Kidney pathology detection kit, detection method and application

Abstract

The invention belongs to the technical field of kidney histopathology and immunological evaluation, and particularly relates to a kidney pathology detection kit, a detection method and application, the detection kit comprises 37 rare metal element labeled monoclonal antibodies combined with antigens of in-situ tissue cells, and the detection method comprises the following steps: (1) collecting a sample to be detected, preparing a pre-dyed paraffin section; (2) mixing 37 monoclonal antibodies labeled by rare metal elements, and carrying out antibody staining on various antigen targets on the surfaces of cells in the in-situ tissue; (3) carrying out nucleus DNA staining on the stained tissue sample; and (4) detecting the stained tissue sample through an imaging mass spectrum flow system, and collecting original information data through laser spot firing. The method is an analysis method for fine grouping identification and relative quantification of structural cells, immune cells and pathological injury cells in the clinical nephropathy sample in-situ tissue. And auxiliary diagnosis information is provided for diagnosis, grading, treatment and prognosis detection of various clinical nephropathy.

Description

Technical Field

[0001] The invention belongs to the technical field of kidney tissue pathology and immunology evaluation, and specifically relates to a kidney pathology detection kit, a detection method and an application. Background Art

[0002] The kidney is an important filtering organ in the human body, mainly responsible for filtering blood, removing waste and excess water, and regulating electrolyte and acid-base balance. The kidney function is completed by millions of nephrons, which are composed of glomeruli, proximal tubules, thin branches of the loop of Henle, distal tubules, connecting ducts and collecting ducts. At present, in the field of nephrology, studies have found that nephrons can be located and distinguished by some specifically expressed markers. For example, Nestin is used to mark the podocytes in the glomerulus, Aquaporin 1 is used to mark the proximal tubules, Uromodulin is used to mark the ascending branches of the loop of Henle, Cadherin is used to mark the distal convoluted tubules, Cytokeratin 7 is used to mark the connecting ducts, and Aquaporin 2 is used to mark the collecting ducts.

[0003] When acute or chronic damage occurs to the human kidney, a series of pathological indicators will appear in clinical examinations. The most direct pathological manifestation is renal unit damage, including the loss of podocytes in the glomerulus, mesangial cell proliferation, tubular atrophy or expansion, increased local immune cell infiltration, enhanced inflammation, extracellular matrix deposition, and enhanced renal interstitial fibrosis. With the development of kidney disease, the degree of renal unit damage increases, and eventually develops into end-stage renal disease. Current pathological studies have also found many kidney injury-related markers, such as upregulation of KIM-1 expression in renal tubular cells after acute kidney injury; in chronic kidney disease, multiple signaling pathways are involved in the progression of kidney disease. For example, in diabetic nephropathy, abnormal activation of the TGF-β1 / SMAD signaling pathway promotes the development of renal fibrosis, including the activation and proliferation of fibroblasts and myofibroblasts, and the promotion of extracellular matrix deposition, such as collagen, fibronectin, and glycoprotein Tenascin-C (TNC).

[0004] The human immune system is a defense system that identifies and resists foreign pathogens and maintains tolerance of self-tissues. It is a complex network structure composed of a variety of immune cells, tissues and organs. Among them, immune cells mainly include myeloid and lymphoid systems. The myeloid system includes monocytes and granulocytes, including macrophages, dendritic cells, neutrophils, basophils, eosinophils, and the lymphoid system includes B lymphocytes, T lymphocytes and natural killer T cells.

[0005] In the field of immunology, researchers have identified more than a hundred cell surface markers, which are called differentiation antigens, or CD molecules. These CD molecules represent specific antigens that appear or disappear during the differentiation, maturation, and activation of immune cells. Most of them are adhesion molecules on the surface of immune cells, as well as receptors for cytokines and chemokines. Specific antibodies can recognize these antigens.

[0006] The expression of these CD molecules on various immune cells is significantly different, which makes it possible to finely classify immune cells. For example, T cells can be further subdivided into multiple subgroups based on the CD molecules on their surface, including initial T cells, memory T cells, effector T cells, etc. The classification of monocytes helps to better understand the functions and effects of immune cells.

[0007] Immune cell typing is closely related to various kidney diseases. For example, in lupus nephritis (LN) and primary membranous nephropathy (MN), immune complexes are deposited on the basement membrane of the kidney, activating the complement system, leading to inflammation and damage; immune cells such as macrophages and T cells release inflammatory factors such as tumor necrosis factor (TNF) and interleukins (ILs) in the kidney, which can directly damage kidney cells; in acute interstitial nephritis, activated T cells can invade the renal interstitium and attack renal cells; antibodies produced by B cells can form immune complexes and deposit in the kidney, activating the complement system, which can promote inflammation, attack renal cells, and cause renal tissue damage. Typing and relative quantification of immune cell subsets infiltrating local renal tissues, as well as their spatial distribution in the nephron, are of great significance for the clinical diagnosis, pathological grading, pathogenesis exploration, and target development of therapeutic drugs for various kidney diseases.

[0008] At present, renal puncture biopsy is the gold standard for the diagnosis and pathological grading of kidney diseases. Pathological detection of renal puncture is performed through pathological methods such as H&E staining, immunohistochemistry, and immunofluorescence. Among them, H&E staining is based on the staining of cell nuclei and cytoplasm, and can only observe the lesions of renal tissue from a morphological level, and it is difficult to accurately distinguish cell types and cell phenotypes; immunohistochemistry experiments are based on antigen-antibody reactions, and detect a marker carried by a certain type of population of cells. This method cannot detect multiple cell markers at the same time, has a narrow detection range, and is complicated to operate. In addition, in renal tissue, secondary antibody staining of antibodies is prone to false positives, which is not conducive to accurate clinical judgment and mechanism research.

