A protein marker group, application, kit and detection method for detecting myeloid cells

Abstract

The present invention provides a protein marker group, application, kit and detection method for detecting myeloid cells, wherein the protein marker group includes mononuclear phagocyte antibody markers, neutrophil antibody markers and antigen presenting cell antibody markers and their analogs. The present invention can accurately depict the state of tumor and immune cells in the tumor microenvironment, provide important reference information for accurate diagnosis of tumors, and also has important significance for immunotherapy of tumors, including the proportion and distribution of immune cell subpopulations, especially the spatial relationship between immune cells and tumor cells, which are important contents of tumor prognosis and prediction research.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a protein marker group and applications thereof. Background Art

[0002] Myeloid refers to myeloid cells, which in a broad sense include granulocytes, red blood cells, macrophages and monocytes, and are hematopoietic cells in a narrow sense, while lymphocytes are considered non-hematopoietic cells. All cellular components of blood are derived from hematopoietic stem cells, among which myeloid cells are cells that are completely differentiated and generated in the bone marrow. Different types of cells have different physiological functions in the body, but they all have certain significance for the normal life activities of the body. Myeloid checkpoints refer to molecular signaling pathways and molecular regulatory factors that play a key regulatory role in the bone marrow hematopoietic system. Bone marrow is the main hematopoietic organ in the human body, responsible for the production of various types of cells in the blood, including red blood cells, white blood cells and platelets. The function of myeloid checkpoints is to ensure the balance and stability of the hematopoietic process and maintain the normal function of the blood system. Disorders in myeloid checkpoints may lead to abnormal hematopoietic function and the occurrence of blood diseases. Current treatment effect predictions are mainly aimed at lymphoid immune checkpoints, such as PD-1, PD-L1, and CTLA4, but lack reagents and supporting algorithms for myeloid immune checkpoints.

[0003] There are various methods that can be used to distinguish the various cell types within the myeloid lineage. First, bone marrow cell subsets can be characterized based on their morphology and distribution in tissues or blood. However, fine-grained classification of different cell types requires immunophenotyping, which uses the expression of different cell surface molecules that can be recognized by antibodies and visualized by immunohistochemistry or flow cytometry. However, clinical samples are generally too precious for researchers to obtain, so immunohistochemistry is generally the method of choice to distinguish cell types.

[0004] Existing treatment effect predictions are mainly targeted at lymphoid immune checkpoints, such as PD-1, PD-L1, and CTLA4. However, there are also immune checkpoint drugs targeting myeloid cells on the market, such as CD47, LILRB1, and CD171. The existing PD-1 and PD-L1 tests cannot predict or evaluate the efficacy of such myeloid immune checkpoint drugs. Moreover, for clinical samples, tissues are particularly precious. Traditional IHC wastes samples, and IHC analysis is qualitative analysis, and its judgment mostly relies on experience. The analysis results will be different and inaccurate. Traditional immunohistochemistry staining can only stain one mark on a slide. If 6 marks need to be stained, 6 connected slices are required, and the final results cannot reach the same cellular level.

[0005] Therefore, there is an urgent need to develop a set of predictive or companion diagnostic reagents and analytical methods for myeloid cell-targeted drugs. Summary of the invention

[0006] The technical purpose of the present invention is to provide a protein marker group, application, kit and detection method for detecting myeloid cells in view of the problems existing in the background technology, which can accurately depict the state of tumor and immune cells in the tumor microenvironment, provide important reference information for the accurate diagnosis of tumors, and also have important significance for the immunotherapy of tumors, including the proportion and distribution of immune cell subpopulations, especially the spatial relationship between immune cells and tumor cells, which are important contents of tumor prognosis and prediction research. The present invention can be aimed at some clinicians who lack pathological knowledge, and can conveniently, quickly and effectively achieve the purpose of researchers.

[0007] To achieve the above technical objectives, the first aspect of the present invention provides a protein marker group for detecting myeloid cells, including monocyte-phagocyte antibody markers, neutrophil antibody marker MPO and antigen presenting cell antibody markers and their analogs.

[0008] Preferably, the protein marker group for detecting myeloid cells includes one or more combinations of the following: CD45, CD11b, CD11c, CD14, CD15, CD16, CD33, CD68, CD86, CD163, CD206, SPP1, FOLR2, C1QC, CD1c, HLA-DR, CD141, S100A9, S100A8, CD117, CD123, MPO, HLA-DR.

[0009] Preferably, the protein marker panel for detecting myeloid cells can be applied to all available fluorescent channels, including but not limited to DAPI, 480, 520, 540, 570, 620, 650, 690, and 780.

[0010] Preferably, the mononuclear-phagocyte antibody markers include differentiation antibody CD68 on the surface of macrophages, βD microglobulin antibody CD11b and helper T cell antibody CD14 and their analogs.

[0011] Preferably, the antigen presenting cell antigen markers include human leukocyte DR antigen HLA-DR and macrophage mannose receptor CD206 and their analogs.

[0012] Preferably, the fluorescence channels corresponding to the protein marker group are:

[0013] CD68………………480,

[0014] CD11b………………520,

[0015] CD14………………570,

[0016] MPO………………620,

[0017] HLA-DR………………690,

[0018] CD206………………780.

[0019] Preferably, the protein marker group includes: differentiation antibody CD68 on the surface of macrophages, βD microglobulin antibody CD11b, helper T cell antibody CD14, neutrophil antibody marker MPO, leukocyte antigen marker HLA-DR and immunohistochemistry CD163.

[0020] Preferably, the object of examination of the protein marker group is tissue cells.

[0021] Preferably, the protein marker group is integrated into a kit.

[0022] A second aspect of the present invention provides an application of a protein marker panel in detecting myeloid cells.

[0023] The protein marker group includes mononuclear-phagocyte antibody markers, neutrophil antibody marker MPO and antigen presenting cell antibody markers and the like.

[0024] Preferably, the mononuclear-phagocyte antibody markers include differentiation antibody CD68 on the surface of macrophages, βD microglobulin antibody CD11b and helper T cell antibody CD14 and their analogs.

[0025] Preferably, the antigen presenting cell antigen markers include human leukocyte DR antigen HLA-DR and macrophage mannose receptor CD206 and their analogs.

[0026] Preferably, the fluorescence channels corresponding to the protein marker group are:

[0027] CD68……………………480

[0028] CD11b……………………520

[0029] CD14……………………570

[0030] MPO……………………620

[0031] HLA-DR………………690

[0032] CD206……………………780.

