Method for constructing proteome dynamic interaction network

Abstract

The invention belongs to the field of biological detection, particularly relates to a method for constructing a proteome dynamic interaction network, and more particularly relates to a method for constructing a secretory protein-membrane protein proteome dynamic interaction network. By using the method disclosed by the invention, an intercellular time, space and activation dynamic interaction network can be constructed, so that the characteristics and dynamic change of protein-mediated intercellular interaction in a microenvironment are disclosed, and an important basis is provided for further researching the development, treatment strategy and prognosis of diseases related to samples, such as tumors.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection, and specifically, relates to a method for constructing a proteome dynamic interaction network, and more specifically, relates to a method for constructing a secretory protein-membrane protein proteome dynamic interaction network. Background Art

[0002] The tumor microenvironment (TME) is a key hallmark of cancer. The TME is atypically rich in non-malignant stromal cells and an extensive extracellular matrix (ECM), which can promote cancer proliferation, metastasis, and drug resistance. Many therapeutic strategies targeting the dense stroma have been tested. Researchers are increasingly working to explore the diversity of the TME and its interactions with cancer cells. However, current functional proteomic studies are performed on cell line-based systems and may not be able to reproduce the signaling events between cells under physiological conditions in cancer.

[0003] At the same time, it is known that genetic changes alone are not sufficient to explain the occurrence and development of diseases. Comprehensive proteomic, phosphoproteomic, glycoproteomic and genomic characterizations of many cancers, such as pancreatic ductal adenocarcinoma (PDAC), have been reported recently, revealing the impact of genomic alterations on protein expression and post-translational modifications (PTMs) and delineating molecular subtypes. A common limitation of these studies is that the overall analysis based on whole tissues cannot fully elucidate the impact 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 impact 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 creatively constructed a proteomic dynamic interaction network method, which can clarify the temporal, spatial, and activation dynamic interaction network between cells mediated by proteins in the tumor microenvironment, especially secretory proteins and membrane proteins.

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

[0007] S1. Enrich the proteins of the test sample and the control sample, and analyze them to obtain proteins with significant differences between the test sample and the control sample;

[0008] S2. Separate the test samples to obtain samples of different types of cells, and analyze them to obtain proteins with significant differences between different types of cells, determine the ligand-receptor signal pairs between different types of cells, and build a ligand-receptor interaction database;

[0009] S3, selecting test samples at different stages and repeating steps S1 and S2 to obtain expression profiles of ligand-receptor signal pairs at different stages;

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

[0011] S5. Construct a dynamic interaction network mediated by proteins in the test sample based on the information obtained in steps S1 to S4.

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

[0013] Further, the test sample and the control sample may be a diseased sample and a non-lesioned sample. Further, the diseased sample may be a tumor sample, and the non-lesioned sample may be a normal sample. In some specific embodiments, the diseased sample may be a pancreatic cancer sample. In some specific embodiments, the diseased sample may be from a pancreatic cancer transgenic mouse model.

[0014] Further, in step S1, the enrichment is performed by a hydrazide probe. Further, the hydrazide probe carries a biotin group. Further, the hydrazide probe can be modified to have higher solubility.

[0015] In some specific embodiments, the structural formula of the hydrazide probe is as follows:

[0016]

[0017] Furthermore, in step S2, the different types of cell samples may be diseased cell samples and matrix cell samples. Furthermore, the different types of cell samples may be cancer cell samples and matrix cell samples.

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

[0019] The separation can also be performed by flow sorting or magnetic bead sorting. Specifically, fresh tissue is broken and digested into single cells, and then a cell sorting method such as flow sorting, magnetic bead sorting, etc. is used to separate specific cell types.

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

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

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

[0023] Furthermore, in the steps S1 and S2, the proteins with significant differences refer to proteins with expression differences of 2 times or more.

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

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

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

[0027] In some specific embodiments, the protein phosphorylation analysis can be a tyrosine phosphorylation protein analysis. Furthermore, the tyrosine phosphorylation protein analysis uses a ternary probe-based Photo-pTyr-Scaffold method to enrich phosphorylated tyrosine interaction complexes and uses SH2 superphiles to enrich tyrosine phosphorylated polypeptides for site identification.

[0028] Further, in step S5, the protein-mediated dynamic interaction network can be a protein-mediated intercellular time, space, and activation dynamic interaction network, wherein step S1 obtains the significant change dynamics in the test sample compared with the control sample, for example, when the test sample is a tumor, the significant change dynamics of the tumor compared with other tissues (such as normal tissue, adjacent cancer tissue, and inflammatory tissue) are obtained. Specifically, for example, when the test sample is a pancreatic tumor, the significant change dynamics of the pancreatic tumor compared with other tissues (such as normal tissue, adjacent cancer tissue, and pancreatitis tissue) are obtained, S2 obtains spatial dynamics, step S3 obtains temporal dynamics, and step S4 obtains activation dynamics.

[0029] Furthermore, it may be a temporal, spatial, and activation dynamic interaction network between cells mediated by secretory proteins and membrane proteins.

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

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

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

[0033] Furthermore, the model is a temporal, spatial, and activation dynamic interaction network model between cells mediated by secretory proteins and membrane proteins.