[0009] Multiple immunofluorescence experiments are also based on antigen-antibody reactions. They can detect multiple markers (no more than 10) of several cells and perform low-dimensional analysis of the cell phenotype, spatial distribution, and cell interaction of several types of cells in in situ tissues. However, there is overlap between the emission spectra of these fluorescent signals, which are prone to interfere with each other and even produce false positive signals, leading to detection errors. Although the acquisition of signals can be corrected by adjusting the fluorescence compensation of the laser confocal, there are differences in system operation due to manual and subjective judgment adjustments, and it varies from person to person, making it difficult to ensure the authenticity of the results. In addition, paraffin samples have spontaneous fluorescence, which will interfere with the results to a certain extent during detection and analysis.

[0010] In order to avoid interference from fluorescent channels, current clinical research often uses a combination of antibodies labeled with 3-4 fluorescent groups to conduct experiments to ensure the reliability of the analysis, and it is impossible to detect dozens of marker molecules on the same pathological section sample. When multiple indicators need to be detected simultaneously, it is necessary to split into multiple groups and use continuous sections for staining analysis. This method causes the original single sample detection to be transformed into multiple sample detection, which not only increases the operation time and sample consumption, but also causes a waste of detection reagents and reduces detection efficiency. In addition, the quality of continuous sections tests the difficulty of operation, and the staining of different samples amplifies the system operation error, which increases the difference in test results and is not conducive to subsequent analysis.

[0011] Due to the complex structure of renal tissue, and in the process of renal disease, the types and quantity of cell surface antigens expressed by renal unit structural cells are changing due to damage; the types of immune cells infiltrating local tissues are complex and the cell phenotypes vary greatly; at the same time, various renal pathological markers are expressed in different intensities and spatial regions. Therefore, how to display the full cell view of renal pathological tissues by detecting the types and expression intensities of cell surface antigens, and accurately assist clinical diagnosis and pathological grading is an urgent problem to be solved. Summary of the invention

[0012] In order to solve the problems in the prior art, the present invention provides a kidney pathology detection kit, a detection method and an application, which detect multiple indicators on a single-layer cell paraffin section and are used for single-cell panoramic drawing of the local pathological microenvironment of the kidney, thereby achieving the goals of high throughput, simple operation and accurate data.

[0013] The present invention solves the technical problem by adopting the following technical solutions:

[0014] The present invention aims to provide a kidney pathology detection kit, comprising 37 rare metal element labeled monoclonal antibodies that bind to antigens of in situ tissue cells, the 37 rare metal element labeled monoclonal antibodies comprising 89Y labeled β-catenin, 139La labeled HistoneH3, 141Pr labeled CD38, 142Nd labeled CD56, 143Nd labeled Vimentin, 144Nd labeled CD14, 145Nd labeled CD31, 146Nd labeled Aquaporin-2, 147Nd labeled 7Sm-labeled CCR7, 148Nd-labeled Nestin, 149Sm-labeled CD11b, 150Nd-labeled KIM-1, 151Eu-labeled Uromodulin, 152Sm-labeled TGF-β, 153Eu-labeled α-SMA, 154Sm-labeled Calbindin, 155Gd-labeled Aquaporin-1, 156Gd-labeled CD4, 158Gd-labeled E-Cadherin, 159Tb-labeled CD68, 160Gd-labeled Granzyme K, 161Dy-labeled CD20, 162Dy-labeled CD8, 163Dy-labeled CD138, 164Dy-labeled Cytokeratin-7, 165Ho-labeled Tenascin C, 166Er-labeled CD45, 167Er-labeled CD15, 168Er-labeled Renin, 169Tm-labeled CD103, 170Er-labeled CD3, 171Yb-labeled CD11c, 172Yb-labeled Caspase3, 173Yb-labeled CD45RO, 174Yb-labeled Fibronectin, 175Lu-labeled Collagen I, 176Yb-labeled Granzyme B.

[0015] That is, the antibodies and metal tags that recognize the target antigens are β-catenin-89Y, HistoneH3-139La, CD38-141Pr, CD56-142Nd, Vimentin-143Nd, CD14-144Nd, CD31-145Nd, Aquaporin-2-146Nd, CCR7-147Sm, Nestin-148Nd, CD11b-149Sm, KIM-1-150Nd, Uromodulin-151Eu, TGF-β-152Sm, a-SMA-153Eu, Calbindin-154Sm, Aquaporin-1-155Gd, CD4-156Gd, E-Cadherin-158Gd, CD68-159Tb, Granzyme K-160Gd, CD20-161Dy, CD8-162Dy, CD138-163Dy, Cytokeratin-7-164Dy, TenascinC-165HO , CD45-166Er, CD15-167Er, Renin-168Er, CD103-169Tm, CD3-170Er, CD11c-171Yb, Cleaved Caspase3-172Yb, CD45RO-173Yb, Fibronectin-174Yb, CollagenI-175Lu, Granzyme B-176Yb.

