target locations.

3. The method of claim 1 or 2, further comprising comparing the identity and / or abundance of any or all of the proteins between at least two or more target locations.

4. The method of any of claims 1-3, wherein the labeling is manual or automated.

5. The method of any one of claims 1-4, wherein the one or more target locations are determined by annotating the cell or tissue sample by cell type, histological patterns, and / or pathological states.

6. The method of any of claims 1-5, further comprising staining the cell or tissue sample with one or more histological or pathological labeling agents.

7. The method of any of claims 1-6, wherein the one or more target locations comprise one or more cell types, one or more cellular structures, one or more sections of a tissue sample, one or more sites of disease, or a combination thereof.

8. The method of any of claims 1-7, wherein each of the one or more target locations is labeled with a different mass tag or representative polypeptides generated by different proteolysis.

9. The method of any of claims 1-8, wherein each of the one or more mass tags or representative polypeptides from the proteins in different cells or tissue areas are distinguishable from each other by mass spectroscopy.

10. The method of any of claims 1-9, further comprising quenching the labeling step.

11. The method of any of claims 1-10, further comprising lysing and proteolysis of the cell or tissue sample.

12. The method of any of claims 1 -1 1 , further comprising separating the proteins electrophoretically or chromatographically prior to mass spectrometry analysis.

13. The method of any of claims 1-12, wherein the tissue sample is a fresh tissue sample, a frozen tissue sample, of a fixed or processed tissue sample.

14. The method of any of claims 1-13, wherein the tissue sample is a tissue slice or section or a tissue microarray.

15. The method of any of claims 1-14, wherein the tissue sample comprises sequential slices or sections of a tissue.

16. The method of claim 15, further comprising assembling a three-dimensional spatial proteomic analysis of the tissue.

17. The method of any of claims 1-16, wherein the tissue sample is a mammalian tissue sample or a lant tissue sample.

18. The method of any of claims 1-17, wherein the tissue sample is a diseased tissue sample.

19. The method of claim 18, wherein the diseased tissue sample is a cancerous tissue sample.

20. The method of claim 18 or 19, further comprising comparing altered levels of protein abundance, rotein modifications, or protein complexes in the diseased tissue sample to a healthy or non-diseased sample.

21. The method of any of claims 1-20, wherein the mass tags are deposited onto a substrate prior to applying the tissue section, thereby enabling reverse transfer of labels to the tissue.

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