[0033] As another preferred embodiment, the protein marker group includes: differentiation antibody CD68 on the surface of macrophages, βD microglobulin antibody CD11b, helper T cell antibody CD14, neutrophil antibody marker MPO, leukocyte antigen marker HLA-DR and type I membrane protein CD163;

[0034] The fluorescence channels corresponding to the protein marker groups are:

[0035] CD68……………………480,

[0036] CD11b……………………520,

[0037] CD14……………………570,

[0038] MPO……………………620,

[0039] HLA-DR………………690,

[0040] CD163……………………780.

[0041] Preferably, the object of examination of the protein marker group is a tumor or an immune cell.

[0042] The third aspect of the present invention provides a kit for detecting myeloid cells, wherein the kit integrates the protein marker group described in the first aspect of the present invention and a detectable labeling component of the protein marker group, and the protein marker group and the detectable labeling component are coupled.

[0043] Preferably, the detectable labeling component comprises a fluorescent substance, and the detectable labeling component corresponds one-to-one to the protein markers in the protein marker group.

[0044] Preferably, the corresponding relationship between the fluorescent substance and the protein marker (antibody) is as shown in Table 1.

[0045] Table 1

[0046]

[0047] In another preferred embodiment, the corresponding relationship between the fluorescent substance and the protein marker (antibody) is shown in Table 2.

[0048] Table 2

[0049]

[0050] The third aspect of the present invention provides a method for detecting myeloid cells, the method using the protein marker panel described in the first aspect of the present invention, or using the kit described in the third aspect of the present invention, and specifically comprising the following steps (taking intestinal tissue as an example):

[0051] 1. Dewaxing of baked slices: bake intestinal tissue slices at 62℃ for 1.5 hours; dewaxing: xylene for 10 minutes, xylene for 10 minutes, 95% ethanol for 5 minutes, 90% ethanol for 5 minutes, 85% ethanol for 5 minutes, 80% ethanol for 5 minutes; wash with water 3 times, 5 minutes each time.

[0052] 2. Fixation: Add 4% paraformaldehyde fixative to completely cover the intestinal tissue on the slice, fix for 15-20 minutes, and wash with water three times, 2 minutes each time.

[0053] 3. Repair: Place the tissue slides in a staining jar containing sufficient Tris-EDTA antigen repair solution (buffer), place in an electric cooker before boiling, start timing for 10 minutes after the water boils, then switch to keep warm for 30 minutes, and cool to room temperature; rinse with PBS 3 times, 5 minutes each time.

[0054] 4. Blocking: Add 3% hydrogen peroxide and incubate in dark for 25 minutes, then wash with PBS 3 times, 5 minutes each time.

[0055] 5. Blocking: Use a histochemical pen to outline the intestinal tissue specimen on the slice, add blocking solution on the intestinal tissue, cover it completely, and block it at room temperature for 10 minutes.

[0056] 6. Primary antibody incubation: dilute the antibody in an appropriate ratio, drain the blocking solution, add CD11b antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 1 hour or at 4° overnight, then wash 3 times with 1× TBST, 2 minutes each time.

[0057] 7. Secondary antibody incubation: Add secondary antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 15 minutes, then wash 3 times with 1×TBST, 2 minutes each time.

[0058] 8. Fluorescence incubation: dilute the fluorescence in proportion, add fluorescence 520, incubate at room temperature for 15 minutes, and then wash 3 times with 1× TBST, 2 minutes each time.

[0059] 9. Repair: Place the intestinal tissue sections in a staining jar containing sufficient Tris-EDTA antigen repair solution, place in a rice cooker before boiling, start timing for 10 minutes, then switch to keep warm for 30 minutes, and cool at room temperature. Rinse once with 1×TBST for 2 minutes.

[0060] 10. Restart steps 5-9 and stop repeating until all five target antibodies are labeled (the labeling order is: CD14-570, MPO-620, HLA-DR-690, CD68-480).

[0061] 11. Continue with step 8 to complete Opal Polaris780 fluorescent labeling of the above 5 target antibodies.

[0062] 12. Blocking: Rinse once with 1×TBST for 2 minutes, add blocking solution on the intestinal tissue, cover it completely, and block at room temperature for 10 minutes.

[0063] 13. Primary antibody incubation: dilute the antibody in proportion, drain the blocking solution, add CD206 to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 1 hour or at 4° overnight, then wash 3 times with 1× TBST, 2 minutes each time.

[0064] 14. Secondary antibody incubation: Add secondary antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 15 minutes, and then wash 3 times with 1× TBST, 2 minutes each time.

[0065] 15. TSA-DIG: Add Opal TSA-DIG working solution (1:100 dilution) onto each intestinal tissue, incubate the slides at room temperature for 10 min, and then wash three times with 1× TBST, 2 min each time.

[0066] 16. Repair: Place the intestinal tissue sections in a staining jar containing sufficient Tris-EDTA antigen repair solution. Before boiling, place the sections in an electric cooker and start the water boiling for 10 minutes, then switch to keep warm for 30 minutes. Cool to room temperature and rinse once with 1×TBST for 2 minutes.

[0067] 17. Add Opal Polaris780 to the intestinal tissue and incubate at room temperature for 1 hour. Then wash 3 times with 1× TBST, 2 minutes each time.

[0068] 18. Nuclear staining: Add the prepared DAPI onto the intestinal tissue sections, incubate at room temperature for 5 minutes, rinse with 1×TBST for 2 minutes, and wash with water for 2 minutes.

[0069] 19. Seal the film.

[0070] 20. Scan the results to form a scan result graph.

[0071] 21. Perform bioinformatics analysis based on image recognition on the scanned result image.

[0072] Preferably, the bioinformatics analysis includes frame selection, cell segmentation, cell clustering, cell annotation, cell proportion analysis, cell neighborhood analysis and cell interaction analysis, wherein the frame selection selects the ROI area as the bioinformatics analysis, and exports the image of each channel in the area as a segmentable TIFF image, and the cell segmentation uses a deepcell-based cell segmentation algorithm to perform cell segmentation on the TIFF image, exports the result as CSV format data, and constructs a segmented data information table to provide a data basis for subsequent analysis.

[0073] Preferably, the framed region refers to using Qupath (v0.4.4) software to frame a suitable number of regions (hereinafter referred to as ROIs) at suitable locations in each sample, and determining suitable grouping information for each ROI, so as to make the analysis result more accurate.