[0016] Furthermore, the masses of the 37 monoclonal antibodies were: 89Y-labeled β-catenin 4-5μg, 139La-labeled HistoneH34-5μg, 141Pr-labeled CD38 4-5μg, 142Nd-labeled CD56 4-5μg, 143Nd-labeled Vimentin 4-5μg, 144Nd-labeled CD14 0.5-1μg, 145Nd-labeled CD31 4-5μg, 146Nd-labeled Aquaporin-2 2-2.5μg, 147Sm-labeled CCR7 4-5μg, 148Nd-labeled Nestin 0.5-1μg, 149Sm-labeled CD11b 0.5-1μg, 150Nd-labeled KIM-1 0.5-1μg, 151Eu-labeled Uromodulin 0.1-0.5μg, 152Sm-labeled TGF-β4-5μg, 153Eu-labeled α-SMA 0.1-0.5μg, 154Sm-labeled Calbindin 0.5-1μg, 155Gd-labeled Aquaporin-1 2-2.5μg, 156Gd-labeled CD4 2-2.5μg, 158Gd-labeled E-Cadherin2-2.5μg, 159Tb-labeled CD68 0.5-1μg, 160Gd-labeled Granzyme K 2-2.5μg, 161Dy-labeled CD20 2-2.5μg, 162Dy-labeled CD8 0.5-1μg, 163Dy-labeled CD138 0.5-1μg, 164Dy-labeled Cytokeratin-7 0.5-1μg, 165HO-labeled Tenascin C 0.5-1μg, 166Er-labeled CD45 0.5-1μg, 167Er-labeled CD15 0.5-1μg, 168Er-labeled Renin 0.1-0.5μg, 169Tm-labeled CD103 0.5-1μg, 170Er-labeled CD3 0.5-1μg, 171Yb-labeled CD11c 0.5-1μg, 172Yb-labeled Caspase3 2-2.5μg, 173Yb-labeled CD45RO 0.5-1μg, 174Yb-labeled Fibronectin 0.5-1μg, 175Lu-labeled CollagenI 0.5-1μg, 176Yb-labeled Granzyme B 0.1-0.5μg.

[0017] Furthermore, the volume of the antibody solution is 50-100 uL.

[0018] A detection method of a kidney pathology detection kit comprises the following steps:

[0019] (1) Collect the sample to be tested and prepare pre-stained paraffin sections;

[0020] (2) After mixing 37 rare metal element-labeled monoclonal antibodies, antibody staining of multiple antigen targets on the cell surface in in situ tissues was performed;

[0021] (3) Staining the stained tissue samples for nuclear DNA;

[0022] (4) The stained tissue samples are detected by an imaging mass spectrometry system, and the original information data is collected by laser spot burning.

[0023] Furthermore, the method for preparing pre-stained paraffin sections in step (1) comprises the following steps: after the sample to be tested is embedded in paraffin, it is sliced ​​into 3-5 μm thickness and mounted on an anti-shedding slide; the slices are dewaxed, followed by antigen repair with a pH 9.0 antigen repair solution, followed by permeabilization of tissue cells with a reagent of 0.2% TritonX100, and finally blocked with 3% BSA to obtain a pre-stained sample.

[0024] Furthermore, in step (3), the DNA staining reagent is a 191 / 193Ir PBS dilution.

[0025] A renal pathology detection kit or a detection method of a renal pathology detection kit is used in analyzing and evaluating cells in renal spatial in situ tissue, for grouping and identifying cells in renal spatial in situ tissue and relatively quantifying them, calculating spatial distances, dividing structural units into regions, and evaluating pathological damage.

[0026] Furthermore, the cells in the in situ tissue include podocytes, mesangial cells, juxtaglomerular cells, proximal tubule cells, acutely injured proximal tubule cells, thick ascending limb cells of the loop of Henle, distal tubule cells, connecting duct cells, collecting duct cells, Uromodulin+Cytokeratin-7+ Shuangyang cells, Aquaporin1+Cytokeratin-7+ Shuangyang cells, Aquaporin1+Calbindin+ Shuangyang cells, fibroblasts, endothelial cells, myofibroblasts, cells, α-SMA+Vimentin+Nestin+ cell populations, Nestin+ fibroblasts, α-SMA+ cells, α-SMA+COL1+ cells, COL1+ cells, B cells, plasma cells, monocytes, dendritic cells, macrophages, M1 macrophages, peripherally derived macrophages, GranzymeB+ macrophages, CD15+ macrophages, naive CD4+T cells, central memory CD4+T cells, effector CD4+T cells, Granzyme K+ effector CD4+T cells, tissue-resident CD4+T cells, naive CD8+T cells, effector CD8+T cells and natural killer T cells.

[0027] Among them, these cells and their markers are shown in the following table

[0028]

[0029]

[0030] Furthermore, the detection data analysis and evaluation method includes the following steps: based on the obtained imaging mass spectrometry detection data, firstly, single cell splitting is performed through Ilastik and Cellprofiler software, and signal overflow correction is performed on each channel; then, through dimensionality reduction analysis and batch correction of the data, tSNE diagram or heat map is drawn, and the metal ion signal is converted into protein or cell signal for subsequent cell classification and identity annotation; finally, the cells are clustered and analyzed to draw a single cell map of local renal tissue, as well as the frequency and proportion of each type of cell; by calculating the spatial distance of each type of cell and cell neighborhood analysis, the kidney functional area is divided, and the type and relative proportion of each single cell in the sample, as well as the spatial distribution and interaction of each type of cell are analyzed.

[0031] Furthermore, the renal pathology assessment method includes the following steps: selecting the pixel points covered by the key markers in the glomerulus and the glomerulus in the imaging mass spectrometry flow graph data as the glomerular region and the renal tubular region respectively; then, in the obtained regions, the expression intensity of the pathology-related markers in the two regions is counted and calculated respectively; combining the expression intensity of the selected markers in each image, using statistical methods to calculate the expression mean and standard deviation of the marker in the entire graph, and finally obtaining the pathological injury expression score Z-Score. In this way, the lesion characteristics of different regions of the kidney can be comprehensively evaluated to help determine the severity of the lesions and pathological changes related to disease progression.