[0034] In a third aspect, the present invention provides a use of the construction method or model as described above in preparing a kit or device for constructing a proteome dynamic interaction network model.

[0035] In a fourth aspect, the present invention provides use of the above-described model in determining biomarkers or screening drugs.

[0036] In a fifth aspect, the present invention provides a device for constructing a proteome dynamic interaction network model, comprising:

[0037] S1, a first analysis module, for enriching the proteins of the test sample and the control sample, and performing analysis to obtain proteins with significant differences between the test sample and the control sample;

[0038] S2, the second analysis module, separates the test sample to obtain different types of cell samples, and analyzes them to obtain proteins with significant differences between different types of cells, and determines the ligand-receptor signal pairs between different types of cells, and constructs a ligand-receptor interaction database;

[0039] S3, a third analysis module, selecting test samples at different stages and repeating steps S1 and S2 to obtain expression profiles of ligand-receptor signal pairs at different stages;

[0040] S4, a fourth analysis module, performing protein phosphorylation analysis on the test sample to obtain the activation state of the ligand-receptor signal pair of different types of cell samples; and

[0041] S5, a construction module, used to construct a dynamic interaction network mediated by proteins in the test sample according to the information obtained in steps S1 to S4.

[0042] In a sixth aspect, the present invention provides a device, 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, and the instructions are used to be executed by the processor to implement any of the above-mentioned methods for constructing a dynamic interaction network of proteomes.

[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, wherein the storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement any of the above-mentioned methods for constructing a dynamic interaction network of proteomes.

[0048] In some embodiments, the storage medium is a computer-readable storage medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a process for constructing a temporal, spatial, and activation dynamic interaction network among cells mediated by S-PM protein in a pancreatic cancer tumor microenvironment according to one embodiment of the present application;

[0050] Figure 2 (a) Synthesized long-chain biotin hydrazide probe; (b) Biotin hydrazide probe used to label glycoproteins in mouse pancreatic tissue lysate; (c) Comparison between probe-based method and traditional hydrazide microsphere-based method; (d) Comparison between glycopeptides and non-glycopeptides at qualitative and quantitative levels; (e) The proportion of S-PM proteins and non-specific reactive proteins in all enriched proteins; (f) The proportion of non-specific adsorbed proteins; (g, h) Comparison between enrichment and non-enrichment at qualitative and quantitative levels; (i, j) Correlation between enrichment and non-enrichment on S-PM protein quantification;

[0051] Figure 3 (a) Pancreatic tissue cohort and S-PM protein analysis process; (b) The proportion of S-PM proteins based on glycopeptides in the total identified proteins; (c) Comparison of S-PM protein sequence coverage by glycopeptides and non-glycopeptides; (d) The number of S-PM proteins identified in 100 tissues; (e) Intercellular signaling function annotation of S-PM proteins; (f) Heat map analysis of significantly differentially identified proteins and the covered biomarkers and drug targets; (g) GOMF analysis of differentially identified proteins;

[0052] Figure 4(a) Spatial and cell type proteome analysis pipeline; (b) Number of S-PM proteins identified by spatial and cell type proteomes; (c) 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 proteomes; (e) Ligand-receptor pair analysis of S-PM proteins identified in tumor tissue; (f) Autocrine and paracrine ligand-receptor pairs between PCC and matrix cells;

[0053] Figure 5 (a) The technical process of temporal expression profiling; (b) The number of S-PM proteins and glycosylation sites identified in mouse pancreatic tissue; (c) Heat map of significantly differentially expressed S-PM proteins; (d) Expression trends in overlapping significant S-PM proteins between humans and mice; (e) Cluster analysis of significant S-PM proteins with consistent trends between humans and mice; (f) Identification of reported tumor biomarkers in the three clusters; (g) The top five GOMFs in the three clusters; (h) Correlation analysis of paracrine signaling pairs with tumor progression;

[0054] Figure 6 Figure 3: (a) The tyrosine phosphorylation proteome analysis pipeline; (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, in which both receptors and downstream proteins are significantly changed in the pTyr complex dataset; (e) Spatial and temporal resolution annotations of 148 screened receptor-ligand pairs; (f) Paracrine signaling pairs from PCCs to stroma and from stroma to PCCs and downstream activated signaling axes. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below in conjunction with specific embodiments and examples, and the advantages and various effects of the present invention will be more clearly presented. It should be understood by those skilled in the art that these specific embodiments and examples are used to illustrate the present invention, rather than to limit the present invention.

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

[0057] In the present invention, "first", "second", "third", "fourth", etc. are only used to distinguish different matters and are not intended to limit the order.

[0058] In the present 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 the extracellular matrix, directly contacting the extracellular environment, and playing important functions. S-PM proteins play a key role in biological processes such as cell-to-cell interaction, signal transduction, cell adhesion and cell recognition. They can act as signal ligands and receptors, cell adhesion molecules, cell recognition molecules, etc. to regulate cell functions and interactions. By enriching and analyzing S-PM proteins, we can gain a deep understanding of the protein composition and regulatory mechanism of the cell surface in different tissues or disease states. For example, in pancreatic cancer research, enriching and analyzing S-PM proteins can help determine proteins with significant differences between pancreatic cancer and normal pancreatic tissue, and further reveal the molecular mechanism of pancreatic cancer development and changes in cell-to-cell interactions.

