A method for constructing dynamic proteome interaction networks

Abstract

This invention belongs to the field of biological detection, specifically relating to a method for constructing a dynamic proteome interaction network, and more specifically, to a method for constructing a dynamic proteome interaction network of secretory proteins and membrane proteins. Using the method of this invention, a dynamic intercellular interaction network encompassing time, space, and activation can be constructed, thereby revealing the characteristics and dynamic changes of protein-mediated intercellular interactions in the microenvironment. This provides an important foundation for further research on diseases involved in the samples, such as tumor development, treatment strategies, and prognosis.

Description

Technical Field

[0001] This invention belongs to the field of biological detection, specifically, it relates to a method for constructing a dynamic proteome interaction network, and more specifically, it relates to a method for constructing a dynamic proteome interaction network of secretory proteins and membrane proteins. Background Technology

[0002] The tumor microenvironment (TME) is a key hallmark of cancer. Atypically rich in non-malignant stromal cells and extensive in the extracellular matrix (ECM), the TME can promote cancer proliferation, metastasis, and drug resistance. Many therapeutic strategies targeting the dense matrix have been tested. Researchers are increasingly dedicated to exploring the diversity of the TME and its interactions with cancer cells. However, current functional proteomics studies are conducted on cell line-based systems and may not be able to reproduce the intercellular signal transduction events under physiological conditions in cancer.

[0003] Meanwhile, it is now known that genetic alterations alone are insufficient to explain the occurrence and development of diseases. Recent reports have documented comprehensive proteomics, phosphorylated proteomics, glycoproteomics, and genomic characterization of many cancers, such as pancreatic ductal adenocarcinoma (PDAC), revealing the impact of genomic alterations on protein expression and post-translational modifications (PTMs) and characterizing molecular subtypes. A common limitation of these studies is that holistic analyses based on the entire tissue cannot fully elucidate the effects of cellular complexity and tumor heterogeneity.

[0004] Therefore, there is a need in the art for a method that can be used to elucidate the influence between cellular complexity and tumor heterogeneity in order to discover more targets for cancer-related diagnosis and treatment. Summary of the Invention

[0005] In view of this, the applicant has creatively constructed a proteome dynamic interaction network method, which can clarify the intercellular temporal, spatial, and activation dynamic interaction network mediated by secretory proteins and membrane proteins in the tumor microenvironment.

[0006] In a first aspect, the present invention provides a method for constructing a dynamic interaction network of proteomes, comprising the following steps:

[0007] S1. Enrich the proteins in the test samples and control samples, and analyze them to obtain proteins that show significant differences between the test samples and control samples.

[0008] S2. Separate the test samples to obtain different types of cell samples, analyze them, obtain proteins that are significantly different between different cell types, identify ligand-receptor signal pairs between different cell types, and construct a ligand-receptor interaction database.

[0009] S3. Select test samples from different stages and repeat steps S1 and S2 to obtain the expression profiles of the ligand-receptor signal pair at different stages.

[0010] S4. Perform protein phosphorylation analysis on the test samples to obtain the activation status of ligand-receptor signal pairs in different cell types; and

[0011] S5. Based on the information obtained in steps S1 to S4, construct a protein-mediated dynamic interaction network in the test sample.

[0012] Furthermore, the protein may be a secretory protein or a membrane protein of the sample.

[0013] Furthermore, the test sample and control sample can be a lesion sample and a non-lesion sample. Even further, the lesion sample can be a tumor sample, and the non-lesion sample can be a normal sample. In some specific embodiments, the lesion sample can be a pancreatic cancer sample. In some specific embodiments, the lesion sample can be derived from a pancreatic cancer transgenic mouse model.

[0014] Further, in step S1, the enrichment is performed using an acylhydrazine probe. Further, the acylhydrazine probe carries a biotin group. Even further, the acylhydrazine probe can be modified to have higher solubility.

[0015] In some specific embodiments, the structural formula of the acylhydrazine probe is shown below:

[0016]

[0017] Further, in step S2, the different types of cell samples can be diseased cell samples and stromal cell samples. Further, the different types of cell samples can be cancer cell samples and stromal cell samples.

[0018] Furthermore, in step S2, the separation can be performed using a laser micro-cutting method.

[0019] The separation can also be performed by flow cytometry or by magnetic bead sorting. Specifically, fresh tissue is broken down and digested into single cells, and then specific cell types are separated by cell sorting methods such as flow cytometry and magnetic bead sorting.

[0020] In some specific implementations, in step S2, the ligand-receptor signal pair is determined using a protein interaction database.

[0021] Furthermore, in step S2, the ligand-receptor signal pair may be a paracrine ligand-receptor signal pair.

[0022] Furthermore, in step S2, the ligand-receptor signal pair can also be a ligand-receptor-downstream protein signal axis.

[0023] Furthermore, in steps S1 and S2, the proteins exhibiting significant differences refer to proteins whose expression fold is 2-fold or greater.

[0024] Furthermore, in step S3, the test samples at different stages can be samples that are classified into stages according to conventional methods, such as samples differentiated according to different stages of disease progression. For example, when the test sample is a tumor sample, it can be divided into early, middle, and late stages according to the tumor stage, or it can be divided into stage I, stage II, stage III, and stage IV.

[0025] Furthermore, in step S3, the test samples at different stages can be samples at different developmental stages.

[0026] Further, in step S4, the protein phosphorylation analysis can be tyrosine phosphorylation analysis, serine phosphorylation analysis, or threonine phosphorylation analysis. Preferably, the protein phosphorylation analysis is tyrosine phosphorylation analysis.

[0027] In some specific implementations, the protein phosphorylation analysis may be tyrosine phosphorylated protein analysis. Furthermore, the tyrosine phosphorylated protein analysis uses a ternary probe-based Photo-pTyr-Scaffold method to enrich phosphorylated tyrosine-interacting complexes, and uses SH2 hyperphilic enrichment of tyrosine phosphorylated peptides for site identification.

[0028] Further, in step S5, the protein-mediated dynamic interaction network can be a protein-mediated intercellular temporal, spatial, and activation dynamic interaction network. Specifically, step S1 acquires the dynamic changes of the test sample compared to the control sample. For example, when the test sample is a tumor, the dynamic changes of the tumor compared to other tissues (such as normal tissue, adjacent tissue, and inflamed tissue) are acquired. For example, when the test sample is a pancreatic tumor, the dynamic changes of the pancreatic tumor compared to other tissues (such as normal tissue, adjacent tissue, and pancreatitis tissue) are acquired. Step S2 acquires the spatial dynamics, step S3 acquires the temporal dynamics, and step S4 acquires the activation dynamics.

[0029] Furthermore, it could be a network of intercellular temporal, spatial, and activation dynamic interactions mediated by secretory proteins and membrane proteins.

[0030] Using the method of this invention, a network of intercellular temporal, spatial, and activation-dynamic interactions can be constructed, thereby revealing the characteristics and dynamic changes of protein-mediated intercellular interactions in the microenvironment, providing an important foundation for further research on diseases involved in the samples, such as tumor development, treatment strategies, and prognosis.