[0032] The present invention studies and designs an analytical method for fine grouping and identification of renal unit structural cells, infiltrating immune cell populations, pathological marker cells, etc. in renal pathological samples based on the detection principle of imaging mass spectrometry, and for immunopathological evaluation. Monoclonal antibodies for cell phenotype identification of 9 structural cells, 17 immune cells, and 4 interstitial cells in renal in situ tissue are used in combination; or glomerular cells, tubular cells, interstitial cells, and tissue-infiltrating monocytes, lymphocytes, and granulocytes in renal tissue are identified and relatively quantified at the same time; or podocytes and mesangial cells in the glomerulus and proximal tubules, ascending limbs of loops of Henle, distal tubules, connecting ducts, and collecting ducts in the renal tubules are identified and relatively quantified at the same time; or the monocytes are identified and relatively quantified at the same time. Identify and relatively quantify macrophages, dendritic cells, and T lymphocytes, B lymphocytes, natural killer NK lymphocytes, and neutrophils among lymphocytes; or calculate the spatial distances of various types of cells at the same time, and divide the structural units into regions, including the glomerular region, proximal tubule region, ascending limb region of Henleus loop, distal convoluted tubule region, connecting duct region, collecting duct region, renal interstitial region, renal fibrosis region, and immune cell-enriched region; or evaluate the pathological damage of the glomerulus and tubules at the same time.

[0033] Compared with the prior art, the beneficial technical effects of the present invention are:

[0034] 1. The present invention overcomes the limitations of the existing pathological sample detection methods for the simultaneous detection of multiple indicators (more than 30) for the fine grouping identification, spatial distribution and spatial interaction of structural cell populations, immune cell populations and pathological cell populations in kidney tissue. The existing method requires that the indicators to be analyzed be distributed to multiple groups for separate detection and analysis. And because the 37 antibodies are connected to unique metal element labels, the matching imaging mass spectrometry flow instrument system can simultaneously identify and collect these metal label signals, so 37 indicators in the pathological section samples can be detected simultaneously, without the need to be split into multiple groups for detection, streamlining the experimental operation, saving the use of detection reagents, reducing the use of precious pathological samples, and also saving sample detection time, thereby improving detection efficiency.

[0035] 2. In the present invention, the 37 metal-labeled monoclonal antibodies used do not have the problem of channel signal overlap because the metal elements they are connected to are different. This means that there is no need to manually compensate the signal, thereby avoiding data errors caused by manual adjustment compensation or compensation of subjective judgment inaccuracy. This design further improves the accuracy of the test results.

[0036] 3. The present invention uses the rare earth metal elements connected to the antibody as the source of the detection signal. The content of rare earth metals in human tissue cells is almost zero, so the interference caused by the autofluorescence of the paraffin sample is almost negligible. This design effectively avoids the influence of the sample autofluorescence on the accuracy of the detection results, thereby significantly improving the reliability of the detection results.

[0037] 4. The present invention uses the rare earth metal element label coupled to the antibody as the detection signal source, thereby avoiding the influence of false positive signals generated by non-specific binding of the secondary antibody to tissue cells on the accuracy of the detection result.

[0038] 5. The present invention is based on the principle of imaging mass spectrometry flow detection. On the paraffin pathological tissue sections of a single cell layer, a variety of different rare metal-coupled monoclonal antibodies are used to accurately bind to specific antigens in the cell membrane, intracellular and nucleus of the cell population in the tissue to be analyzed. The tissue is single-point burned by a laser spot with an accuracy of 1 μm, and the labeled cells are vaporized into an ion cloud. By detecting the content and type of rare metals labeled in each single cell at the same time, the type and relative content of the antigen in each spatially in situ single cell in the sampled tissue area are accurately confirmed, thereby achieving identity definition and spatial interaction analysis of all cell populations in the tissue sample. This method provides valuable auxiliary diagnostic information for the diagnosis, grading, treatment and prognosis monitoring of various kidney diseases. At the same time, by constructing a single-cell atlas of local kidney tissue, the spatial distribution and spatial interaction of cells in the local pathological microenvironment of the kidney are observed panoramically, which is useful for clinical research on the occurrence and development mechanism of kidney disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1-3 The diagrams show the verification of 37 metal-labeled antibodies in the kit of the present invention in normal kidney tissue and hydronephrosis tissue using immunohistochemistry and imaging mass spectrometry flow cytometry.

[0040] Figure 4 This is a multi-channel overlapping display diagram of the mass spectrometry flow cytometry data of normal kidney tissue imaging detected by the combination of 37 antibodies in the kit of the present invention.

[0041] Figure 5 The 37 antibodies in the kit of the present invention are used to stain and identify kidney tissue samples from healthy people, and the cells in the local kidney tissue are finely identified and grouped, the sampled tissue is demarcated into single cells and the cell identity is annotated, and the annotated cell population is mapped back to the spatial original tissue to draw a single cell map of the kidney tissue.

[0042] Figure 6 The present invention is a diagram of the renal unit structure in the multi-channel overlapping display of the mass spectrometry flow cytometry graphic data of kidney tissue imaging of stage IV diabetic nephropathy (DN) detected by the antibody combination of the kit of the present invention.

[0043] Figure 7 The antibody combination of the kit of the present invention detects the tissue infiltration and spatial distribution of immune cell populations and kidney pathological structure diagram in the multi-channel overlapping display of mass spectrometry flow cytometry graphic data of DN stage IV kidney tissue imaging.

[0044] Figure 8 The single cell atlas of DN stage IV renal tissue detected by the antibody combination of the kit of the present invention.

[0045] Fig. 9 The present invention is a diagram of the renal unit structure in the multi-channel overlapping display of the imaging mass spectrometry flow cytometry graphic data of IgA nephropathy (IgAN) stage IV kidney tissue detected by the antibody combination of the kit of the present invention.

[0046] Fig.10 The antibody combination of the kit of the present invention detects the tissue infiltration and spatial distribution of immune cell populations and kidney pathological structure diagram in the multi-channel overlapping display of IgAN stage IV kidney tissue imaging mass spectrometry flow cytometry graphic data.

[0047] Fig.11 The single cell atlas of IgAN stage IV renal tissue detected by the antibody combination of the kit of the present invention.

[0048] Fig.12This is a specific flow chart of the present invention, in which kidney tissue is labeled with 37 metal-labeled antibodies and high-dimensional graphic data is collected by imaging mass spectrometry flow cytometry, and then the dimension reduction and visualization analysis of the graphic data are performed.