[0074] Preferably, the cell segmentation adopts the deepcell algorithm, locates the position of each cell nucleus based on the fluorescence expression of the DAPI channel, finds the cell boundary with the cell nucleus as the cell center, and finally obtains a complete cell information, and takes the average expression level of each protein marker in each cell as the expression level of the cell on each protein marker, which will be used for subsequent cell clustering and cell type judgment.

[0075] Preferably, the cell clustering means that after the cell segmentation is completed, due to the high background signal intensity of the multiple immunohistochemistry fluorescence scheme (mIHC), and the background signal intensity varies to varying degrees between different protein markers and even different samples, it is difficult to determine an overall threshold to remove the background signal intensity. The present invention adopts the CELESTA (v0.0.0.9000) algorithm to predict the probability of each protein marker being positive, and innovatively abandons the use of the original expression amount, and uses probability to cluster cells, thereby greatly reducing the impact of background signals on cell clustering and cell annotation, reducing the difficulty of cell annotation and the probability of incorrect annotation caused by false positive markers. In terms of clustering, the present invention uses FastPG (v0.0.8) to cluster cells.

[0076] Preferably, the cell annotation refers to outputting a heat map of protein marker positivity probabilities, so that the expression of each cell cluster on the six protein markers can be clearly known, and the cell cluster is annotated through the heat map of positivity probabilities to determine the cell type of each cell.

[0077] Preferably, after the cell annotation is completed, the present invention analyzes the cell type proportion, cell neighborhood, and interaction of each ROI.

[0078] Among them, the proportion of cell types: the present invention counts the proportion of each cell type in each ROI, and uses ggplot2 (v3.5.0) to draw a bar graph to clearly show the proportion of different cell types in each ROI. At the same time, each ROI is distinguished according to the grouping situation, so as to conveniently view the differences between the cell proportions in different groups. In addition, the present invention uses a t-test to test the differences in the proportions of the same cell types in different groups, and uses the p-value to determine whether there is a significant difference in the proportion of cells in different groups in a certain cell type. In statistics, we usually believe that p<=0.05 represents a significant difference between groups. The analysis is intuitively displayed in the form of a box plot.

[0079] Cell neighborhood analysis: The single-cell distribution obtained by clustering cells based on the environment can be interpreted as cellular neighborhoods (CN), which can represent the site of unique local processes. The present invention uses the buildSpatialGraph method in imcRtools (v1.6.5) to find the 20 nearest cells around each cell in a Knn manner, and uses the aggregateNeighbors method to count the cell type proportions of the 20 cells around each cell. The proportion is used as the spatial information of each cell, and the kmeans (k=15) algorithm is used to cluster the cells to obtain 15 different neighborhood structures (CN). By analyzing the proportion of different cell types in each neighborhood structure (CN), the corresponding cell enrichment can be obtained. The pheatmap method in pheatmap (v1.0.12) is used to display the cell enrichment in the form of a heat map. At the same time, the ggvoronoi (v0.8.6) package is used to draw the voronoi images of all neighborhood structures (CN) in each ROI to clearly display the CN situation in each ROI. By analyzing the proportion of the number of cells in each neighborhood structure (CN) to the total number of cells, a t-test was performed on the CN differences between different groups, the significance of the differences between different groups in different CNs was analyzed, and a box plot was drawn for better display.

[0080] Interaction relationship analysis: referred to as interaction analysis. The present invention uses the testInteractions() method in imcRtools (v1.6.5) to determine whether the interaction / avoidance between each cell type in each ROI occurs more frequently than random observation, thereby determining the type of interaction relationship between each cell, whether there is an interaction relationship (Interaction) or avoidance (Aviodance), and at the same time calculates the average number of each interaction pair around each cell and the multiples of interaction and avoidance to better display the interaction relationship between cell types, and draws a heat map to display the above information.

[0081] The beneficial effects of the present invention are:

[0082] (1) The panel designed in the present invention contains a variety of immune cell markers, covering the mononuclear phagocyte lineage, neutrophils (MPO) and antigen presenting cells (HLA-DR), as well as their analogs (substitutes), which can comprehensively evaluate the composition and function of different types of immune cells.

[0083] (2) The protein marker panel of the present invention can evaluate the inflammatory response and the presence and activity of inflammation-related cells by analyzing the expression of markers such as MPO and CD68, which helps to understand the occurrence and development mechanism of inflammatory diseases.

[0084] (3) The protein marker panel of the present invention can be used for antigen presentation assessment: HLA-DR and its analogs are markers of antigen presenting cells. Their detection can help assess the activity and function of antigen presenting cells and help understand the related mechanisms of immune response and immune regulation.

[0085] (4) The protein marker panel of the present invention can be used for immune regulation assessment: CD206 and its analogs are M2 markers of macrophages. Their detection can help assess the presence and function of immune regulatory cells and help understand aspects such as immune regulation and immune fatigue.

[0086] (5) The protein marker panel of the present invention can be used for immune identification: through quantitative and localization analysis of markers such as CD11b and CD14, different types of immune cells can be accurately identified and located, which is helpful for diagnosis and prognosis assessment.

[0087] (6) The present invention has important research and application value: The panel of the present invention has important research and application in the fields of inflammatory diseases, immune regulation and immune fatigue, and helps to gain a deeper understanding of the changes in the immune system under different disease states.

[0088] (7) The present invention can provide comprehensive information on the functional status, immune regulation and inflammatory response of immune cells by comprehensively analyzing the expression of one or more combinations of markers such as CD45, CD11b, CD11c, CD14, CD15, CD16, CD33, CD68, CD86, CD163, CD206, SPP1, FOLR2, C1QC, CD1c, HLA-DR, CD141, S100A9, S100A8, CD117, CD123, MPO, HLA-DR, etc., which helps to deeply understand the role of the immune system in the disease process and provide an important basis for personalized treatment and research.

[0089] (8) The present invention provides an important basis for tumor prognosis and prediction through scientific annotation of cell types and scientific analysis of cell proportions and spatial relationships. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a cell Umap image generated in the bioinformatics analysis of Example 2 of the present invention.

[0091] Figure 2 It is a distribution FeaturePlot image of the positive probability of each protein marker generated in the bioinformatics analysis of Example 2 of the present invention.

[0092] Figure 3 It is a heat map of each cell cluster generated in the bioinformatics analysis of Example 2 of the present invention.