[0059] For ease of understanding, generally speaking, the terms in the present invention have the following explanations:

[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: Use appropriate techniques to separate proteins on the cell surface and in the extracellular matrix from other cellular components.

[0063] Proteomic analysis: A common method for proteomic analysis of the enriched S-PM proteins is mass spectrometry. 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 with significant changes in the test samples compared with the control samples were identified. These proteins may be related to the development of lesions and cell-cell interactions, providing potential targets and basis for further research.

[0065] Generally speaking, since S-PM proteins are usually of low abundance, they represent most of the N-linked glycosylated proteins in the human proteome. In order to improve the effect of the technical solution of the present application, it is necessary to obtain as many S-PM proteins as possible. To this end, the inventors propose to adopt a hydrazide chemistry strategy for 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 of hydrazide and aldehyde, and selective enrichment of S-PM proteins is achieved by introducing hydrazide functional groups into the aldehyde groups of the sugar chains on the surface of proteins. Generally speaking, the general steps for enriching S-PM proteins with the hydrazide chemistry strategy are:

[0066] Glycosylation: Oxidation of proteins extracted from cell or tissue samples. This step will introduce aldehyde functional groups (such as formaldehyde) to the sugar chains of cell or tissue S-PM proteins.

[0067] Hydrazide acylating: A chemical reagent containing biotin and hydrazide functional groups (also called biotin-hydrazide bifunctional probe) is added to the aldehyde-acylating sample. The hydrazide functional group reacts specifically with the aldehyde group to form an acylhydrazone structure.

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

[0069] Elution: Use an appropriate wash 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 slices to separate diseased cells from stromal cells.

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

[0072] Spatial proteomic analysis: The proteins in the two cell samples were analyzed using chromatography-mass spectrometry, a technique that preserves the original 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, lesion cell-specific proteins and stromal cell-specific proteins are identified.

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

[0075] Preparation of tissue samples: Pancreatic cancer tissue samples and normal pancreatic tissue samples were collected and lysed into cell extracts to obtain protein samples.

[0076] Tyrosine phosphorylated protein enrichment: The ternary probe-based Photo-pTyr-Scaffold method was used to enrich phosphorylated tyrosine interaction complexes, and SH2 superphile was used to enrich tyrosine phosphorylated peptides for site identification.

[0077] Protein identification: Through high-throughput techniques such as mass spectrometry, the enriched phosphotyrosine protein complexes are analyzed and identified to determine the composition and relative abundance of specific proteins. These analyses can provide information on tyrosine phosphorylation-related protein complexes.

[0078] Identify phosphorylation sites: Phosphorylated tyrosine sites can be identified by mass spectrometry analysis of tyrosine phosphorylated peptides enriched by SH2 superphiles. Identification of these sites can reveal which tyrosines are phosphorylated in pancreatic cancer tissues.

[0079] Embodiment 1, the material used in the present 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., DelGiorno, 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 (CA, USA). The cells were cultured according to the supplier's instructions.

[0082] Mouse

[0083] Transgenic KPC (Kras) for collecting tumor tissues at different stages of pancreatic cancer development LSL-G12D / + ;Trp53 flox / flox ; Pdx1-Cre) mouse model was described in the literature (Shi, Y., Gao, W., Lytle, NK, Huang, P., Yuan, X., Dann, AM, Ridinger-Saison, M., DelGiorno, 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 samples and plasma samples were purchased from Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology, approved by the Medical Ethics Committee of Tongji Hospital. A total of 74 patients were included in this study for tissue sample collection, including 30 patients with PDAC, 16 patients diagnosed with cancer whose tumors were close to the pancreas (within 5 cm) and part of the normal pancreas needed to be removed, and 28 patients with chronic pancreatitis.

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

[0087] The structure of the long-chain biotin-hydrazide bifunctional probe is as follows Figure 2 a. Chemicals and materials were purchased from commercial sources. Silica gel was used F254 plates (Merck) and UV detectors were used for thin layer chromatography (TLC). Nuclear magnetic resonance spectroscopy (NMR) analysis was performed using a Bruker AvanceIII HD 400MHz machine. NMR signals were reported as s (singlet), d (doublet), t (triplet) or m (multiplet), 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 degrees Celsius for 0.5 hours. 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (19.29 g, 0.101 mol) was then added and incubated overnight at room temperature (RT). The obtained product was filtered and vacuum dried to produce C2 with a yield of 77.2% (9.71 g).

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

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

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

[0092] Synthesis of long-chain biotin hydrazide: Hydrazine hydrate (1.2 mL) was added to a 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] The tissue was 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, 200mM sodium chloride and 100m sodium acetate, pH 5.5. The sample was further broken by treating with ice bath ultrasound (Scientz JY 96-IIN, Ningbo scientz, 20% energy) for 2 minutes (cycle of 3 seconds on and then 3 seconds off). Tissue fragments were removed by centrifugation. Cultured cells were washed with PBS and lysed directly in lysis and labeling buffer, and then sonicated for 30 seconds with the same settings. The protein concentration was determined by the BCA method, and 25ng of bovine fetuin was added to 500μg of lysate as a glycoprotein standard for experimental quality control before glycoprotein enrichment.