[0049] Fig.13 This is the single cell atlas-spatial partitioning diagram of the renal biopsy samples for detecting IgAN stage I and stage IV according to the present invention.

[0050] Fig.14 The present invention detects and compares the pathological damage evaluation and damage differences of glomeruli in normal renal tissue, diabetic nephropathy stage IV, IgAN stage IV, and hydronephrosis samples.

[0051] Fig.15 The present invention detects and compares the pathological damage evaluation and damage differences of renal tubules in normal renal tissue, diabetic nephropathy stage IV, IgAN stage IV, and hydronephrosis samples.

[0052] Fig.16 This is a diagram of the model for evaluating the pathological damage of human kidney functional units (glomeruli / renal tubules) detected by the present invention. DETAILED DESCRIPTION

[0053] Unless otherwise specified, various raw materials, reagents, instruments and equipment used in the present invention can be purchased from the market or prepared by existing methods.

[0054] Table 1: Sources of reagents

[0055]

[0056]

[0057] The metal element labeled antibody combination used includes an antibody recognizing a target antigen and a metal tag, as shown in Table 2 below.

[0058] Table 2

[0059]

[0060]

[0061] The method for detecting kidney pathology comprises the following steps:

[0062] (1) Kidney pathology paraffin samples from the hospital were cut into 3-5 μm thick sections and attached to anti-shedding slides.

[0063] (2) Dewaxing: Preheat the water bath to 96°C and the oven to 60°C, and bake the paraffin sections for 1 to 2 hours. Prepare two dye vats and put xylene in. After taking the sections out of the oven, soak them in one of the dye vats for 10 minutes while they are still hot. Do not close the lid tightly. After completion, transfer them to the other dye vat and soak them for another 10 minutes.

[0064] (3) Rehydration: Soak the sections in a series of ethanol concentrations (100%, 95%, 80%, 70%) for 5 minutes each time; when rehydrating, dilute the antigen retrieval solution to the working concentration (pH 9.0 Antigen Retrieval solution 50× diluted to 1×), prepare several 50mL centrifuge tubes (each centrifuge tube can hold two sections back to back), and put in the diluted antigen retrieval solution, about 40mL in each tube, to ensure that the area where the tissue is located is covered. Preheat it in a 96-degree water bath, and do not tighten the lid; rinse the slides with ddH2O for 5 minutes (place the staining tank on a horizontal shaker).

[0065] (4) Place the slide in a centrifuge tube containing preheated antigen retrieval solution and incubate at 96°C for 30 min. Do not tighten the lid to maintain internal and external pressure balance.

[0066] (5) After incubation, place the centrifuge tube at room temperature and allow it to cool naturally. After cooling to 70°C for 10 min, transfer the slide to a dye tank containing ddH2O and wash for 10 min.

[0067] (6) Transfer the slide into a staining jar containing DPBS, wash for 10 min, and use a PAP pen to draw a circle around the slide.

[0068] (7) The tissue was first permeabilized with 0.2% TritonX100 (DPBS) at room temperature for 15 min, then washed with DPBS for 5 min. After washing three times, the tissue was blocked with DPBS containing 3% BSA for 120 min.

[0069] (8) Antibody mixtures were prepared using DPBS containing 0.5% BSA. All metal-conjugated antibodies were stored at a concentration of 0.5 mg / mL, and the optimal staining concentration was adjusted through single-channel / multi-channel pre-experiments. Two groups of antibody mixtures A (antibodies numbered 1-26) and B (antibodies numbered 27-37) were prepared according to the two staining incubation conditions of all antibodies in Table 3.

[0070] (9) Aspirate the blocking solution and drop antibody mixture A onto the tissue section (if the antibody cannot be spread, apply it horizontally with the tip of a pipette, being careful not to scratch the section). Incubate in a humidified chamber at 4°C overnight, approximately 15 hours.

[0071] (10) Gently shake the slide dry, wash with DPBS for 8 min × 2 times, drop antibody mixture B onto the tissue section area, and incubate in a wet box at room temperature for 5 h.

[0072] (11) Gently shake the slide dry, wash with DPBS for 8 min x 2 times, and shake slowly on a shaker; dilute Ir-Intercalator (DNA dye) with DPBS at 1:400 and stain the tissue for 30 min at room temperature (place in a wet box).

[0073] (12) Rinse slowly with ddH2O for 5 min and air-dry the slides at room temperature for at least 30 min.

[0074] (13) The mass spectrometer is ignited and preheated according to standard operating procedures, and laser calibration and parameter tuning are completed.

[0075] (14) After staining, the samples were sampled according to the standard operating procedures of the imaging mass spectrometry flow cytometry instrument, and all the parts of interest in the renal biopsy tissue visible under the microscope were included in the sampling range.

[0076] Table 3: Antibody configuration scheme

[0077]

[0078] The steps of data analysis are as follows:

[0079] 1. After obtaining the tissue graphic data of mass spectrometry imaging, first use the graphic software MCDViewer to observe the 37 antigen information of the antibody combination markers in the graphic data one by one, and through the channel combination and color matching of different markers, preliminarily observe various tissue cells, immune cells, pathological structural characteristics, etc.; further select markers to distinguish the cell nucleus-cytoplasm and cytoplasm-background, including DNA-Ir, HistoneH3, CD45, Aquaporin1, Aquaporin2, Nestin, etc.

[0080] 2. Use the selected markers to train a random forest learning model in the graphic analysis software Ilastik to classify the pixels in the kidney tissue graphic data into cell nuclei, cytoplasm, and background.

[0081] 3. Import the data into the graphic analysis software Cellprofiler, organize the classified pixels into probability maps of the nucleus, cytoplasm and background, and use mathematical models to delineate the boundaries of single cells, thereby completing the splitting of all single cells in the renal pathological tissue detection area.