[0031] Secondly, the present invention provides a dynamic proteome interaction network model, which is constructed according to any of the above methods.

[0032] Furthermore, the proteome is a secretory protein and membrane protein group.

[0033] Furthermore, the model is a network model of intercellular temporal, spatial, and activation dynamic interactions mediated by secretory proteins and membrane proteins.

[0034] Thirdly, the present invention provides the application of the construction method or model described above in the preparation of a kit or apparatus for constructing a dynamic proteome interaction network model.

[0035] Fourthly, the present invention provides the application of the model described above in identifying biomarkers or screening drugs.

[0036] Fifthly, the present invention provides an apparatus for constructing a dynamic proteome interaction network model, comprising:

[0037] S1, the first analysis module, is used to enrich and analyze the proteins in the test sample and the control sample to obtain proteins that show significant differences between the test sample and the control sample.

[0038] S2, the second analysis module, separates the test samples to obtain different types of cell samples, analyzes them, obtains proteins that are significantly different between different types of cells, identifies ligand-receptor signal pairs between different types of cells, and constructs a ligand-receptor interaction database;

[0039] S3, the third analysis module, selects test samples from different stages and repeats steps S1 and S2 to obtain the expression profiles of the ligand-receptor signal pair at different stages;

[0040] S4, the fourth analysis module, performs protein phosphorylation analysis on the test samples to obtain the activation status of ligand-receptor signal pairs in different types of cell samples; and

[0041] S5, Construction Module, is used to construct a protein-mediated dynamic interaction network in the test sample based on the information obtained in steps S1 to S4.

[0042] Sixthly, the present invention provides an apparatus comprising:

[0043] At least one processor; and

[0044] A memory communicatively connected to at least one of the processors; wherein,

[0045] The memory stores instructions that can be executed by the processor to implement the method for constructing dynamic interaction networks of proteomics as described above.

[0046] In some embodiments, the device further includes at least one input device and at least one output device; in the device, the processor, memory, input device, and output device are connected via a bus.

[0047] In a seventh aspect, a storage medium is provided that stores computer instructions for execution by the computer to implement the method for constructing dynamic interaction networks of proteomics as described in any of the preceding claims.

[0048] In some implementations, the storage medium is a computer-readable storage medium. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for constructing an S-PM protein-mediated intercellular temporal, spatial, and activation dynamic interaction network in the pancreatic cancer tumor microenvironment according to an embodiment of this application.

[0050] Figure 2 (a) Synthesized long-chain biotinylated probe; (b) Biotinylated probe used to label and enrich glycoproteins in mouse pancreatic tissue lysate; (c) Comparison of probe-based methods and conventional hydrylated microsphere-based methods; (d) Comparison of glycopeptides and non-glycopeptides at qualitative and quantitative levels; (e) Percentage of S-PM proteins and non-specifically reacting proteins among all enriched proteins; (f) Percentage of non-specifically adsorbed proteins; (g, h) Comparison of enrichment and non-enrichment at qualitative and quantitative levels; (i, j) Correlation between enrichment and non-enrichment for S-PM protein quantification.

[0051] Figure 3 The study included: (a) a pancreatic tissue cohort and S-PM protein analysis workflow; (b) the percentage of glycopeptide-based S-PM proteins among all identified proteins; (c) a comparison of glycopeptide and non-glycopeptide sequence coverage of S-PM proteins; (d) the number of S-PM proteins identified in 100 tissue samples; (e) annotation of the intercellular signal transduction function of S-PM proteins; (f) heatmap analysis of significantly differentially expressed proteins and the biomarkers and drug targets covered; and (g) GOMF analysis of differentially expressed proteins.

[0052] Figure 4The analysis includes (a) the spatial and cell-type proteomic analysis workflow; (b) the number of S-PM proteins identified by spatial and cell-type proteomic analysis; (c) the number of PCC and matrix-specific proteins; (d) cellular localization annotation of S-PM proteins identified in whole tumor tissue by spatial and cell-type proteomic analysis; (e) ligand-receptor pair analysis of S-PM proteins identified in tumor tissue; and (f) autocrine and paracrine ligand-receptor pairs between PCC and stromal cells.

[0053] Figure 5 The following are the technical procedures for (a) time-expression profiling analysis; (b) the number of S-PM proteins and glycosylation sites identified in mouse pancreatic tissue; (c) heatmaps of significantly differentially expressed S-PM proteins; (d) expression trends of significantly overlapping S-PM proteins between humans and mice; (e) cluster analysis of significantly S-PM proteins with consistent trends in humans and mice; (f) identification of reported tumor biomarkers in the three clusters; (g) the top five GOMFs in the three clusters; and (h) correlation analysis of paracrine signals with tumor progression.

[0054] Figure 6 The analysis includes: (a) the workflow for tyrosine phosphorylation proteomics; (b) the number of pTyr complexes and sites identified in normal and tumor tissue samples; (c) the expression trends of pTyr writers, readers, and erasers in normal and tumor tissues; (d) four activated ligand-receptor-downstream signaling axes, where both receptors and downstream proteins showed significant variations in the pTyr complex dataset; (e) spatial and temporal resolution annotations of 148 selected receptor-ligand pairs; and (f) paracrine signaling pairs from PCCs to the matrix and from the matrix to PCCs, as well as downstream activated signaling axes. Detailed Implementation

[0055] The present invention will be described in detail below with reference to specific implementation schemes and embodiments, thereby making the advantages and various effects of the present invention more clearly apparent. Those skilled in the art should understand that these specific implementation schemes and embodiments are for illustrative purposes only and are not intended to limit the present invention.

[0056] The construction method of this invention can be used to construct networks for any two different sample types. For example, a dynamic network can be constructed for lesion samples and normal samples. More specifically, a dynamic network can be constructed for tumor samples and normal samples. In this invention, a pancreatic cancer tumor sample is used for exemplary operation, and the exemplary steps are as follows: Figure 1 As shown.

[0057] In this invention, terms such as "first," "second," "third," and "fourth" are used only to distinguish different matters and are not intended to limit the order in any way.

[0058] In this invention, S-PM proteins refer to secreted (S) proteins and plasma membrane (PM) proteins. These proteins are mainly located on the cell membrane surface and extracellular matrix, directly interacting with the extracellular environment and playing important functions. S-PM proteins play a crucial role in biological processes such as cell-cell interactions, signal transduction, cell adhesion, and cell recognition. They can act as signal ligands and receptors, cell adhesion molecules, and cell recognition molecules, regulating cell function and interactions. By enriching and analyzing S-PM proteins, we can gain a deeper understanding of the protein composition and regulatory mechanisms on the cell surface in different tissues or disease states. For example, in pancreatic cancer research, enriching and analyzing S-PM proteins can help identify proteins that show significant differences between pancreatic cancer and normal pancreatic tissues, further revealing the molecular mechanisms of pancreatic cancer development and changes in cell-cell interactions.