[0096] Glycoprotein labeling and enrichment

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

[0098] Enzymatic hydrolysis on microspheres

[0099] The streptavidin microspheres were washed three times with 6M urea buffer containing 0.1% (v / w) SDS and 100mM Tris-HCl (pH 7.8), once with 1M NaCl, and once with 80% (v / v) acetonitrile (ACN). Finally, 50mM ammonium bicarbonate (ABC) was used twice. Under such harsh washing conditions, only a few dozen proteins were non-specifically adsorbed on the streptavidin beads, accounting for less than 1% of all identified proteins. The microspheres were incubated overnight at 37 degrees Celsius in 50μL of 50mM ABC containing 1μg of trypsin for protein digestion. After digestion, non-glycopeptides were collected. The microspheres were washed as described above, except that 6M urea was removed from the first wash buffer. 250U PNGase F was added to 30μL 50mM ABC and incubated at 37 degrees Celsius 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 separated into five fractions by sequentially eluting with a 5 mM ammonium formate (pH 10) solution in a series of ACN concentrations (3%, 6%, 9%, 15%, and 80% [v / v]). The collected eluate was lyophilized using a Speed-Vac and stored at -20 degrees Celsius prior to LC-MS / MS analysis.

[0100] Example 4. Laser microdissection (LCM) and sample preparation

[0101] For spatially resolved proteomic analysis, 13 frozen tumor tissues were embedded in OCT embedding medium (Sakura Finetek) and sectioned on a CM 1900 Cryostat platform (Leica). Immunohistochemistry (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 Biotech) and then incubated with KRT19 (1:1000) or PDGFRB (1:500) primary antibodies at 37°C for 1 hour. After PBS washing, 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 detection kit. Finally, cell nuclei were visualized by hematoxylin staining. LCM was performed on an LMD7000 system (Leica) using frozen sections adjacent to IHC-stained sections. Tissue sections (15 μm thick) were mounted on PEN-coated slides (Leica), moderately stained with hematoxylin, and dehydrated through a series of ethanol solutions. LCM of PCC and stromal regions was guided by IHC staining images for KRT19 and PDGFRB, respectively, with a total section area of ​​10 mm 2 .

[0102] LCM samples were added to a lysate containing 600mM guanidine hydrochloride, 1% (w / v) n-dodecylβ-D-maltoside, 150mM NaCl, 15mM TCEP, and 10mM HEPES, pH 7.4, and then sonicated in a water bath sonicator (AutoScience) for 20 minutes. The SISPROT technology was used to enzymatically hydrolyze proteins. In brief, the sample pH was adjusted to about 3, and then loaded onto the SISPROT tip by centrifugation, which was made by filling a C18 membrane into a 200μL pipette tip as a stopper and then loading a strong cation exchange resin. The centrifugal tip was washed with 80% (v / v) ACN buffered with 8mM potassium citrate (pH 3), and then washed with water. After enzymatic hydrolysis and alkylation of the protein with 1μg trypsin in 50mM ABC containing 10mM IAA, the polypeptide was eluted onto the C18 membrane, and then desalted and graded into 5 fractions as described above.

[0103] Example 5: Data processing

[0104] MS database search

[0105] The mass spectrometry raw data were retrieved using MaxQuant software (version 1.5.5.1) against 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). Carbamidomethylation of cysteine ​​was set as a fixed modification. Deamination (N / Q) and oxidation (M) were set as dynamic modifications for all data sets, and phosphorylation-S / T / Y was also added as a dynamic modification for the pTyr peptide enrichment data set. Two missed cleavage sites were allowed. Label-free quantification (LFQ) was selected to achieve normalization of all data. Matching between runs was selected to reduce missing values. Database searches were performed on raw files from dimethyl-labeled pTyr quantification against the IPI human database (v.3.79) containing 91,464 entries using MaxQuant software (v.1.1.1.36), with phosphorylation S / T / Y set as variable modifications. Protein quantification of dimethyl-labeled pTyr peptides required a minimum ratio of 2. Unless otherwise stated, protein / domain identification in each dataset of this study required a total of at least 2 unique peptides to be identified across all samples.

[0106] Database generation

[0107] Generation of the S-PM protein database. The human plasma membrane (PM) protein database containing 2829 PM proteins cited reported works. The transmembrane domain was confirmed by TMHMM v2.0 and 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 in the UniProtKB / Swiss-Prot database, proteins containing signal peptides predicted by SignalP v4.1 or Phobius. Then, proteins included in at least two sources were retained in the database. Finally, proteins included in the PM protein database were removed to generate the final secretory protein database containing 2527 secretory proteins.