[0082] 4. Further draw the map of the split cell boundary on the overlapped signal map of the 37-channel marker expression, conduct a quality check on the single-cell splitting results, and eliminate unqualified data, for example, serious signal loss leads to inaccurate cell boundary positioning, or there are non-specific signals or high noise, etc. Through signal filtering, you can get pre-analysis data for subsequent experiments.

[0083] 5. All pre-analysis data that passed the quality inspection were corrected for single-channel signal overflow in 37 signal channels, that is, the pixel signal cross-contamination of adjacent channels was corrected through the data matrix. All corrected data were subjected to dimensionality reduction visualization analysis, including tSNE analysis and UMAP analysis, and the expression and distribution characteristics of 37 markers in various cell populations were displayed through heat maps and quadrant distribution maps.

[0084] 6. Complete cell splitting and signal correction of imaging data, annotate all single cell identities in two steps:

[0085] 1) First, use the artificial gating method to annotate the identities of all cells in some samples according to the cell types defined by known markers. After the annotation is completed, the gating scheme is applied to all samples in combination with the machine learning classifier. The artificial gating method is to annotate specific cell identities according to the spatiotemporal expression characteristics of reported markers, including nephron cells, glomeruli, juxtaglomerular cells, proximal tubules, thick ascending limbs of loops of Henle, distal tubules, connecting ducts and collecting ducts; cells of lymphoid origin include T lymphocytes, CD4+T lymphocytes, CD8+T lymphocytes, natural killer T cells, B lymphocytes, plasma cells; cells of myeloid origin include monocytes, macrophages, DC cells, neutrophils, etc. Use the annotated cells as the training set for the random forest machine learning classification model, and the trained classification model can be applied to all samples;

[0086] 2) In the second step, the R Phenograph clustering analysis method is used to complete the cell annotation with higher accuracy. The purpose of the second step clustering analysis is: (i) to use clustering analysis to further verify and adjust the cell annotation results and improve the accuracy of cell identity positioning; (ii) clustering analysis can be used to solve the problem of a certain proportion of undefined cells generated to ensure accuracy when using machine learning classification models; (iii) the types of cells annotated in the first step are all classical cell types that have been reported and recognized in the field. Through further clustering analysis, new cell populations in kidney tissue can be discovered, and functional subgroups of classical cell types and cell phenotypes under different disease states can also be discovered;

[0087] 3) Some markers have the characteristics of region-specific expression, such as Vimentin, which is expressed in fibroblasts, myofibroblasts, macrophages, lymphocytes, etc., but in the glomerular region, Vimentin is mainly expressed in mesangial cells. In order to adapt to the characteristics of Vimentin's specific expression in the glomerular region, the patch detection method in the spatial analysis method is used to locate the glomerular region, and the located glomerular cells are clustered to complete the annotation of mesangial cells in the glomerulus.

[0088] 7. Map various annotated cell populations back to the spatial in situ tissue to construct a single-cell atlas of the local area of ​​the kidney. Then, the number and frequency changes of various types of cells in the local tissue of the kidney in normal and different disease stages can be counted and compared.

[0089] 8. To realize the spatial interaction analysis of annotated cell populations in local renal tissues, we first defined the interaction relationship between individual cells by setting a spatial distance threshold, and then used the permutation analysis method to statistically analyze the avoidance-interaction relationship between annotated cell populations under different distance settings, thereby obtaining the spatial aggregation and distribution characteristics of tissue cells and immune cells. Next, we used the k-nearest neighbor classification (KNN, n=30) algorithm to redefine the interaction relationship between cells and divide the classical structural regions of the kidney (including the glomerular region, the proximal convoluted tubule region, the medullary ascending limb region, the distal tubule region, the connecting duct region, the collecting duct region, the fibroblast-rich region, the vascular and fibrosis region, and the immune cell-rich region), and then used the cell neighborhood analysis in each region to obtain the dynamic changes of pathological damage of renal structural units.

[0090] 9. The pathological injury assessment method is to first determine the glomerular / tubular expression area, and then calculate the expression of case markers in the area to obtain the pathological injury score of the glomerulus and tubules. The specific analysis in the renal mass spectrometry flow cytometry data includes the following steps: select the pixel points covered by the podocyte marker (Nestin) in the glomerulus as the glomerular area; select the renal tubular epithelial cell markers: Aquaporin1-proximal tubule, Uromodulin-ascending limb of the loop of Henle, Calbindin-distal convoluted tubule, Cytokeratin-7-connecting tubule, Aquaporin2-collecting duct, and the pixel points covered by the expression of these five markers are selected as the tubular area; then, in the obtained area, the expression intensity of pathological related markers Vimentin, α-SMA, Collagen I, Fibronectin, KIM-1, etc. in the two areas is counted and calculated respectively. Combined with the expression intensity of the selected markers in each image, the expression mean and standard deviation of the markers in the entire figure are calculated using statistical methods, and finally the pathological injury expression score Z-Score is obtained. Z-Score is a standardized scoring method used to reflect the degree of deviation of the expression level of each marker from the normal or control value, thereby providing a quantitative evaluation basis for the degree of pathological damage to the glomeruli and renal tubules. In this way, the lesion characteristics of different areas of the kidney can be comprehensively evaluated to help determine the severity of the lesions and pathological changes related to disease progression.

[0091] Experimental Example 1

[0092] like Figure 4 The 37 core antibodies of the present invention are combined for staining and identification of kidney tissue samples of healthy people, and the cells of local kidney tissue are finely identified and grouped. The obtained normal kidney tissue imaging mass spectrometry flow graphics data are displayed in multi-channel overlapping manner, and the tissue structure is distinguished and displayed by markers of multiple channels, and the glomerulus, proximal tubule, ascending limb of the loop of Henle, distal tubule, connecting duct and collecting duct in the renal unit are distinguished respectively.