[0093] Figure 4 It is a Umap image of a cell population after cell annotation in the bioinformatics analysis of Example 2 of the present invention.

[0094] Figure 5 It is a heat map of cell clusters after cell annotation in the bioinformatics analysis of Example 2 of the present invention.

[0095] Figure 6 It is a bar chart showing the proportion of each cell type within each ROI in the bioinformatics analysis of Example 2 of the present invention.

[0096] Figure 7 It is a bar chart showing the proportion of each cell type in ROIs of different groups in the bioinformatics analysis of Example 2 of the present invention.

[0097] Figure 8 It is a box plot of the overall proportion of different cell types in the bioinformatics analysis of Example 2 of the present invention.

[0098] Fig. 9It is a box plot of the significance of each cell type grouping in the bioinformatics analysis of Example 2 of the present invention (taking DC cells as an example, ratio = ratio).

[0099] Fig.10 It is a heat map of cell enrichment in the bioinformatics analysis of Example 2 of the present invention.

[0100] Fig.11 It is the voronoi image of all neighborhood structures (CN) in each ROI in the bioinformatics analysis of Example 2 of the present invention (taking BOWEL-M_3 as an example).

[0101] Fig.12 It is a box plot of the significant differences between different groups in different neighborhood structures (CN) in the bioinformatics analysis of Example 2 of the present invention.

[0102] Fig.13 It is a heat map of the interaction relationship between cell types in the bioinformatics analysis of Example 2 of the present invention.

[0103] Fig.14 This is a scanning result diagram of Example 3 of the present invention.

[0104] Fig.15 It is a cell Umap image generated in the bioinformatics analysis of Example 4 of the present invention.

[0105] Fig.16 It is a distribution FeaturePlot image of the positive probability of each protein marker generated in the bioinformatics analysis of Example 4 of the present invention.

[0106] Fig.17 It is a heat map of each cell cluster generated in the bioinformatics analysis of Example 4 of the present invention.

[0107] Fig.18 It is a Umap image of a cell population after cell annotation in the bioinformatics analysis of Example 4 of the present invention.

[0108] Fig.19 It is a heat map of cell clusters after cell annotation in the bioinformatics analysis of Example 4 of the present invention.

[0109] Fig. 20 It is a bar chart showing the proportion of each cell type within each ROI in the bioinformatics analysis of Example 4 of the present invention.

[0110] Fig.21 It is a bar chart showing the proportion of each cell type in ROIs of different groups in the bioinformatics analysis of Example 4 of the present invention.

[0111] Fig. 22 It is a box plot of the overall proportion of different cell types in the bioinformatics analysis of Example 4 of the present invention.

[0112] Fig.23 It is a box plot of the significance of each cell type grouping in the bioinformatics analysis of Example 4 of the present invention (taking DC cells as an example, ratio = ratio).

[0113] Fig.24 It is a heat map of cell enrichment in the bioinformatics analysis of Example 4 of the present invention.

[0114] Fig.25 It is the voronoi image of all neighborhood structures (CN) in each ROI in the bioinformatics analysis of Example 4 of the present invention (taking T001-ROI001 as an example).

[0115] Fig.26 It is a box plot of the significant differences between different groups in different neighborhood structures (CN) in the bioinformatics analysis of Example 4 of the present invention.

[0116] Fig. 27 It is a heat map of the interaction relationship between cell types in the bioinformatics analysis of Example 4 of the present invention. DETAILED DESCRIPTION

[0117] In order to enable those skilled in the art to better understand and implement the technical solution of the present invention, the present invention is specifically, clearly and completely described in the following embodiments in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Moreover, based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work, any modifications, equivalent substitutions, improvements, etc., should be included in the protection scope of the present invention.

[0118] In order to enable those skilled in the art to better understand the technical solution of the present invention, some technical terms of the present invention are first explained. In the text of the present invention, these explanations are mainly to help those skilled in the art understand and implement the technical solution of the present invention, rather than to limit the present invention. In addition, there may be other technical understandings of these technical terms in other disclosures outside the present invention. In order to avoid technical ambiguity, when understanding the present invention, these explanations of the present invention should be based on them.

[0119] Myeloid cells refer to a series of white blood cells produced in the bone marrow, including granulocytes, monocytes and macrophages. These cells originate from hematopoietic stem cells and undergo a series of differentiation and maturation processes to form mature white blood cells, which are responsible for the body's immune defense and inflammation regulation. In the present invention, the markers for identifying myeloid cells include one or more of the following combinations: CD45, CD11b, CD11c, CD14, CD15, CD16, CD33, CD68, CD86, CD163, CD206, SPP1, FOLR2, C1QC, CD1c, HLA-DR, CD141, S100A9, S100A8, CD117, CD123, MPO, HLA-DR.

[0120] CD45: Mainly expressed on all white blood cells. CD45 is an important phosphatase that is involved in regulating signal transduction of T cells and B cells. It plays a key role in the activation, proliferation and differentiation of immune cells. The expression level of CD45 can be used to distinguish different types of white blood cells.

[0121] CD11b: is an integrin, also known as Mac-1 or ITGAM, and is a marker on the surface of monocytes, macrophages, and neutrophils. The detection of CD11b can help identify and quantify the presence and distribution of monocytes, macrophages, and neutrophils in tissues. These cells play an important role in inflammatory responses, infections, immune responses, and tissue repair. Therefore, the detection of CD11b can help pathologists understand the pathophysiological process of the disease and guide the diagnosis and formulation of treatment plans. CD11c: Mainly expressed on dendritic cells and monocytes. CD11c is one of the markers of dendritic cells and is involved in antigen presentation and cell-to-cell adhesion. It plays a key role in the initiation and regulation of immune responses.

[0122] CD14: Commonly used to detect helper T cells. CD4 is a membrane receptor molecule that is mainly expressed on the surface of helper T cells. Helper T cells play a key role in the immune system. They assist and regulate immune responses by interacting with antigen-presenting cells. The detection of CD4-positive cells can help pathologists determine the presence and distribution of helper T cells in tissues. In pathological diagnosis, the detection of CD4 can be used to identify the immune cell infiltration pattern of specific diseases, such as autoimmune diseases, infectious diseases, and tumors. The location and counting of CD4-positive cells can help doctors further understand the pathophysiological mechanism of the disease, thereby guiding the selection of treatment options and evaluating the prognosis of the disease. CD15: Mainly expressed on neutrophils and some tumor cells. CD15 is a sugar chain antigen that participates in cell-to-cell adhesion and signal transduction. It plays a role in inflammation, tumor metastasis, and the homing of immune cells.