[0059] For ease of understanding, the terms used in this invention generally have the following interpretations:

[0060] Sample collection: Collect test tissue samples and control tissue samples (which may be normal tissue) to ensure that the sample sources are representative.

[0061] Tissue lysis: The collected tissue samples are lysed to extract tissue proteins.

[0062] Enrichment of S-PM proteins: Using appropriate techniques, proteins on the cell surface and in the extracellular matrix are separated from other cellular components.

[0063] Proteomic analysis: Mass spectrometry is a commonly used method for proteomic analysis of the enriched S-PM proteins. This method can identify and quantify proteins in different samples. By comparing the protein composition of different samples, proteins with significant differences can be identified. For example, by comparing the S-PM protein composition between pancreatic cancer samples and control samples, proteins with significant differences can be identified.

[0064] Data Analysis and Interpretation: By comparing the results of proteomic analysis, proteins that showed significant changes in the test samples compared to the control samples were identified. These proteins may be related to disease development and intercellular interactions, providing potential targets and a basis for further research.

[0065] Generally, S-PM proteins are typically found in low abundance, but represent the majority of N-linked glycosylated proteins in the human proteome. To improve the effectiveness of the technical solution presented in this application, it is necessary to obtain as many S-PM proteins as possible. Therefore, the inventors propose a hydrazide chemistry strategy for the selective enrichment and comprehensive analysis of the glycosylated S-PM proteome. The hydrazide chemistry strategy for enriching S-PM proteins is based on the specific reaction between hydrazides and aldehydes. By introducing the hydrazide functional group onto the aldehyde group of the protein surface glycan, selective enrichment of S-PM proteins is achieved. The general steps for enriching S-PM proteins using the hydrazide chemistry strategy are as follows:

[0066] Glycan aldehyde esterification: An oxidative treatment of proteins extracted from cell or tissue samples. This step introduces aldehyde functional groups (such as formaldehyde) into the glycans of S-PM proteins in cells or tissues.

[0067] Acylhydrazideation: A chemical reagent containing biotin and acylhydrazide functional groups (also known as a biotin-acylhydrazide bifunctional probe) is added to an aldehyde-treated sample. The acylhydrazide functional group reacts specifically with the aldehyde group to form an acylhydrazone structure.

[0068] Enrichment: Enrichment materials (such as agarose or affinity resin microspheres with streptavidin on their surface) are added to the sample. Biotin can bind to streptavidin on the surface of the resin microspheres to achieve enrichment of S-PM protein.

[0069] Elution: Use an appropriate washing buffer to wash away non-specifically bound proteins while retaining the enriched S-PM proteins.

[0070] Laser microdissection: Laser technology is used to microdissect sample tissue sections, separating diseased cells from stromal cells.

[0071] Cell sample lysis: The obtained diseased cell samples and stromal cell samples are lysed to extract total protein.

[0072] Spatial proteomics analysis: Proteins in these two cell samples were analyzed using chromatography-mass spectrometry. This technique preserves the in-situ distribution information of proteins in space, thereby revealing differences between different cell types.

[0073] Identification of specific proteins: By comparing the spatial proteomic analysis results of lesion cell samples and stromal cell samples, specific proteins of lesion cells and stromal cells were identified.

[0074] S-PM protein screening: From identified specific proteins, molecules containing S-PM proteins are screened out. These molecules act as signaling molecules and participate in paracrine ligand-receptor signaling pairs between diseased cells and stromal cells.

[0075] Preparation of tissue samples: Collect pancreatic cancer tissue samples and normal pancreatic tissue samples, lyse them into cell extracts to obtain protein samples.

[0076] Enrichment of tyrosine-phosphorylated proteins: Phosphorylated tyrosine-interacting complexes were enriched using the ternary probe-based Photo-pTyr-Scaffold method, and tyrosine-phosphorylated peptides were enriched using SH2 hyperparent assays for site identification.

[0077] Protein identification: High-throughput techniques such as mass spectrometry are used to analyze and identify enriched phosphorylated tyrosine protein complexes, determining the specific protein composition and relative abundance. These analyses can provide information on tyrosine phosphorylation-related protein complexes.

[0078] Identification of phosphorylation sites: Mass spectrometry analysis of tyrosine-phosphorylated peptides enriched by SH2 superphiles can identify the phosphorylated tyrosine sites. Identification of these sites can reveal which tyrosines are phosphorylated in pancreatic cancer tissue.

[0079] Example 1: Materials used in this invention

[0080] cell lines

[0081] The sources of human pancreatic cancer cell lines PANC1 (CRL-1469), AsPC1 (CRL-1682), MIA PaCa2 (CRL-1420), KP4 (JCRB0182), and human pancreatic stellate cell line hPSC have been described in previous studies (Shi, Y., Gao, W., Lytle, NK, Huang, P., Yuan, X., Dann, AM, Ridinger-Saison, M., Del Giorno, KE, Antal, CE, Liang, G., et al. (2019). Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring. Nature 569, 131-135.). Human pancreatic cancer cell lines SU.86.86 (CRL-1837) and SW 1990 (CRL-2172) were purchased from ATCC. The human pancreatic stellate cell line HPaSteC was purchased from ScienCell (California, USA). Cell culture was performed according to the supplier's instructions.

[0082] mice

[0083] Transgenic KPC (Kras) was used to collect tumor tissues from pancreatic cancer at different stages of development. LSL-G12D / + Trp53 flox / flox The Pdx1-Cre mouse model is described in the literature (Shi, Y., Gao, W., Lytle, NK, Huang, P., Yuan, X., Dann, AM, Ridinger-Saison, M., Del Giorno, KE, Antal, CE, Liang, G., et al. (2019). Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring. Nature 569, 131-135.).

[0084] Clinical samples

[0085] Human pancreatic tissue and plasma samples were purchased from Tongji Hospital, affiliated with Tongji Medical College, Huazhong University of Science and Technology, and approved by the Medical Ethics Committee of Tongji Hospital. A total of 74 patients underwent tissue sample collection in this study, including 30 patients with PDAC, 16 patients diagnosed with cancer located close to the pancreas (within 5 cm) and requiring partial removal of normal pancreas, and 28 patients with chronic pancreatitis.

[0086] Example 2: Synthesis of long-chain biotinylate probe

[0087] The structure of the long-chain biotin-hydrazide bifunctional probe is as follows: Figure 2 As shown in Figure a. The chemicals and materials were purchased from commercial sources. Silicone was used. Thin-layer chromatography (TLC) was performed using F254 plates (Merck) and a UV detector. Nuclear magnetic resonance (NMR) analysis was performed using a Bruker AvanceIII HD 400MHz instrument. NMR signals were reported as s (singleton), d (doublet), t (triplet), or m (multiplex), and all coupling constants were reported in Hertz (Hz). Molecular weights were measured using a Q-Exactive high-resolution mass spectrometer (Thermo Fisher Scientific).