[0108] Generation of 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 on August 30, 2018, May 29, 2018, and May 28, 2018, respectively. By combining the three databases, 1,179 ligand-receptor pairs were obtained. Then, pairing relationships between ligands and receptors were generated in the inventors' S-PM database based on experimentally verified protein-protein interactions (PPIs) in HPRD and STRING (v10.5). From HPRD, the inventors obtained binary PPIs proven by one of the three sources (in vivo, in vitro, and yeast two-hybrid). From STRING, the inventors obtained interactions based on physical binding confidence scores >= 700 in "Homo sapiens" and confidence scores >= 700 supported by experimental data. In addition, the pairing database was expanded by PPIs in the OmniPath database. Finally, by integrating these six online available databases and references, the inventors established a ligand-receptor database containing 788 ligands, 766 receptors, and 3919 pairs.

[0109] Generate a database of pTyr writers, readers, and erasers. The database of pTyr writers, readers, and erasers was generated based on the references, including 98 pTyr writers, 112 pTyr readers containing SH2 domains, 53 pTyr readers containing PTB domains, and 108 pTyr erasers.

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

[0111] N-glycosylation sites were identified using the motif N-!PS / T or NXC, where N is deaminated asparagine, !P represents any amino acid except proline, and X represents any amino acid. Based on this criterion, 6181 N-glycosylation sites were identified from 100 tissue samples, of which 98.7% were class I sites with a score 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 with a score difference greater than 5 and a localization probability greater than 0.75.

[0112] Identification and quantification of S-PM proteins

[0113] The proteome tables generated by non-glycopeptides and glycopeptides were merged, and a total of 2741 S-PM proteins were identified, with at least 2 unique peptides for non-glycopeptide card values ​​and at least 1 unique peptide for glycopeptides. LFQ intensities were log2 transformed and normalized using the Limma R package. A total of 2658 S-PM proteins had at least one quantified value in 100 pancreatic samples. Statistical significance between tumor and normal samples was calculated with a p value < 0.05 and a fold change > 2, or a ratio of the number of samples quantified after normalization > 2, and at least 5 samples were quantified in at least one group. S-PM proteins in the mouse dataset were processed using the same workflow and criteria as human tissue samples. Combining the proteins identified by non-glycopeptides and glycopeptides, a total of 1643 S-PM proteins were quantified in all mouse samples. Statistical significance was calculated between tumor samples and NT samples of mice of different ages, with p value < 0.05 and fold change > 2, the ratio of the number of samples quantified after normalization > 2, and at least half of the samples in at least one group had quantitative values. In addition, since about one-third of the S-PM proteins had raw intensity values ​​but no LFQ intensity values, in order to increase the coverage of the S-PM proteome, these S-PM proteins that were filtered out due to fewer LFQ intensity values, but met the above calculation criteria using raw intensity values, were also included in the final list of significantly changed S-PM proteins.

[0114] Spatially resolved and cell type-specific protein quantification

[0115] For quantification, LFQ intensities were log2 transformed and normalized by Limma to eliminate batch effects. PCC or matrix-specific proteins were defined according to the statistical criteria described above. In addition, for LCM samples, each protein was required to be quantified in at least 5 samples in a group, or for cell line samples, at least 2 replicates in at least one PCC or PSC line. Significantly changed proteins quantified by both strategies were combined for downstream analysis. For opposite results between tissues and cell lines, the quantitative results of the tissues were used to determine the cell type specificity of these proteins.

[0116] Quantification of pTyr protein complexes and pTyr peptides

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

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

[0119] Non-glycopeptide information is used to quantify S-PM proteins, and N-sugar site information is used to verify the glycosylation of S-PM proteins, and even to identify new glycoproteins. Compared with traditional hydrazide chemistry that requires overnight coupling of glycoproteins to hydrazide microspheres, the long-chain biotin hydrazide bifunctional chemical probe synthesized by the inventors is highly water-soluble and effectively labels glycoproteins in tissue lysates within 30 minutes ( Figure 2 bc). Traditional glycopeptide-based methods show high selectivity for the enrichment of S-PM proteins. However, glycopeptides occupy only a small portion of the glycoprotein sequence compared to non-glycopeptides, thus affecting identification sensitivity and quantitative accuracy ( Figure 2 d). In addition to glycoproteins, trypsin digestion also released some proteins enriched by non-glycan-specific hydrazine chemical reactions and a very small amount of proteins non-specifically adsorbed on streptavidin microspheres, but 70% of the total LFQ intensity of all quantified proteins came from S-PM proteins ( Figure 2 e,f). In addition, compared with the whole proteome analysis without enrichment, the inventors' method can identify 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), and the ratio of S-PM proteins between cancer and adjacent tissues obtained after enrichment is well correlated with the ratio obtained without enrichment, indicating that the inventors' method is unbiased and can reflect the changes in the expression level of S-PM proteins themselves without being affected by glycosylation ( Figure 2 gj).