[0123] CD16: Mainly expressed on natural killer cells and some monocytes. CD16 is the Fc receptor of immunoglobulin G (IgG), participates in antibody-dependent cell-mediated cytotoxicity (ADCC), and plays an important role in the clearance of viral infection and tumor cells.

[0124] CD33: Mainly expressed on myeloid leukemia cells and some myeloid precursor cells. CD33 is a myeloid cell marker that participates in cell-to-cell adhesion and signal transduction. It is of great significance in the diagnosis and treatment of leukemia.

[0125] CD68: Usually used to detect macrophages. Macrophages are an important immune cell that plays an important role in inflammation, infection, tumors and tissue repair. CD68 is one of the markers of macrophages. The detection of CD68 can help pathologists determine the location, number and activity of macrophages in tissues, thereby understanding the diagnosis and pathophysiological process of the disease.

[0126] CD86: Mainly expressed on antigen presenting cells, such as dendritic cells and B cells. CD86 is a co-stimulatory molecule that is essential for the activation of T cells. It provides a second signal by binding to CD28 on the surface of T cells, promoting the initiation and regulation of immune responses.

[0127] CD206: It is a macrophage-specific receptor, usually used to mark M2 macrophages in macrophages. M2 macrophages are a subtype of macrophages with immunoregulatory properties, which are closely related to processes such as inflammation regulation, repair and regeneration. In pathological histology, the detection of CD206 is mainly used to identify and quantify the presence and activity status of M2 macrophages. These cells play an important role in physiological and pathological processes such as inflammation regulation, tissue repair and immune regulation. By detecting CD206-positive cells, pathologists can evaluate the distribution and number of M2 macrophages in tissues, so as to better understand the pathophysiological mechanisms and prognosis of the disease. Therefore, the detection of CD206 helps pathologists evaluate the disease status and guide the diagnosis and treatment strategies of related diseases.

[0128] CD163: It is a type I membrane protein, also known as M130 antigen, which is mainly expressed in the mononuclear cell system and macrophage system. It is a marker of monocytes and macrophages, such as dendritic cells in the spleen and alveolar macrophages, and is mainly used for the detection of monocytes and tissue cells in tumors and reactive lesions.

[0129] SPP1: Widely expressed on multiple cell types, it is used for the classification of macrophages. SPP1 (osteopontin) is involved in bone formation and remodeling, and also plays a role in inflammation and tumor metastasis, and can act as a regulator of cell adhesion and migration.

[0130] FOLR2: can be used for macrophage typing. FOLR2 (folate receptor β) is involved in the transport and metabolism of folic acid and has an impact on cell proliferation and immune response. FOLR2 can be used as a target in tumor diagnosis and treatment.

[0131] C1QC: Mainly expressed on macrophages and monocytes. C1QC is part of the complement system and is involved in the activation of the classical complement pathway. It plays a role in immune defense and inflammatory processes.

[0132] CD1c: Mainly expressed on dendritic cells. CD1c is a member of the CD1 molecule family, involved in the presentation of lipid antigens to T cells, and plays an important role in the immune response to certain bacterial and fungal infections.

[0133] HLA-DR: It is an antigen mainly expressed on the surface of immune cells. HLA-DR is usually used to detect professional antigen-presenting cells with antigen presentation and immunomodulatory functions, mainly mature and activated cells in the monocyte series. Especially in disease states such as inflammation, infection, autoimmune diseases and tumors, HLA-DR-positive cells play an important role in tissues. These cells include macrophages, dendritic cells and B lymphocytes, which play a role in immune response and antigen presentation. By detecting HLA-DR-positive cells, pathologists can evaluate the activity state of immune cells, the degree of immune response and the immunopathological characteristics of the disease, so as to better understand the pathogenesis and progression of the disease. CD141: Mainly expressed on specific dendritic cell subsets. CD141 is a subset marker of dendritic cells. These cells play an important role in the capture and presentation of antigens, especially in the immune response to viruses and tumor antigens.

[0134] S100A9: Mainly expressed on neutrophils and monocytes. S100A9 is a member of the S100 calcium-binding protein family and is involved in regulating inflammatory responses and apoptosis. It forms a complex with S100A8 and plays a role in host defense and inflammatory diseases.

[0135] S100A8: Mainly expressed on neutrophils and monocytes. S100A8 is also a member of the S100 calcium-binding protein family and, together with S100A9, is involved in inflammation regulation and apoptosis. The complex of these two proteins plays an important role in inflammatory and infectious diseases.

[0136] CD117: Mainly expressed on hematopoietic stem cells, progenitor cells and some tumor cells. CD117 (stem cell factor receptor) is a tyrosine kinase receptor that is essential for the proliferation and differentiation of hematopoietic stem cells. In pathology, CD117 is a diagnostic marker for certain tumors, such as gastrointestinal stromal tumors.

[0137] CD123: Mainly expressed on plasmacytoid dendritic cells and certain acute myeloid leukemia cells. CD123 (IL-3 receptor α chain) is a subunit of the cytokine receptor and is important for the diagnosis and treatment of certain types of leukemia.

[0138] MPO: is an enzyme specific to granulocytes. MPO is usually used to detect granulocytes, including neutrophils (neutrophils) and eosinophils. MPO is an important enzyme in granulocytes, and its detection can help determine the presence and activity of granulocytes in tissues. Granulocytes play an important role in the course of diseases such as inflammation, infection, and tumors. The detection of MPO helps to assess the degree of inflammation in tissues, the infiltration of granulocytes, and the pathophysiological mechanisms of the disease. Therefore, by detecting MPO-positive cells, we can better understand the situation of granulocytes in tissues and provide important information for the diagnosis and treatment of diseases.

[0139] Example 1, preparation of the first panel.

[0140] In this embodiment, the experimental panel is shown in Table 1.

[0141] The production steps are as follows (taking intestinal tissue as an example):

[0142] (1.1) Dewaxing of baked slices: bake the slices at 62℃ for 1.5 hours; dewaxing: xylene for 10 minutes, xylene for 10 minutes, 95% ethanol for 5 minutes, 90% ethanol for 5 minutes, 85% ethanol for 5 minutes, 80% ethanol for 5 minutes. Wash with water 3 times, 5 minutes each time.