[0088] Synthesis of C2: N-hydroxysuccinimide (8.51 g, 0.074 mol) was added to a biotin solution (9 g biotin [0.037 mol] dissolved in 50 mL DMF) and incubated at 50 °C for 0.5 h. Then, 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (19.29 g, 0.101 mol) was added and incubated overnight at room temperature (RT). The obtained product was filtered and vacuum dried to produce C2, yield 77.2% (9.71 g).

[0089] Synthesis of C3: C2 (8 g, 0.023 mol) was added to a solution of 4,7,10-trioxa-1,13-tetanediamine (26 g [0.118 mol] dissolved in 60 mL MeOH / H2O [5:1], v / v]) and incubated in an ice bath for 1.5 h. Trifluoroacetic acid (26 mL, 0.35 mol) was added dropwise and incubated in an ice bath for 2 h. The crude product was dried under vacuum and purified to obtain C3 in 60.9% (8 g).

[0090] Synthesis of C4: Succinic anhydride (0.98 g, 9.87 mmol) was added to 15 mL of DMF solution containing C3 (3.67 g, 8.23 ​​mmol) and N,N-diisopropylethylamine (1.27 g, 9.83 mmol), and the mixture was stirred for 1–2 hours. The solution was dried under vacuum and C4 was obtained in 72.1% (2.58 g) yield.

[0091] Synthesis of C5: Thionyl chloride (2.18 g, 18.33 mmol) was added to C4 solution (1 g [1.83 mmol] in 10 mL MeOH) and incubated overnight in an ice bath. The solution was dried under vacuum and purified to give C5 in 83.8% (860 mg) yield.

[0092] Synthesis of long-chain biotinylate: Hydrazine hydrate (1.2 mL) was added to C5 solution (600 mg [1.07 mmol] dissolved in 6 mL MeOH) and incubated overnight at room temperature (RT). The solution was dried under vacuum and purified by a semi-preparative HPLC column to give the final product (300 mg, 50% yield). 1 H NMR (400MHz, DMSO-d6) δ8.95(s,1H),7.81(t,J=5.5Hz,1H),7.76(t,J=5.6Hz,1H),6. 44(s,1H),6.37(s,1H),4.41-4.25(m,1H),4.18-4.09(m,3H),3.54-3.44(m,8H),3.39 (t,J=6.4Hz,4H),3.12-3.04(m,5H),2.82(dd,J=12.4,5.1Hz,1H),2.58(d,J=12.4Hz ,1H),2.31-2.26(m,4H),2.05(t,J=7.4Hz,2H),1.67-1.41(m,8H),1.38-1.21(m,2H). 13 C NMR (126MHz, DMSO-d6) δ172.36,171.50,171.36,163.17,70.22,69.99,68.55,68.51,61.50,59. 65,55.90,36.24,36.17,35.67,31.21,29.87,29.81,29.45,28.69,28.51,25.77.MW(m / z):[M+H] + Calculated for C 24 H 45 O7N6S561.3065, found 561.3044.

[0093] Example 3: Glycoprotein enrichment and enzymatic hydrolysis

[0094] Protein extraction

[0095] Tissues were ground into powder in liquid nitrogen and vortexed in a lysis and labeling buffer containing 1% (v / v) Triton X-100, 2% (w / v) SDS, 200 mM sodium chloride, and 100 mM sodium acetate at pH 5.5. Samples were further disrupted by sonication in an ice bath (Scientz JY 96-IIN, Ningbo Scientz, 20% energy) for 2 minutes (cycles of 3 seconds on, then 3 seconds off). Tissue fragments were removed by centrifugation. Cultured cells were washed with PBS and lysed directly in the lysis and labeling buffer, followed by sonication for 30 seconds at the same settings. Protein concentrations were determined using the BCA method, with 25 ng bovine fetal globulin added to 500 μg of lysis buffer as a glycoprotein standard for quality control prior to glycoprotein enrichment.

[0096] Glycoprotein labeling and enrichment

[0097] Following tissue protein extraction and oxidation, glycoproteins were directly covalently labeled with the hydrazide group of a synthetic biotin-hydrazide probe, followed by enrichment with streptavidin microspheres. First, the glycans on the glycoproteins were oxidized by adding sodium periodate to a final concentration of 2 mM and incubating at 4 °C for 30 min. Residual sodium periodate was then quenched by reacting with 4 mM sodium thiosulfate for 10 min at room temperature. The oxidized glycans were labeled with the biotin-hydrazide probe at a final concentration of 2 mM for 30 min at room temperature. Excess probe was removed by precipitation with methanol and chloroform. The protein precipitate was redissolved in a lysis buffer containing 8 M urea and 100 mM Tris-HCl (pH 7.8), then reduced with 10 mM DTT at 50 °C for 20 min, and alkylated with 30 mM IAA for 30 min at room temperature. The urea concentration was diluted to 2 M, and 25 μL of streptavidin microspheres were added, followed by gentle rotation at room temperature for 1 hour to enrich the labeled proteins.

[0098] Enzymatic hydrolysis on microspheres

[0099] Streptavidin microspheres were washed three times with 6M urea buffer containing 0.1% (v / w) SDS and 100 mM Tris-HCl (pH 7.8), once with 1M NaCl, and once with 80% (v / v) acetonitrile (ACN). Finally, they were washed twice with 50 mM ammonium bicarbonate (ABC). Under these harsh washing conditions, only a few dozen proteins were nonspecifically adsorbed onto the streptavidin beads, representing less than 1% of all identified proteins. The microspheres were then enzymatically digested overnight at 37°C in 50 μL of 50 mM ABC containing 1 μg trypsin. After digestion, non-glycopeptides were collected. The microspheres were washed as described above, except that the 6M urea was removed from the first wash buffer. 250 U PNGase F was added to 30 μL of 50 mM ABC and the mixture was incubated at 37°C for 1 hour to release glycopeptides. Glycopeptides and non-glycopeptides obtained from cell lines and mouse tissues were desalted on a C18 StageTip. Non-glycopeptides from human tissue samples were enriched on a C18 StageTip, desalted, and sequentially eluted in 5 mM ammonium formate (pH 10) solutions of varying concentrations (3%, 6%, 9%, 15%, and 80% [v / v]) to separate the non-glycopeptides into five fractions. The eluents were collected by lyophilization using Speed-Vac and stored at -20°C prior to LC-MS / MS analysis.

[0100] Example 4: Laser Micro-cutting (LCM) and Sample Preparation

[0101] For spatially resolved proteomics analysis, 13 frozen tumor tissue samples were embedded in OCT embedding medium (SakuraFinetek) and sectioned on a CM 1900 Cryostat platform (Leica). Immunohistochemical (IHC) staining was performed. Briefly, frozen sections (8 μm thick) were fixed with formaldehyde (4% aqueous solution, w / v) and incubated with hydrogen peroxide (3% aqueous solution, w / v) for 15 minutes. Sections were blocked with 10% (v / v) goat serum (Boster Biologicals) and then incubated with KRT19 (1:1000) or PDGFRB (1:500) primary antibody at 37°C for 1 hour. After washing with PBS, sections were incubated with HRP-conjugated goat anti-rabbit IgG at room temperature for 30 minutes, and the signal was detected using the Dako REAL EnVision assay kit. Finally, the nuclei were visualized by hematoxylin staining. LCM was performed on frozen sections adjacent to the IHC-stained sections on an LMD7000 system (Leica). Tissue sections (15 μm thick) were mounted on PEN-coated slides (Leica), moderately stained with hematoxylin, and dehydrated using a series of ethanol solutions. The LCM of the PCC and stromal regions was guided by IHC staining images targeting KRT19 and PDGFRB, respectively. The total section area was 10 mm². 2 .