[0120] Next, a panel of human pancreatic tissue samples was evaluated, including 29 tumors, 27 paracancerous tissues (NT), 28 chronic pancreatitis (CP), and 16 normal tissues ( Figure 3 ac). To eliminate the technical challenges and potential variability of pancreatic tissue sample preparation due to dense matrix and abundant proteases, the glycoprotein bovine fetuin was spiked into each sample immediately after tissue lysis to monitor the entire workflow, and the intensity of its LFQ showed negligible variation between each sample, demonstrating the high reproducibility of the method ( Figure 3d). The average 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 in each sample, which is a significant improvement compared to the average of 1500 N-glycosylation sites identified per sample based on intact glycopeptides in a recently reported PDAC proteomics study. The inventors identified a total of 6181 non-redundant N-glycan sites 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 higher when only S-PM proteins expressed in a single PDAC tissue sample are considered. Notably, approximately 76% of the S-PM proteome was validly annotated as having intercellular signaling functions, especially transmembrane receptors and secreted ligands that are critical for intercellular signaling ( Figure 3 e).

[0121] The inventors explored the tumor-specific S-PM proteome by quantitative comparison between tumor and normal tissues. A total of more than 1,000 differentially expressed proteins were found, spanning four orders of magnitude, covering 31 well-characterized cancer biomarkers and 91 targets of FDA-approved drugs ( Figure 3 f). Those S-PM proteins that have a clear up-regulation trend in tumors and have not been reported provide a valuable resource for discovering new PDAC diagnostic and therapeutic targets. Overall, most of the differentially expressed proteins showed a trend of gradual change from normal samples to tumor samples. Considerable similarities were found between tumor and CP samples, which is because CP is a progressive inflammatory disease with high fibrosis that shares many common pathological features with PDAC. Gene ontology molecular function (GOMF) analysis showed that the differentially expressed S-PM proteins between tumor and normal tissues could be divided into 5 major clusters, including proteases, receptor tyrosine kinase (RTK)-related proteins, ECM-related proteins, transporters, and were generally more significant than the differential proteins between tumor and CP tissues ( Figure 2 f).

[0122] Example 7. Spatially resolved and cell type-specific proteomic analysis reveals intercellular signaling in the PDAC TME (step S2)

[0123] Cancer-stroma signaling is one of the key factors regulating tumorigenesis and tumor progression. To explore the cell type origin and potential intercellular signaling role of the S-PM proteome, 13 PDAC tumor samples were accurately separated from PCC and stroma by immunohistochemistry (IHC)-guided laser capture microdissection (LCM), and then sample preparation was performed using the integrated proteomics sample preparation technology SISPROT ( Figure 4 a). Approximately 7000–8000 proteins were identified in each stromal or PCC region sample, including approximately 1400 S-PM proteins, with high quantitative reproducibility between biological samples, representing the largest spatial proteomic atlas of PDAC to date ( Figure 4 b) To further confirm the cell origin, secretome and hydrazide chemistry-based transmembrane proteome analysis was performed on six representative human PCC cell lines as well as 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 ( Figure 4 c). In contrast, our spatial proteomic dataset covers more S-PM proteins than recent LCM-based PDAC spatial proteomic datasets reported in the literature, as well as region- or cell-type-specific transcriptomic datasets. The spatial and cell-type-specific proteomic datasets annotated cellular localization for more than 76% of the S-PM proteome identified in whole-mount tumor tissue, including 723 PCC-specific proteins and 557 matrix-specific proteins. Therefore, these data define the cell-type origin of S-PM proteins identified in whole-mount tissue and are correspondingly relevant to clinical significance when comparing tumor and normal tissue. GOMF analysis showed that most proteins with ECM and protease activities are matrix-specific. Approximately 90% of transport proteins are PCC-specific ( Figure 4 d), because cancer cells reprogram their metabolic pathways to meet the energy and metabolic demands of being surrounded by a dense and poorly vascularized stromal microenvironment. Importantly, six PCC-specific PM proteins that were upregulated in tumors were validated by tyramide signal amplification (TSA) staining. For example, TRPV4 calcium channel, which showed strict PCC-specific expression ( Figure 4 d).

[0124] To systematically explore the intercellular signaling flows between tumor and stromal cells, we first constructed a comprehensive ligand-receptor interaction database and annotated the pairwise relationships of the S-PM proteome identified in the whole tissue, resulting in 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). Growth factor, cytokine, and integrin-related pairs were among the most abundant GOMFs based on the number of pairs. Interestingly, 54% of the ligands and receptors in the pairs showed consistent upregulation in tumors, suggesting their potential functional importance in the PDAC TME. Of these pairs, 524 had determined cellular localization of ligands and receptors, including 262 paracrine signaling pairs. As more ligands are stromal-specific and more receptors are PCC-specific, more paracrine signaling pairs are transmitted from stromal cells to PCCs (n=190) than from PCCs to stromal cells (n=72). Figure 4 f) Functionally, many signaling proteins are overlapped by these paracrine signaling pairs, for example, ECM and integrin families are more abundant in stroma, while RTK and transporter families are more abundant in 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-stage PDAC is difficult to diagnose, it is difficult to collect human pancreatic cancer samples at different stages of tumor progression.