[0143] (1.2) Fixation: Add 4% paraformaldehyde fixative to completely cover the tissue, fix for 15-20 minutes, and wash with water three times, 2 minutes each time.

[0144] (1.3) Repair: Place the slide containing intestinal tissue in a staining jar containing sufficient Tris-EDTA antigen repair solution, and place it in an electric rice cooker before boiling. After the water boils, start timing for 10 minutes, then switch to keep warm for 30 minutes, and cool at room temperature. Wash with PBS 3 times, 5 minutes each time.

[0145] (1.4) Blocking: Add 3% hydrogen peroxide and incubate in dark for 25 minutes. Wash with PBS three times, 5 minutes each time.

[0146] (1.5) Sealing: Use a histochemical pen to outline the intestinal tissue specimen, add a drop of sealing solution on the intestinal tissue, cover it completely, and seal it at room temperature for 10 minutes.

[0147] (1.6) Primary antibody incubation: dilute the antibody in proportion, drain the blocking solution, add CD11b antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 1 hour or at 4° overnight. Then wash 3 times with 1X TBST, 2 minutes each time.

[0148] (1.7) Secondary antibody incubation: Add secondary antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 15 min. Then wash 3 times with 1× TBST, 2 min each time.

[0149] (1.8) Fluorescence incubation: dilute the fluorescence at 1:100, add fluorescence 520, incubate at room temperature for 15 min, and then wash 3 times with 1× TBST, 2 min each time.

[0150] (1.9) Repair: Place the slide containing intestinal tissue into a staining jar containing sufficient Tris-EDTA antigen repair solution, and place it in an electric rice cooker before boiling. After the water boils, start timing for 10 minutes, then switch to keep warm for 30 minutes, and cool at room temperature. Rinse once with 1× TBST for 2 minutes.

[0151] (1.10) Restart steps (1.5)-(1.9) until all five targets are labeled and then stop repeating the steps (the labeling order of the other four target antibodies is: CD14-570, MPO-620, HLA-DR-690, CD68-480).

[0152] (1.11) Continue with step (1.8) to complete Opal Polaris780 fluorescent labeling of the above 5 target antibodies.

[0153] (1.12) Blocking: Rinse once with 1× TBST for 2 min. Add blocking solution dropwise onto the intestinal tissue to cover it completely, and block at room temperature for 10 min.

[0154] (1.13) Primary antibody incubation: dilute the antibody in proportion, drain the blocking solution, add CD206 to completely cover the tissue sections, incubate at room temperature in the dark for 1 hour or at 4° overnight. Then wash 3 times with 1× TBST, 2 minutes each time.

[0155] (1.14) Secondary antibody incubation: Add secondary antibody to completely cover the intestinal tissue sections, incubate at room temperature in the dark for 15 min. Then wash 3 times with 1× TBST, 2 min each time.

[0156] (1.15) TSA-DIG: Add Opal TSA-DIG working solution (1:100 dilution) to each intestinal tissue and incubate the slides at room temperature for 10 min. Then wash three times with 1× TBST, 2 min each time.

[0157] (1.16) Repair: Place the slide containing intestinal tissue into a staining jar filled with sufficient Tris-EDTA antigen repair solution, and place it in an electric cooker before boiling. After the water boils, start timing for 10 minutes, then switch to keep warm for 30 minutes, and cool at room temperature. Rinse once with 1× TBST for 2 minutes.

[0158] (1.17) Add Opal Polaris780 dropwise onto the intestinal tissue and incubate at room temperature for 1 hour. Then wash three times with 1× TBST, 2 minutes each time.

[0159] (1.18) Nuclear staining: Add the prepared DAPI dropwise onto the tissue slide and incubate at room temperature for 5 min. Rinse with 1× TBST for 2 min and wash with water for 2 min.

[0160] (1.19) Seal the slides.

[0161] (1.20) Result scanning: Use Vectra Opal Polaris quantitative pathology imaging system to scan tissue sections and obtain scanning result images.

[0162] Example 2, bioinformatics analysis of the first panel.

[0163] (2.1) Use Qupath (v0.4.4) software to open the qptiff image (scanning result image) of each intestinal tissue obtained according to step (1.20) of Example 1, and select a suitable area on the image as the region of bioinformatics analysis (hereinafter referred to as ROI) according to the staining of the sample tissue. Export each channel image of each region as an image in tiff format for cell segmentation in step (2.2).

[0164] (2.2) Cell segmentation: Use the deepcell-based cell segmentation algorithm to perform cell segmentation on the TIFF image exported in step (2.1), extract the fluorescence intensity (expression level) of each cell after segmentation at different protein markers, and export the results as CSV format data.

[0165] (2.3) Construct data information tables: Construct data information tables info.csv (Table 3) and marker.csv (Table 4) for bioinformatics analysis based on sample scanning and marker conditions.

[0166] Table 3

[0167]

[0168] Table 4

[0169]

[0170] As can be seen from Table 3, the info.csv file contains the following specific information: sample_id (sample name), roi_id (sample-ROI information), batch_id (batch information), roi_area (area of ​​each ROI, in square millimeters), group (group information set according to ROI).

[0171] As can be seen from Table 4, the marker.csv file contains the following specific information: channel (channel name), marker (corresponding protein information), lineage (protein type information involved in the calculation): 1 means participating in subsequent clustering calculations and visualization, usually a Lineage Marker (lineage marker, usually used to identify and determine the lineage affiliation of cells); 0 means not participating in subsequent clustering calculations or visualization.

[0172] (2.4) Import the csv, info.csv, and marker.csv containing the cell information of each ROI into R (v4.3.1), and construct a data structure object spe of the SpatialExperiment type (SpatialExperiment v1.10.0, SingleCellExperiment v1.22.0, imcRtools v1.6.5) to store the cell information, protein expression information, grouping information, batch information, ROI area, and other information stored in the info table for each ROI.

[0173] (2.5) Prediction of the probability of positive cell protein expression: In order to remove the interference of the strong background signal of the fluorescence method itself, an algorithm based on CELESTA (v0.0.0.9000) was used to predict the probability of each protein marker being positive.

[0174] (2.6) Cell clustering: Based on the probability of each protein marker being positive calculated in step (2.5), the fastCluster method in the FastPG (v0.0.8) package was used to cluster cells. The runUMAP method in scater (v1.28.0) was used to calculate the Umap image coordinates of each cell. The cell clustering results were plotted using the dittoDimPlot method in the dittoSeq (v1.12.2) package to plot the Umap image of the cell cluster. Figure 1 ), the distribution of each protein marker positive probability FeaturePlot image ( Figure 2 ) and use the Heatmap method in the ComplexHeatmap (v2.16.0) package to draw a heat map of each cell cluster ( Figure 3 )(expression value=expression value).