[0093] Single cell demarcation and cell identity annotation were performed on the sampled tissues, and finally the absolute counts of each cell component and its frequency in the total cells were counted; the single cell segmentation results of the mass spectrometry flow cytometry imaging data of normal kidney tissue are shown in Table 4 below.

[0094] Table 4

[0095]

[0096]

[0097] Experimental Example 2

[0098] The antibody combination of the kit of the present invention is used to perform detailed identification and grouping of renal tissue cells in renal puncture samples of patients with DN stage IV, such as Figure 6 and 7 As shown, the combination of multi-channel markers respectively demonstrated the characteristics of the renal unit structure, tissue infiltration and spatial distribution of immune cell populations, and renal pathological structure of DN stage IV samples. Figure 8 The single cell map of DN stage IV renal tissue. The single cell segmentation results of mass spectrometry flow imaging data of DN stage IV renal tissue are shown in Table 5 below.

[0099] Table 5

[0100]

[0101]

[0102] Experimental Example 3

[0103] The antibody combination of the kit of the present invention is used to perform detailed identification and grouping of renal tissue cells in renal puncture samples of patients with IgAN stage IV. Fig. 9 and 10 As shown, the combination of multi-channel markers respectively demonstrated the characteristics of the nephron structure, tissue infiltration and spatial distribution of immune cell populations, and renal pathological structure in IgAN stage IV samples; Fig.11 To detect the single cell map of IgAN stage IV renal tissue. The single cell segmentation results of mass spectrometry flow cytometry imaging data of IgAN stage IV renal tissue are shown in Table 6 below.

[0104] Table 6

[0105]

[0106]

[0107] Experimental Example 4

[0108] like Fig.13After single-cell segmentation and cell identity annotation of imaging mass spectrometry data of renal biopsy samples of two cases of IgAN stage I and IgAN stage IV, the local renal tissue was divided into 9 functional regions through cell neighborhood analysis in spatial analysis: glomerulus region, proximal tubule region (PT region), ascending limb of loop of Henle region (TAL region), distal convoluted tubule region (DCT region), connecting duct region (ConDuct region), collecting duct region (ColDuct region), immune cell-enriched region (Immune region), matrix region (Stromalregion), and fibroblast-enriched region (Fibroblast region).

[0109] Experimental Example 5

[0110] like Fig.14 / 15, several image regions (ROIs) were collected from two normal renal tissues (Normal control, NC), two cases of stage IV diabetic nephropathy (DN), two cases of stage IV IgA nephropathy (IgAN), and two cases of hydronephrosis (HyN). After single-channel analysis and graphic pixel extraction of imaging mass spectrometry flow cytometry data, the pathological markers of glomeruli and tubules in the four different kidney diseases were displayed and the Z-score of each marker was calculated for pathological damage assessment. At the same time, the differences in pathological damage of the four different kidney diseases were statistically compared and calculated according to the number of ROIs.

[0111] The present invention is based on the monoclonal antibodies that simultaneously label 37 different rare metal elements at the single cell level of the in situ tissue of the kidney, and specifically bind to the antigens on the surface of the kidney tissue cells to be analyzed. The tissue is burned with a laser spot with an accuracy of 1 μm to form an ion cloud, and then the signal intensity and type of the rare metal ions are simultaneously detected and analyzed by the detector to determine the type and content of the antigens on the cell surface. Finally, through data conversion, the spatial distribution of all detected antigens is restored 1:1 to generate 37-channel high-dimensional spatial in situ graphic data of the kidney tissue. The method of the present invention is used for fine grouping identification and relative quantitative analysis of clinical kidney disease samples, including diabetic nephropathy, IgA nephropathy, hydronephrosis, vasculitis, structural cells in situ tissues, immune cells, and pathologically damaged cells. Provide auxiliary diagnostic information for the diagnosis, grading, treatment and prognosis detection of various clinical kidney diseases.

[0112] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0113] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A kidney pathology detection kit, characterized in that: Including 37 rare metal element labeled monoclonal antibodies that bind to antigens of in situ tissue cells, the 37 rare metal element labeled monoclonal antibodies include 89Y labeled β-catenin, 139La labeled HistoneH3, 141Pr labeled CD38, 142Nd labeled CD56, 143Nd labeled Vimentin, 144Nd labeled CD14, 145Nd labeled CD31, 146Nd labeled Aquaporin-2, 147Sm labeled CCR7, 148Nd-labeled Nestin, 149Sm-labeled CD11b, 150Nd-labeled KIM-1, 151Eu-labeled Uromodulin, 152Sm-labeled TGF-β, 153Eu-labeled α-SMA, 154Sm-labeled Calbindin, 155Gd-labeled Aquaporin-1, 156Gd-labeled CD4, 158Gd-labeled E-Cadherin, 159Tb-labeled CD68, 160Gd-labeled Granzyme K, 161Dy-labeled CD20, 162Dy-labeled CD8, 163Dy-labeled CD138, 164Dy-labeled Cytokeratin-7, 165Ho-labeled Tenascin C, 166Er-labeled CD45, 167Er-labeled CD15, 168Er-labeled Renin, 169Tm-labeled CD103, 170Er-labeled CD3, 171Yb-labeled CD11c, 172Yb-labeled Caspase3, 173Yb-labeled CD45RO, 174Yb-labeled Fibronectin, 175Lu-labeled Collagen I, 176Yb-labeled Granzyme B.