[0102] LCM samples were added to a lysis buffer containing 600 mM guanidine hydrochloride, 1% (w / v) n-dodecylβ-D-maltoside, 150 mM NaCl, 15 mM TCEP, and 10 mM HEPES at pH 7.4, and then sonicated in a water bath sonicator (AutoScience) for 20 minutes. Proteins were then enzymatically digested using the SISPOT technique. In short, the sample pH was adjusted to approximately 3, and the sample was then centrifuged and loaded onto a SISPOT pipette tip, which was constructed by filling a C18 membrane into a 200 μL pipette tip as a stopper and then loading it with a strong cation exchange resin. The pipette tip was washed with 80% (v / v) ACN buffered with 8 mM potassium citrate (pH 3), followed by a wash with water. After enzymatic digestion and alkylation of the proteins with 1 μg of trypsin in 50 mM ABC containing 10 mM IAA, the peptides were eluted onto a C18 membrane, then desalted and fractionated into 5 fractions as described above.

[0103] Example 5: Data Processing

[0104] MS Database Retrieval

[0105] Raw mass spectrometry data were retrieved using MaxQuant software (version 1.5.5.1) from the UniProt Human Proteome Database (version 2019-06-22, 74,416 entries), the Human PM Protein Database (sequences divided into ECD and ICD), or the Mouse Proteome Database (version 2017-02-12, 50,306 entries). Urea methylation of cysteine ​​was set as a fixed modification. Deamination (N / Q) and oxidation (M) were set as dynamic modifications for all datasets, and phosphorylation-S / T / Y was also added as a dynamic modification for the pTyr peptide-enriched dataset. Two missing cleavage sites were allowed. Label-free quantification (LFQ) was selected to normalize all data. Matching between runs was selected to reduce missing values. Database searches were performed on raw files for dimethyl-labeled pTyr quantification using MaxQuant software (v.1.1.1.36) against the IPI Human Database (v.3.79), containing 91,464 entries, with phosphorylation S / T / Y set as variable modifications. A minimum ratio of 2 was required for protein quantification of dimethyl-labeled pTyr peptides. Unless otherwise specified, protein / domain identification in each dataset of this study required the identification of at least two unique peptides across all samples.

[0106] Database generation

[0107] Generation of the S-PM Protein Database. A human plasma membrane (PM) protein database containing 2829 PM proteins was constructed, referencing previously reported work. Transmembrane domains were confirmed using TMHMM v2.0 and the UniProtKB / Swiss-Prot database (version 2018_01). The secretory protein database was generated according to the following steps: First, typical secretory proteins were screened from three available online sources: proteins containing signal peptides from the UniProtKB / Swiss-Prot database, and proteins containing signal peptides predicted by SignalP v4.1 or Phobius. Then, proteins contained in at least two sources were retained in the database. Finally, proteins from the PM protein database were removed, resulting in a final secretory protein database containing 2527 secretory proteins.

[0108] Generation of the ligand-receptor database. The ligand-receptor pair database was established as described in the literature, with slight modifications. First, ligand-receptor pairs were downloaded from the following databases: DLRP, IUPHAR, and HPMR, downloaded on August 30, 2018, May 29, 2018, and May 28, 2018, respectively. By combining the three databases, 1179 ligand-receptor pairs were obtained. Then, based on experimentally validated protein-protein interactions (PPIs) in HPRD and STRING (v10.5), pairing relationships between ligands and receptors were generated in the inventors' S-PM database. From HPRD, the inventors obtained binary PPIs validated by one of three sources (in vivo, in vitro, and yeast two-hybrid). From STRING, the inventors obtained interactions based on a physical binding confidence score >= 700 in Homo sapiens and interactions with a confidence score >= 700 supported by experimental data. In addition, the pairing database was expanded using PPIs from the OmniPath database. Finally, by integrating these six available online databases and references, the inventors created a ligand-receptor database containing 788 ligands, 766 receptors, and 3919 pairs.

[0109] A database of pTyr writers, readers, and erasers was generated based on the references. This database includes 98 pTyr writers, 112 pTyr readers containing the SH2 domain, 53 pTyr readers containing the PTB domain, and 108 pTyr erasers.

[0110] Identification of N-glycosyl sites and phosphate sites

[0111] N-glycosylation sites were identified using the motif N-!PS / T or NXC, where N stands for deaminoasparagine, !P represents any amino acid except proline, and X represents any amino acid. Based on this standard, 6181 N-glycosylation sites were identified from 100 tissue samples, of which 98.7% were Class I sites with a resolution difference greater than 5 and a localization probability greater than 0.75. A total of 1360 pTyr sites were identified, of which 90.9% were Class I sites satisfying both a resolution difference greater than 5 and a localization probability greater than 0.75.

[0112] Identification and quantification of S-PM protein

[0113] The proteome tables generated from non-glycopeptides and glycopeptides were merged, identifying a total of 2741 S-PM proteins, with at least two unique peptides for non-glycopeptides and at least one unique glycopeptide. LFQ intensities were log2 transformed and normalized using the Limma R package. In 100 pancreatic samples, a total of 2658 S-PM proteins had at least one quantification value. Statistical significance between tumor and normal samples was calculated as a p-value <0.05 and a fold change >2, or a ratio of quantified samples after normalization >2, with at least 5 samples quantified in at least one group. S-PM proteins in the mouse dataset were processed using the same workflow and standards as those in human tissue samples. Combining the proteins identified from non-glycopeptides and glycopeptides, a total of 1643 S-PM proteins were quantified from all mouse samples. Statistical significance was calculated between tumor samples and NT samples from mice of different ages, with a p-value <0.05 and a fold change >2, a ratio of quantified samples after normalization >2, and at least half of the samples in at least one group having quantified values. Furthermore, since approximately one-third of S-PM proteins have raw intensity values ​​but no LFQ intensity values, these were filtered out due to their lower LFQ intensity values ​​to increase S-PM proteome coverage. However, S-PM proteins that met the above calculation criteria using raw intensity values ​​were also included in the final list of significantly varied S-PM proteins.

[0114] Spatial resolution and quantification of cell type-specific proteins

[0115] For quantification, LFQ intensities were log2 transformed and normalized using Limma to eliminate batch effects. PCC or matrix-specific proteins were defined according to the statistical criteria described above. Furthermore, for LCM samples, each protein was required to be quantified in at least five samples in a set, or for cell line samples, at least two replicates in at least one PCC or PSC line were required to be quantified. Significantly varying proteins quantified using both strategies were combined for downstream analysis. In cases of inverse results between tissue and cell line analyses, tissue-based quantification results were used to determine the cell type specificity of these proteins.