[0175] (2.7) Cell annotation: Annotate the cell clusters according to the immunology database and map the cell annotations to each cell cluster in the spe data structure. The same as in step (2.6), draw the Umap image of the annotated cell cluster ( Figure 4 ) and heatmap ( Figure 5 ).

[0176] (2.8) Cell ratio analysis: Based on the cell annotation results completed in step (2.7), analyze the ratio of each cell type within each ROI and draw a bar graph using ggplot2 (v3.5.0) ( Figure 6 ) to visually display the results, and according to the grouping information determined in Table 2, analyze the proportion of each cell type in the ROI of different groups, using the grouped bar chart ( Figure 7 ) to display the result. In this embodiment, it can be clearly found that BOWEL-M_4 and BOWEL-M_0 have a higher CD206-Macrophage ratio than the remaining three ROIs, while BOWEL-M_1 has a higher CD206+Macrophage ratio.

[0177] To show the differences between the proportions of different cell types in the whole, this example uses ggplot2 to draw a box plot ( Figure 8 ), and sorted according to the median size of each cell type. In this embodiment, it can be seen from the box plot that in myeloid cells, the median of CD206+Macrophage in all ROIs presents a larger value. According to the grouping of ROI, the t-test was used to calculate the significant difference p value of each cell type in different groups. Statistics generally believe that when p<0.05, there is a significant difference between two groups. The box plot ( Fig. 9 ) The p-value of the significant difference is displayed.

[0178] (2.9) Cell neighborhood (CN) analysis: In this example, the buildSpatialGraph method in imcRtools (v1.6.5) was used to find the 20 cells closest to each cell, and the aggregateNeighbors method was used to count the cell type ratio of the 20 cells around each cell. This ratio was used as the spatial information of each cell, and the kmeans algorithm was used to cluster the cells to construct 15 different neighborhood structures (CN). By analyzing the ratio of different cell types in each neighborhood structure (CN), the corresponding cell enrichment can be obtained. The pheatmap method in pheatmap (v1.0.12) is used to display the cell enrichment through a heat map ( Fig.10). As can be seen from the heat map, CD206-Macrophage is mostly enriched in CN9, CN14, CN6, and CN10, and Granulocyte is enriched in CN5. At the same time, the ggvoronoi (v0.8.6) package was used to draw the voronoi image of all neighborhood structures (CN) in each ROI ( Fig.11 ), to clearly show the CN situation in each ROI. By analyzing the proportion of the number of cells in each neighborhood structure (CN) to the total number of cells, according to the grouping provided in step (2.3), a t-test was performed on the CN differences between different groups to analyze the significance of the differences in different CNs between different groups, and the box plot ( Fig.12 ) to better display.

[0179] (2.10) Interaction analysis: This example uses the testInteractions() function in imcRtools (v1.6.5) to determine whether the interaction / avoidance between each cell type occurs more frequently than random observation, thereby determining the type of interaction between each cell, whether there is an interaction (Interaction) or avoidance (Aviodance), and at the same time calculating the average number of each interaction pair around each cell and the multiple of interaction and avoidance to better display the interaction relationship between cell types ( Fig.13 ). In this example, it can be clearly found that in addition to the interaction between cells of the same type, there is a strong interaction relationship between Granulocyte and MPO+Macrophage between different cell types.

[0180] Example 3, preparation of the second panel.

[0181] In this embodiment, the experimental panel is shown in Table 2.

[0182] The preparation steps of this example refer to those of Example 1, wherein CD206 is replaced by CD163 in the primary antibody incubation in step (1.13), and the other preparation steps are the same as those of Example 1.

[0183] In this embodiment, the scanning results are as follows: Fig.14 shown.

[0184] Example 4, bioinformatics analysis of the second panel.

[0185] (4.1) Use Qupath (v0.4.4) software to open the qptiff image of each tissue obtained in Example 3 (as shown in Figure 4). Fig.14As shown in the figure, select a suitable area on the image as the region for bioinformatics analysis (hereinafter referred to as ROI) according to the staining of the sample tissue. Export each channel image of each region as a tiff format image for cell segmentation in step (4.2).

[0186] (4.2) Cell segmentation: Use the deepcell-based cell segmentation algorithm to perform cell segmentation on the TIFF image exported in step (4.1), extract the fluorescence intensity (expression level) of each cell after segmentation at different protein markers, and export the results as CSV format data.

[0187] (4.3) Construct data information tables: Construct data information tables info.csv (Table 5) and marker.csv (Table 6) for bioinformatics analysis based on sample scanning and marker conditions.

[0188] Table 5

[0189]

[0190] Table 6

[0191]

[0192] As can be seen from Table 5, the info.csv file contains the following specific information: sample_id (sample name), roi_id (sample-ROI information), batch_id (batch information), roi_area (area of ​​each ROI, in square millimeters), group (group information set according to ROI).

[0193] As can be seen from Table 6, the marker.csv file contains the following specific information: channel (channel name), marker (corresponding protein information), lineage (protein type information involved in the calculation): 1 means participating in subsequent clustering calculations and visualization, usually a Lineage Marker (lineage marker, usually used to identify and determine the lineage affiliation of cells); 0 means not participating in subsequent clustering calculations or visualization.

[0194] (4.4) Import the csv, info.csv, and marker.csv containing the cell information of each ROI into R (v4.3.1), and construct a data structure object spe of the SpatialExperiment type (SpatialExperiment v1.10.0, SingleCellExperiment v1.22.0, imcRtools v1.6.5) to store the cell information, protein expression information, grouping information, batch information, ROI area, and other information stored in the info table for each ROI.

[0195] (4.5) Prediction of the probability of positive cell protein expression: In order to remove the interference of the strong background signal of the fluorescence method itself, an algorithm based on CELESTA (v0.0.0.9000) was used to predict the probability of each protein marker being positive.

[0196] (4.6) Cell clustering: Based on the probability of each protein marker being positive calculated in step (4.5), use the fastCluster method in the FastPG (v0.0.8) package to cluster cells. Use the runUMAP method in scater (v1.28.0) to calculate the Umap image coordinates of each cell. Use the dittoDimPlot method in the dittoSeq (v1.12.2) package to plot the Umap image of the cell cluster. Fig.15 ), the distribution of each protein marker positive probability FeaturePlot image ( Fig.16 ) and use the Heatmap method in the ComplexHeatmap (v2.16.0) package to draw a heat map of each cell cluster ( Fig.17 )(expressionvalue=expressionvalue).