2. A kidney pathology detection kit as claimed in claim 1, characterized in that: The masses of the 37 monoclonal antibodies are as follows: 89Y-labeled β-catenin 4-5μg, 139La-labeled HistoneH3 4-5μg, 141Pr-labeled CD38 4-5μg, 142Nd-labeled CD56 4-5μg, 143Nd-labeled Vimentin 4-5μg, 144Nd-labeled CD14 0.5-1μg, 145Nd-labeled CD31 4-5μg, 146Nd-labeled Aquaporin-2 2-2.5μg, 147Sm-labeled CCR7 4-5μg, 148Nd-labeled Nestin 0.5-1μg, 149Sm-labeled CD11b 0.5-1μg, 150Nd-labeled KIM-1 0.5-1μg, 151Eu-labeled Uromodulin 0.1-0.5μg, 152Sm-labeled TGF-β4-5μg, 153Eu-labeled α-SMA 0.1-0.5μg, 154Sm-labeled Calbindin 0.5-1μg, 155Gd-labeled Aquaporin-1 2-2.5μg, 156Gd-labeled CD4 2-2.5μg, 158Gd-labeled E-Cadherin 2-2.5μg, 159Tb-labeled CD68 0.5-1μg, 160Gd-labeled Granzyme K 2-2.5μg, 161Dy-labeled CD20 2-2.5μg, 162Dy-labeled CD8 0.5-1μg, 163Dy-labeled CD138 0.5-1μg, 164Dy-labeled Cytokeratin-7 0.5-1μg, 165HO-labeled Tenascin C 0.5-1μg, 166Er-labeled CD45 0.5-1μg, 167Er-labeled CD15 0.5-1μg, 168Er-labeled Renin 0.1-0.5μg, 169Tm-labeled CD103 0.5-1μg, 170Er-labeled CD3 0.5-1μg, 171Yb-labeled CD11c 0.5-1μg, 172Yb-labeled Caspase3 2-2.5μg, 173Yb-labeled CD45RO 0.5-1μg, 174Yb-labeled Fibronectin 0.5-1μg, 175Lu-labeled CollagenI 0.5-1μg, 176Yb-labeled Granzyme B 0.1-0.5μg.

3. A kidney pathology detection kit as claimed in claim 2, characterized in that: The volume of the antibody solution is 50-100uL.

4. The detection method of a renal pathology detection kit according to any one of claims 1 to 3, characterized in that: The following steps are involved: (1) Collect the sample to be tested and prepare pre-stained paraffin sections; (2) After mixing 37 rare metal element-labeled monoclonal antibodies, antibody staining of multiple antigen targets on the cell surface in in situ tissues was performed; (3) Staining the stained tissue samples for nuclear DNA; (4) The stained tissue samples are detected by an imaging mass spectrometry system, and the original information data is collected by laser spot burning.

5. The detection method of a kidney pathology detection kit as claimed in claim 4, characterized in that: The method of preparing pre-stained paraffin sections in step (1) comprises the following steps: after the sample to be tested is embedded in paraffin, it is sliced ​​into 3-5 μm thickness and mounted on an anti-shedding slide; the slice is dewaxed, and then antigen repair is performed with a pH 9.0 antigen repair solution, and then the tissue cells are permeabilized with a reagent 0.2% TritonX100, and finally blocked with 3% BSA to obtain a pre-stained sample.

6. The detection method of a kidney pathology detection kit as claimed in claim 4, characterized in that: In step (3), the DNA staining reagent is a 191 / 193Ir PBS dilution.

7. Use of a renal pathology detection kit as claimed in any one of claims 1 to 3 or a renal pathology detection kit as claimed in any one of claims 4 to 6 in analyzing and evaluating cells in renal space in situ tissue, characterized in that: It is used to identify and relatively quantify cells in the in situ spatial tissue of the kidney, calculate spatial distances, divide structural units into regions, and evaluate pathological damage.

8. The use according to claim 7, characterized in that: The cells in the in situ tissue include podocytes, mesangial cells, juxtaglomerular cells, proximal tubule cells, acutely injured proximal tubule cells, thick segment cells of the ascending limb of the loop of Henle, distal tubule cells, connecting duct cells, collecting duct cells, Uromodulin+Cytokeratin-7+ Shuangyang cells, Aquaporin1+Cytokeratin-7+ Shuangyang cells, Aquaporin1+Calbindin+ Shuangyang cells, fibroblasts, endothelial cells, myofibroblasts, α-SMA+Vimentin+Nestin+ Cell populations, Nestin+ fibroblasts, α-SMA+ cells, α-SMA+COL1+ cells, COL1+ cells, B cells, plasma cells, monocytes, dendritic cells, macrophages, M1 macrophages, peripherally derived macrophages, GrzB+ macrophages, CD15+ macrophages, naive CD4+T cells, central memory CD4+T cells, effector CD4+T cells, GzmK+ effector CD4+T cells, tissue-resident CD4+T cells, naive CD8+T cells, effector CD8+T cells and natural killer T cells.

9. The use according to claim 7, characterized in that: The detection data analysis and evaluation method includes the following steps: based on the obtained imaging mass spectrometry detection data, firstly, single cell splitting is performed through Ilastik and Cellprofiler software, and signal overflow correction is performed on each channel; then, through dimensionality reduction analysis and batch correction of the data, tSNE diagrams or heat maps are drawn to convert metal ion signals into protein or cell signals for subsequent cell classification and identity annotation; finally, the cells are clustered and analyzed to draw a single cell map of local renal tissues, as well as the frequency and proportion of each type of cell; by calculating the spatial distance of each type of cell and analyzing the cell neighborhood, the kidney functional areas are divided, and the type and relative proportion of each single cell in the sample, as well as the spatial distribution and interaction of each type of cell are analyzed.

10. The use according to claim 7, characterized in that: The renal pathology assessment method includes the following steps: selecting pixels covered by key markers in the glomerulus and glomerulus in the imaging mass spectrometry flow graph data as the glomerular region and tubular region, respectively; then, within the obtained regions, counting and calculating the expression intensity of pathology-related markers in the two regions, respectively; combining the expression intensity of the selected markers in each image, using statistical methods to calculate the expression mean and standard deviation of the marker in the entire graph, and finally obtaining the pathological damage expression score Z-Score.

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