[0116] Quantification of pTyr protein complex and pTyr peptide

[0117] LFQ intensities were log2 transformed and normalized according to the LFQ intensities of the bait protein (SH2 superparent) in each sample. Statistical significance between tumor and normal samples was calculated with a p-value < 0.05 and a fold change > 2, or a ratio of quantified samples > 2 after normalization. Furthermore, proteins quantified in at least 20% (at least 3 samples) of the Photo-pTyr-scaffold dataset or in at least 5 samples of the pTyr-peptide enrichment dataset were also retained.

[0118] Example 6: Intercellular S-PM proteome dataset in PDAC (Step S1)

[0119] Non-glycopeptide information was used to quantify S-PM protein, and N-glycosite information was used to verify the glycosylation of S-PM protein, and even to identify novel glycoproteins. Compared with traditional acylhydrazine chemistry, which requires overnight coupling of glycoproteins to acylhydrazine microspheres, the inventors' synthesized long-chain biotinylate bifunctional chemical probe effectively labeled glycoproteins in tissue lysates within 30 minutes due to its high water solubility. Figure 2 bc). Traditional glycopeptide-based methods exhibit high selectivity for enriching S-PM proteins. However, compared to non-glycopeptides, glycopeptides occupy only a small fraction of the glycoprotein sequence, thus affecting identification sensitivity and quantitative accuracy. Figure 2 d). In addition to glycoproteins, trypsin hydrolysis also released some proteins enriched from non-glycan-specific hydrazine chemical reactions, as well as a very small amount of proteins non-specifically adsorbed onto streptavidin microspheres. However, 70% of the total LFQ intensity of all quantified proteins came from S-PM proteins ( Figure 2 e, f). Furthermore, compared to non-enriched whole proteome analysis, the inventor's method identified hundreds more S-PM proteins after enrichment, accounting for 30% of the total identified S-PM proteins, including many important intercellular signal transduction proteins (such as LIF). Moreover, the ratio of enriched S-PM proteins between cancer and adjacent normal tissues showed a strong correlation with the ratio obtained without enrichment, indicating that the inventor's method is unbiased and reflects changes in the expression level of S-PM proteins themselves without being affected by glycosylation. Figure 2 gj).

[0120] Next, a group of human pancreatic tissue samples were evaluated, including 29 tumors, 27 adjacent normal tissues (NT), 28 chronic pancreatitis (CP) tissues, and 16 normal tissues. Figure 3 To eliminate the technical challenges and potential variations in pancreatic tissue sample preparation caused by the dense matrix and abundant proteases, bovine placental glycoprotein was incorporated into each sample immediately after tissue lysis. Monitoring throughout the workflow showed negligible variation in LFQ intensity between samples, demonstrating the high reproducibility of the method. Figure 3d). The mean Pearson correlation coefficient for all samples in each group was 0.83, demonstrating the high quality of the dataset. A total of 2741 S-PM proteins were identified, including 1280 secreted proteins and 1461 PM proteins. On average, approximately 3500 N-glycosylation sites were identified per sample, a significant improvement compared to the average of 1500 N-glycosylation sites per sample identified based on intact glycopeptides in recently reported PDAC proteomics studies. The inventors identified a total of 6181 non-redundant N-glycosites and 80 newly identified glycoproteins. The identified S-PM proteins cover more than half of all predicted S-PM proteomes in the human proteome. Figure 3 d), and the coverage is even higher when considering only S-PM proteins expressed in a single PDAC tissue sample. Notably, approximately 76% of the S-PM proteome was efficiently annotated for intercellular signal transduction functions, particularly transmembrane receptors and secretory ligands crucial for intercellular signal transduction. Figure 3 e).

[0121] The inventors explored the tumor-specific S-PM proteome through quantitative comparisons between tumor and normal tissues. They discovered over 1000 differentially expressed proteins, spanning four orders of magnitude, covering 31 well-characterized cancer biomarkers and 91 targets of FDA-approved drugs. Figure 3 f). Those previously unreported S-PM proteins with a significant upregulation trend in tumors provide a valuable resource for discovering new diagnostic and therapeutic targets for PDAC. Overall, most differentially expressed proteins exhibited a gradual trend from normal to tumor samples. Significant similarities were found between tumor and CP samples, given that CP is a highly fibrotic, progressive inflammatory disease sharing many pathological features with PDAC. Gene Ontology Molecular Function (GOMF) analysis revealed that differentially expressed S-PM proteins between tumor and normal tissues could be categorized into five major clusters, including proteases, receptor tyrosine kinase (RTK)-related proteins, ECM-related proteins, and transporters, and were generally more significantly expressed than those between tumor and CP tissues. Figure 2 f).

[0122] Example 7: Spatial-resolved and cell-type-specific proteomic analysis revealed intercellular signaling in PDAC TME (step S2)

[0123] Cancer-stromal signaling is a key factor regulating tumorigenesis and progression. To explore the cell type origin and potential intercellular signaling roles of the S-PM proteome, 13 PDAC tumor samples were accurately separated from the PCC and stroma using immunohistochemistry-guided laser capture microdissection (LCM), followed by sample preparation using the integrated proteomics sample preparation technology SISPROT. Figure 4 a). Approximately 7,000–8,000 proteins were identified in each matrix or PCC region sample, including approximately 1,400 S-PM proteins. High quantitative reproducibility was observed across biological samples, representing the largest PDAC spatial proteome map to date. Figure 4 b). To further confirm the cell origin, transmembrane proteomic analysis based on secretomics and acylhydrazide chemistry was performed on six representative human PCC cell lines and two PSC cell lines that constitute the main components of the matrix. Figure 4 a). By merging the two datasets, 2331 S-PM proteins were identified, of which 787 and 584 were enriched in PCCs and stromal cells, respectively, including some known cell-specific marker proteins (a). Figure 4 c). In contrast, the inventors' spatial proteome dataset covers more S-PM proteins than recently reported LCM-based PDAC spatial proteome datasets and region- or cell-type-specific transcriptome datasets. The spatial and cell-type-specific proteome datasets annotated the cell localization of over 76% of the S-PM proteome identified in whole-tumor tissue, including 723 PCC-specific proteins and 557 matrix-specific proteins. Therefore, these data define the cell-type origin of the S-PM proteins identified in whole-tissue tissue and are correspondingly clinically relevant when comparing tumor and normal tissues. GOMF analysis showed that most proteins with ECM and protease activity were matrix-specific. Approximately 90% of the transport proteins were PCC-specific. Figure 4 d) This is because cancer cells reprogram their metabolic pathways to meet their energy and metabolic needs within a dense and poorly vascularized stromal microenvironment. Importantly, tyrosine signal amplification (TSA) staining validated the upregulated expression of six PCC-specific PM proteins in the tumor. For example, the TRPV4 calcium channel showed strictly PCC-specific expression. Figure 4 d).