[0127] From genetic engineering KPC (Kras LSL-G12D / + ;Trp53 flox / flox Tumor tissues and normal mouse pancreatic tissues (NT) of different ages (corresponding to different tumor progression stages) were collected from Pdx1-Cre (Pdx1-Cre) mice to characterize the temporal changes of the S-PM proteome during tumor progression ( Figure 5 a). At 3 weeks of age, the pancreas showed obvious inflammation, pancreatic intraepithelial neoplasia (PanIN), and some small solid tumor nodules; at 5 weeks of age, progressive PDAC began to form; and at 7 weeks of age, almost all masses were advanced invasive tumors. By using a glycoprotein enrichment strategy and proteomic analysis, the inventors identified 3000 N-glycan sites 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 pancreatic tissue from KPC mice at 3 weeks had an expression profile more similar to that of NT tissue and was distinct from that at 5 and 7 weeks ( Figure 5c). Even so, there were still 385 significantly differentially expressed S-PM proteins at 3 weeks compared to NT, indicating their potential importance in early tumor development. The protein expression trends between 3 and 5 weeks were significantly different, but the trends between 5 and 7 weeks were similar. Encouragingly, 90% of the overlapping significant S-PM proteins between human and mouse had consistent expression trends ( Figure 5 d), proteins with consistent change 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, we performed a cluster analysis based on the expression trends of the S-PM proteins with consistent trends between humans and mice, and found that these proteins were mainly divided into three clusters: Figure 5 e). Among them, 529 S-PM proteins were defined as tumor progression-related proteins (membership value > 0.45), including 25 reported PDAC markers 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 proteins in human samples, the GOMFs of progression-related proteins can be divided into four groups. Cell adhesion and ECM-related proteins are enriched in cluster 3, which is consistent with the dense matrix in the late stage of PDAC ( Figure 5 g). In contrast, cluster 2 showed an up-regulation trend starting from week 3 and contained various functional proteins, which are expected to be essential for early PDAC TME development and intercellular signaling, and are a valuable resource for validating PDAC early detection biomarkers. The inventors selected TNFRSF11B and NPTX1 for ELISA validation and found that they were significantly up-regulated in PDAC patient plasma samples compared with control samples, indicating the potential of tumor progression-related proteins as novel plasma biomarkers for the discovery of PDAC.

[0129] Finally, the inventors annotated human ligand-receptor pairs by the tumor progression trends of S-PM proteins obtained in the KPC model. For the 1,724 ligand-receptor pairs identified in whole human tissues, there were approximately 600 pairs in which at least one ligand or one receptor was significantly changed in tumors compared to normal tissues. 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 stroma to PCC and from PCC to stroma, indicating that paracrine communication plays an important role in cell-cell interactions in both directions. Signaling from stroma to PCC was more enriched during tumor progression. Interestingly, when pairs with progression trends for at least ligand or receptor were further classified according to the three clusters, most pairs were assigned to cluster 3, demonstrating the importance of active cell-cell signaling and TME in advanced PDAC.

[0130] Example 9. Tyrosine phosphorylation-mediated intercellular signaling in PDAC (step S4)

[0131] Tyrosine phosphorylation (pTyr) mechanisms have been widely recognized to activate the first wave of intercellular signaling, which plays an important role in tumorigenesis and is therefore a promising therapeutic target in PDAC. Therefore, the inventors investigated the activation state of the pTyr-mediated ligand-receptor-downstream signaling axis in PDAC. Since pTyr accounts for 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 aimed to capture three key pTyr machineries: pTyr writers (kinases), readers (SH2 or PTB domain-containing proteins), and erasers (phosphatases), which usually form protein complexes with the pTyr site.

[0132] Taking advantage of the high affinity of Photo-pTyr-Scaffold for pTyr proteins and capturing transient pTyr-protein complexes by photocrosslinking, we identified a total of 464 PM proteins, 51 pTyr writers, 94 pTyr readers, and 46 pTyr erasers, many of which were identified to the corresponding pTyr sites by pTyr peptide enrichment. Combined, we achieved high coverage of pTyr writers, readers, and erasers in the corresponding databases ( Figure 6 b). We quantified the pTyr proteins quantified by the Photo-pTyr-Scaffold method after normalizing the LFQ intensity with the bait protein (Src SH2 superphile). The heat map showed that most of the three categories of proteins were more activated in tumor tissues than in normal tissues, and pTyr sites were identified in most of them, including 16 FDA-approved drug targets. PDGFRB, PTPN11, and TLN1 were the most significantly changed among the three categories of proteins, and therefore deserve further study ( Figure 6 c).

[0133] Example 10: Multidimensional intercellular signaling network assembly and analysis (step S5)

[0134] In order to systematically assemble a multidimensional proteomic dataset, the inventors performed a systematic bioinformatics analysis to explore the intercellular signaling network between PCC and stromal cells mediated by ligand-receptor-pTyr downstream proteins. First, the gene ontology biological process (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 with cancer signaling pathways based on PDAC genomic data. According to STRING, a total of 9299 ligand-receptor-downstream protein signaling axes were predicted for 224 ligands, 156 receptors, and 103 downstream proteins, which can be divided into 16 categories according to the significance of each node in tumors compared with normal tissues.