[0197] (4.7) Cell Annotation: Professional immunology experts annotate the cell clusters and map the cell annotations to each cell cluster in the spe data structure. The same as in step (4.6), draw the Umap image of the annotated cell cluster ( Fig.18 ) and heatmap ( Fig.19 ).

[0198] (4.8) Cell proportion analysis: Based on the cell annotation results completed in step (4.7), analyze the proportion of each cell type within each ROI and use ggplot2 (v3.5.0) to draw a bar graph ( Fig. 20) to visually display the results, and analyze the proportion of each cell type in the ROI of different groups according to the grouping information determined by the info table in Table 5, using the grouped bar chart ( Fig.21 ) to display the result. In this embodiment, it can be clearly found from the grouped bar graph that the three ROIs T001_ROI003, T001_ROI005 and T001_ROI006 have a higher CD163+Macrophage ratio compared to the remaining three ROIs. From the perspective of grouping, the ROI of Group A has a higher MPO+Macrophage cell ratio, and Group C has a higher CD163+Macrophage cell ratio.

[0199] To show the differences between the proportions of different cell types in the whole, this example uses ggplot2 to draw a box plot ( Fig. 22 ), and sorted according to the median size of each cell type. In this embodiment, it can be seen from the box plot that in myeloid cells, the median of CD163+Macrophage in all ROIs presents a larger value. According to the grouping of ROI, the t-test was used to calculate the significant difference p value of each cell type in different groups. Statistics generally believe that when p<0.05, there is a significant difference between two groups. The box plot ( Fig.23 )The p value of the significant difference is displayed. It can be seen from this embodiment that CD163+Macrophage shows an upward trend from Group A, Group B, and Group C. At the same time, Group A and Group C show significant differences in the proportion of CD163+Macrophage.

[0200] (4.9) Cell neighborhood (CN) analysis: In this example, the buildSpatialGraph method in imcRtools (v1.6.5) was used to find the 20 cells closest to each cell, and the aggregateNeighbors method was used to count the cell type ratio of the 20 cells around each cell. This ratio was used as the spatial information of each cell, and the kmeans algorithm was used to cluster the cells to construct 10 different neighborhood structures (CN). By analyzing the ratio of different cell types in each neighborhood structure (CN), the corresponding cell enrichment can be obtained. The pheatmap method in pheatmap (v1.0.12) is used to display the cell enrichment through a heat map ( Fig.24). As can be seen from the heat map, MPO+Macrophage is mostly enriched in CN9, CN14, CN2, and CN3, APM is enriched in CN5, and CD163+Macrophage is mainly enriched in CN1 and CN10. At the same time, the ggvoronoi (v0.8.6) package was used to draw the voronoi image of all neighborhood structures (CN) in each ROI ( Fig.25 ), to clearly show the CN situation in each ROI, and the tissue structure of each ROI can be explored through the CN grouping in the voronoi image. By analyzing the proportion of the number of cells in each neighborhood structure (CN) to the total number of cells, according to the grouping provided in Table 4 and Table 5 in step (4.3), a t-test was performed on the CN differences between different groups to analyze the significance of the differences between different groups in different CNs, and the box plot ( Fig.26 ) to better display. Fig.26 It can be seen that the proportion of CN7 shows an upward trend from Group A to C, and there is a significant difference between the two groups.

[0201] (4.10) Interaction analysis: This example uses the testInteractions() function in imcRtools (v1.6.5) to determine whether the interaction / avoidance between each cell type occurs more frequently than random observation, thereby determining the type of interaction between each cell, whether there is an interaction (Interaction) or avoidance (Aviodance), and at the same time calculating the average number of each interaction pair around each cell and the multiple of interaction and avoidance to better display the interaction between cell types ( Fig. 27 ). Through the interaction relationship diagram, it can be clearly found that in addition to the interaction between cells of the same type, there is an interactive relationship between APM and CD163-Macrophage between different cell types, while there is an avoidance relationship between other cell types.

[0202] Although the preferred embodiments of the present invention have been described in the specification, once the basic creative concept is known to those skilled in the art, they may make additional changes and modifications to these embodiments or combinations of embodiments without creative work. Therefore, the protection scope of the present invention should be understood as the scope that the claims are intended to explain and cover, and should not be limited to the parts described in detail in the specification and embodiments of the present invention, and the contents described in the present invention, including the preferred embodiments, include all changes and modifications that fall within the scope of the present invention.

Claims

1. A method for detecting myeloid cells, characterized in that: The detection method uses a protein marker group, or a kit comprising the protein marker group, wherein the protein marker group is selected from at least one of CD68, CD11b, CD14, MPO, HLA-DR, CD206 and CD68, CD11b, CD14, MPO, HLA-DR, CD163, and the detection method specifically comprises the following steps: (1) Dewaxing of baked sheets, (2) Cell tissue fixation and repair, (3) Blocking: use a tissue pen to circle the tissue specimen and perform tissue sealing; (4) Antibody and fluorescence incubation; (5) Repeat (2)-(4) until all target antibodies are incubated; (6) Nuclear staining and sealing; (7) Scanning the results to form a scanning result graph; (8) Perform bioinformatics analysis based on image recognition on the scanned image; The bioinformatics analysis in step (8) includes box selection, cell segmentation, cell clustering, and cell annotation, wherein the box selection selects the ROI area as the bioinformatics analysis, and exports the image of each channel in the area as a segmentable TIFF image. The cell segmentation uses a deepcell-based cell segmentation algorithm to perform cell segmentation on the TIFF image, exports the result as CSV format data, and constructs a segmented data information table to provide a data basis for subsequent analysis.

2. A method for detecting myeloid cells according to claim 1, characterized in that: The bioinformatics analysis in step (8) also includes at least one of cell proportion analysis, cell neighborhood analysis and cell interaction analysis.

3. A method for detecting myeloid cells according to claim 1, characterized in that: The protein marker panel was applied to all available fluorescent channels, including DAPI, 480, 520, 540, 570, 620, 650, 690, 780.

4. A method for detecting myeloid cells according to claim 1, characterized in that: The objects to be tested for the protein marker group are tissue cells.

Images