[0124] To systematically explore intercellular signal transduction between tumor cells and stromal cells, the inventors first constructed a comprehensive ligand-receptor interaction database and performed pairwise annotation of the S-PM proteome identified in the entire tissue, obtaining a total of 1,724 ligand-receptor pairs consisting of 427 ligands and 424 receptors, covering most of the identified ligands or receptors in our pairing database. Figure 4 e). Based on the number of pairings, growth factor, cytokine, and integrin-related pairs belong to the most abundant GOMFs. Interestingly, 54% of the ligands and receptors in these pairs showed consistent upregulation in tumors, suggesting their potential functional importance in PDAC TME. Of these pairs, 524 pairs have had their cellular localization of ligands and receptors identified, including 262 paracrine signaling pairs. Since more ligands are matrix-specific and more receptors are PCC-specific, there are more paracrine signaling pairs transmitted from stromal cells to PCCs (n=190) than from PCCs to stromal cells (n=72). Figure 4 f). Functionally, many signaling proteins are covered by these paracrine signaling pairs; for example, the ECM and integrin family are more abundant in the matrix, while the RTK and transporter family are more abundant in the PCC.

[0125] Example 8: Time-resolved analysis of the S-PM proteome during tumor progression in a PDAC transgenic mouse model (step S3)

[0126] Because advanced PDAC cannot be surgically removed due to metastasis, and early PDAC is difficult to diagnose, it is difficult to collect pancreatic cancer samples from people at different stages of tumor progression.

[0127] From genetically engineered KPC (Kras) LSL-G12D / + Trp53 flox / flox Tumor tissues from Pdx1-Cre mice at different ages (corresponding to different stages of tumor progression) and pancreatic tissues from normal mice (NT) were collected to characterize the temporal changes of the S-PM proteome during tumor progression. Figure 5 a). At 3 weeks of age, significant inflammation, pancreatic intraepithelial neoplasia (PanIN), and some small solid tumor nodules were observed in the pancreas; at 5 weeks of age, progressive pancreatic dysplasia-associated cancer (PDAC) began to form; and by 7 weeks of age, almost all masses were late-stage invasive tumors. Using a glycoprotein enrichment strategy and proteomic analysis, the inventors identified 3000 N-glycosites and approximately 1500 S-PM proteins in each sample, with high reproducibility between biological replicates. Figure 5 b). Quantitative analysis of the S-PM proteome showed that at 3 weeks, the pancreatic tissue of KPC mice had a more similar expression profile to that of NT tissue, and was significantly different from that at 5 and 7 weeks. Figure 5c). Even so, 385 significantly differentially expressed S-PM proteins remained at 3 weeks compared to NT, suggesting their potential importance in early tumor development. Protein expression trends differed significantly between 3 and 5 weeks, but were similar between 5 and 7 weeks. Encouragingly, 90% of the significantly overlapping S-PM proteins between humans and mice showed consistent expression trends. Figure 5 d) These proteins exhibiting consistent trends were defined as tumor progression-related proteins. These results demonstrate the feasibility of using KPC mice to study the early development of pancreatic cancer.

[0128] Next, cluster analysis based on expression trends was performed on S-PM proteins with consistent trends in both humans and mice, revealing that these proteins were mainly divided into three clusters ( Figure 5 e). Of these, 529 S-PM proteins were defined as tumor progression-related proteins (membership value > 0.45), including 25 reported PDAC biomarkers and 57 FDA-approved drug targets, such as Lif, Thbs2, Timp1, Gpc1, Msln, and Lgals3. Figure 5 f). Similar to the significantly altered S-PM protein in human samples, the GOMF of progression-related proteins can be divided into four groups. Cell adhesion and ECM-related proteins are enriched in cluster 3, consistent with the dense matrix in the later stages of PDAC (f). Figure 5 (g) In contrast, cluster 2 showed an upregulation trend starting from week 3, containing various functional proteins, and is expected to be crucial for early PDAC TME development and intercellular signal transduction, and is a valuable resource for validating early detection biomarkers for PDAC. The inventors selected TNFRSF11B and NPTX1 for ELISA validation and found that they were significantly upregulated in plasma samples from PDAC patients compared with control samples, indicating the potential of tumor progression-related proteins as novel plasma biomarkers for PDAC.

[0129] Finally, the inventors annotated human ligand-receptor pairs using tumor progression trends of the S-PM protein obtained in the KPC model. Of the 1,724 ligand-receptor pairs identified in whole human tissue, approximately 600 pairs showed significant changes in at least one ligand or receptor compared to normal tissue. Of these pairs, 81% of the ligands or receptors showed tumor progression trends in KPC mice, while 15% of both ligands and receptors showed progression trends. Figure 5h). These percentages were similar for paracrine signaling pairs from the matrix to the PCC and from the PCC to the matrix, indicating that paracrine communication plays a crucial role in intercellular interactions in both directions. Signaling from the matrix to the PCC was more enriched during tumor progression. Interestingly, when further classified according to pairs with at least one ligand or receptor showing a progression trend across the three clusters, most pairs were assigned to cluster 3, demonstrating active intercellular signaling and the importance of TME in late-stage PDAC.

[0130] Example 9: Tyrosine phosphorylation-mediated intercellular signal transduction in PDAC (Step S4)

[0131] Tyrosine phosphorylation (pTyr) is widely recognized as activating the first wave of intercellular signaling, playing a crucial role in tumorigenesis and thus representing a promising therapeutic target for PDAC. Therefore, the inventors investigated the activation status of the ligand-receptor-downstream signaling axis mediated by pTyr in PDAC. Since pTyr constitutes less than 1% of the phosphorylated proteome, the inventors integrated the Photo-pTyr-scaffold method and the SH2 superbinder pTyr peptide enrichment method to simultaneously enrich and analyze pTyr-mediated protein complexes and pTyr sites from the same tissue sample. Figure 6 a). With this strategy, the inventors aim to capture three key pTyr machines: pTyr writers (kinases), readers (proteins containing SH2 or PTB domains), and erasers (phosphatases), which typically form protein complexes through pTyr sites.

[0132] Utilizing the high affinity of Photo-pTyr-Scaffold for pTyr proteins and capturing transient pTyr protein complexes via photocrosslinking, we identified a total of 464 PM proteins, 51 pTyr writers, 94 pTyr readers, and 46 pTyr erasers. Through pTyr peptide enrichment, many of these proteins were identified with corresponding pTyr sites. Combined, we achieved high coverage of pTyr writers, readers, and erasers in their respective databases. Figure 6 b). We normalized the LFQ intensity using the bait protein (Src SH2 superparent) and then quantitatively analyzed the pTyr protein quantified by the Photo-pTyr-Scaffold method. The heatmap showed that most of the three protein classes were more activated in tumor tissues than in normal tissues, and most of them identified pTyr sites, including 16 FDA-approved drug targets. PDGFRB, PTPN11, and TLN1 showed the most significant changes among the three protein classes and therefore warrant further investigation. Figure 6 c).