[0135] We focused on 148 ligand-receptor pairs with at least one ligand or receptor significantly altered in tumors and 1672 pTyr-activated signaling axes formed by them ( Figure 6 d). Of these pairs, 22 were annotated as paracrine signaling pairs by spatial and cell type-specific proteomes, of which 18 showed tumor progression trends in the KPC model, including 8 stroma-to-PCC pairs and 10 PCC-to-stroma pairs ( Figure 6 e). These 18 pairs constitute 291 signaling axes that are closely associated with RTKs, such as the insulin receptor on PCCs, and the PDGFR-related axis on stromal cells ( Figure 6 f).

[0136] In general, the first step is to construct the signal axis of ligand-receptor-downstream protein (such as Figure 6 d) Ligand-receptor construction Figure 4 e As explained, the receptor-downstream protein relationship was determined by the public protein interaction database. The datasets of ligands, receptors, and downstream proteins are from Figure 6 A note is made below. Figure 6 d, a total of more than 9,000 signal axes were obtained, of which the circled ones were more than 1,000 significantly changed signal axes consisting of 148 ligand-receptor pairs (one ligand-receptor pair can correspond to multiple downstream proteins, and the explanation of significant changes is shown in Example 9)

[0137] Next, we performed Figure 6 e annotation, determine which pairs are paracrine signaling pairs (tumor to stroma or stroma to tumor), and which pairs of ligands or receptors are tumor progression-related proteins. A total of 18 ligand-receptor pairs meet these two conditions. Finally, these 18 ligand-receptor pairs and their downstream proteins are specifically displayed, such as Figure 6f.

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

Claims

1. A method for constructing a dynamic interaction network of a proteome, comprising the following steps: S1. Enrich the proteins of the test sample and the control sample, and analyze them to obtain proteins with significant differences between the test sample and the control sample; S2. Separate the test samples to obtain samples of different types of cells, and analyze them to obtain proteins with significant differences between different types of cells, determine the ligand-receptor signal pairs between different types of cells, and build a ligand-receptor interaction database; S3, selecting test samples at different stages and repeating steps S1 and S2 to obtain expression profiles of ligand-receptor signal pairs at different stages; S4. Perform protein phosphorylation analysis on the test samples to obtain the activation status of the ligand-receptor signaling pair of different types of cell samples; and S5. Construct a dynamic interaction network mediated by proteins in the test sample based on the information obtained in steps S1 to S4.

2. The method according to claim 1, characterized in that The proteins are secretory proteins and membrane proteins of the sample.

3. The method according to claim 1, characterized in that The test sample and the control sample are a diseased sample and a non-lesioned sample. Preferably, the diseased sample is a tumor sample, and the non-lesioned sample is a normal sample.

4. The method according to claim 1, characterized in that: In the step S1, the enrichment is performed by a hydrazide probe. Preferably, the structural formula of the hydrazide probe is as follows:

5. The method according to claim 1, characterized in that In the step S2, the separation is performed by a laser micro-cutting method or by flow sorting. Preferably, the separation is performed by a laser micro-cutting method.

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

7. 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. Preferably, the protein phosphorylation analysis is tyrosine phosphorylation analysis.

8. The method according to claim 1, characterized in that In step S4, the tyrosine phosphorylation analysis uses the Photo-pTyr-Scaffold method based on a ternary probe to enrich the phosphorylated tyrosine interaction complex, and uses the SH2 superphile to enrich the tyrosine phosphorylated polypeptide for site identification.

9. The method according to claim 1, characterized in that: In step S5, the protein-mediated dynamic interaction network is a protein-mediated intercellular time, space, and activation dynamic interaction network, wherein step S1 obtains the significant change dynamics in the test sample compared with the control sample, S2 obtains the spatial dynamics, step S3 obtains the temporal dynamics, and step S4 obtains the activation dynamics.

10. A proteome dynamic interaction network model constructed according to the method according to any one of claims 1 to 9.

11. The proteome dynamic interaction network model according to claim 10, characterized in that: The proteome is the secretory protein and membrane protein group.

12. Use of the model according to claim 10 or 11 in preparing a kit or device for constructing a proteome dynamic interaction network model.

13. Use of the model according to claim 10 or 11 in determining biomarkers or screening drugs.

14. A device for constructing a proteome dynamic interaction network model, comprising: S1, a first analysis module, for enriching the proteins of the test sample and the control sample, and performing analysis to obtain proteins with significant differences between the test sample and the control sample; S2, the second analysis module, separates the test sample to obtain different types of cell samples, and analyzes them to obtain proteins with significant differences between different types of cells, and determines the ligand-receptor signal pairs between different types of cells, and constructs a ligand-receptor interaction database; S3, a third analysis module, selecting test samples at different stages and repeating steps S1 and S2 to obtain expression profiles of ligand-receptor signal pairs at different stages; S4, a fourth analysis module, performing protein phosphorylation analysis on the test sample to obtain the activation state of the ligand-receptor signal pair of different types of cell samples; and S5, a construction module, used to construct a dynamic interaction network mediated by proteins in the test sample according to the information obtained in steps S1 to S4.

15. A storage medium storing computer instructions, wherein the computer instructions are used to be executed by the computer to implement the method for constructing a dynamic interaction network of a proteome as claimed in any one of claims 1 to 9.

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