[0133] Example 10: Assembly and Analysis of Multidimensional Intercellular Signaling Networks (Step S5)

[0134] To systematically assemble a multidimensional proteome dataset, the inventors conducted systematic bioinformatics analysis to explore the intercellular signaling network between PCCs and stromal cells mediated by ligand-receptor-pTyr downstream proteins. First, Gene Ontology Bioprocessing (GOBP) analysis of the S-PM group and pTyr protein complex in the glycoproteome dataset showed high coverage of cancer signaling pathways and high similarity to cancer signaling pathways based on PDAC genomic data. Based on STRING, a total of 9299 ligand-receptor-downstream protein signaling axes were predicted for 224 ligands, 156 receptors, and 103 downstream proteins, which were categorized into 16 classes based on the significance of each node compared to normal tissues.

[0135] Our focus is on 148 ligand-receptor pairs that show significant changes in at least one ligand or receptor in tumors, and the 1672 pTyr-activated signaling axes formed by them. Figure 6 d). Of these pairs, 22 were annotated as paracrine signaling pairs by space- and cell-type-specific proteomics, of which 18 showed tumor progression trends in the KPC model, including 8 matrix-to-PCC pairs and 10 PCC-to-matrix pairs. Figure 6 e). These 18 pairs constitute 291 signaling axes closely related to RTK, such as the insulin receptor on PCC and the PDGFR-related axis on stromal cells (e). Figure 6 f).

[0136] In summary, the first step was to construct a ligand-receptor-downstream protein signaling axis (such as...). Figure 6 d) The construction of ligand-receptor in Figure 4 As stated in e, the receptor-downstream protein relationship was determined using a public protein interaction database; the datasets from which the ligand, receptor, and downstream protein originate are located... Figure 6 The annotation is below. Figure 6 The analysis of d yielded more than 9,000 signal axes, among which the focus was on the more than 1,000 signal axes with significant changes, which consisted of 148 ligand-receptor pairs (one ligand-receptor pair can correspond to multiple downstream proteins; see Example 9 for explanation of significant changes).

[0137] Next, we analyzed these 148 signal pairs. Figure 6 The annotations for 'e' identified which pairs are paracrine signaling pairs (tumor-to-stromal or stroma-to-tumor) and which pairs have ligands or receptors that are tumor progression-related proteins. A total of 18 ligand-receptor pairs were found to fit both criteria. Finally, these 18 ligand-receptor pairs and their downstream proteins are specifically illustrated, for example... Figure 6f.

[0138] Therefore, by systematically integrating the S-PM proteome, spatial and cell type proteomes, and the pTyr proteome, we systematically depicted the first wave of intercellular signaling pathways in the ligand-receptor mediated interactions between PCCs and stromal cells in the PDAC.

Claims

1. A method for constructing a dynamic interaction network of proteomes, comprising the following steps: S1. Enrich the proteins in the test samples and control samples, and analyze them to identify proteins that show significant differences between the test samples and control samples. The proteins are secretory proteins and membrane proteins of the sample, and the test sample and control sample are diseased samples and non-diseased samples, respectively. S2. Separate the test samples to obtain different types of cell samples, analyze them, obtain proteins that are significantly different between different types of cells, identify ligand-receptor signal pairs between different types of cells, and construct a ligand-receptor interaction database. The different types of cell samples are diseased cell samples and stromal cell samples. S3. Select test samples from different stages and repeat steps S1 and S2 to obtain the expression profiles of the ligand-receptor signal pair at different stages. S4. Perform protein phosphorylation analysis on the test samples to obtain the activation status of ligand-receptor signal pairs in different cell types; and S5. Based on the information obtained in steps S1 to S4, construct a protein-mediated dynamic interaction network in the test sample, wherein the protein-mediated dynamic interaction network is a protein-mediated intercellular temporal, spatial, and activation dynamic interaction network.

2. The method according to claim 1, characterized in that, The lesion sample is a tumor sample, and the non-lesion sample is a normal sample.

3. The method according to claim 1, characterized in that, In step S1, the enrichment is performed using an acylhydrazine probe.

4. The method according to claim 3, characterized in that, The structural formula of the acylhydrazide probe is shown below: 。 5. The method according to claim 1, characterized in that, In step S2, the separation is performed by laser micro-cutting or by flow cytometry.

6. The method according to claim 5, characterized in that, The separation is performed using a laser micro-cutting method.

7. The method according to claim 1, characterized in that, In step S2, the ligand-receptor signal pair is a paracrine ligand-receptor signal pair.

8. The method according to claim 1, characterized in that, In step S4, the protein phosphorylation analysis is tyrosine phosphorylation analysis, serine phosphorylation analysis, or threonine phosphorylation analysis.

9. The method according to claim 8, characterized in that, The protein phosphorylation analysis was a tyrosine phosphorylation analysis.

10. The method according to claim 8 or 9, characterized in that, In step S4, the tyrosine phosphorylation analysis uses the Photo-pTyr-Scaffold method based on a ternary probe to enrich phosphorylated tyrosine interaction complexes, and uses SH2 hyperparental enrichment of tyrosine phosphorylated peptides for site identification.

11. The method according to claim 1, characterized in that, Step S1 obtains the dynamics of significant changes in the test sample compared to the control sample; step S2 obtains the spatial dynamics; step S3 obtains the temporal dynamics; and step S4 obtains the activation dynamics.

12. A proteome dynamic interaction network model, which is constructed according to the method described in any one of claims 1 to 11.

13. The proteome dynamic interaction network model according to claim 12, characterized in that, The proteome consists of secretory proteins and membrane proteins.

14. The use of the model as described in claim 12 or 13 in the preparation of a kit or apparatus for constructing a dynamic proteome interaction network model.

15. The use of a model as described in claim 12 or 13 in identifying biomarkers or screening drugs.

16. An apparatus for constructing a dynamic proteome interaction network model, comprising: S1, First analysis module, used to enrich and analyze proteins in test samples and control samples to obtain proteins that show significant differences between test samples and control samples, wherein the proteins are secretory proteins and membrane proteins of the samples, and the test samples and control samples are diseased samples and non-diseased samples. S2, the second analysis module separates the test sample to obtain different types of cell samples, analyzes them, obtains proteins that are significantly different between different types of cells, identifies ligand-receptor signal pairs between different types of cells, and constructs a ligand-receptor interaction database. The different types of cell samples are diseased cell samples and stromal cell samples. S3, the third analysis module, selects test samples from different stages and repeats steps S1 and S2 to obtain the expression profiles of the ligand-receptor signal pair at different stages; S4, the fourth analysis module, performs protein phosphorylation analysis on the test samples to obtain the activation status of ligand-receptor signal pairs in different types of cell samples; and S5. Construction module, used to construct a protein-mediated dynamic interaction network in the test sample based on the information obtained in steps S1 to S4, wherein the protein-mediated dynamic interaction network is a protein-mediated intercellular temporal, spatial, and activation dynamic interaction network.

17. A storage medium storing computer instructions for execution by the computer to implement the method for constructing dynamic interaction networks of proteomics as described in any one of claims 1 to 11.

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