Detection kit and detection system for IgA nephropathy

Abstract

The invention discloses a detection kit and a detection system for IgA nephropathy, and belongs to the technical field of nephropathy detection.The kit comprises a plasma Gd-IgA1 detection reagent, a Gd-IgA1 fluorescent antibody, an IgA1 fluorescent antibody, a CD19 fluorescent antibody, a CD11c fluorescent antibody and a CD27 fluorescent antibody. The ratio of the first cell subset and the ratio of the second cell subset are detected through the detection kit, the IgA nephropathy is predicted / diagnosed, the detection kit has important guiding value for disease prognosis prediction and curative effect evaluation, and a new view is opened up for clinical management of the IgA nephropathy; the method is non-invasive clinical prediction and can realize non-invasive diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of kidney disease detection, and in particular to a detection kit and a detection system for IgA nephropathy. Background Art

[0002] IgA nephropathy (IgAN) is the most common primary glomerular disease. IgA or IgA deposition is the main component of the glomerular mesangial area, leading to hematuria and proteinuria. Some patients may develop renal failure. The cause of IgA nephropathy is not yet fully understood, and the most widely recognized pathogenesis is the "quadruple hit theory."

[0003] Overproduction of Gd-IgA1 is one of the most important factors in the development of the disease. However, studies have found that elevated levels of Gd-IgA1 are not unique to IgAN patients, but may also be elevated in normal people and other kidney disease patients, limiting the ability of Gd-IgA1 to serve as a specific marker. The Gd-IgA1 secreting cell subpopulation cannot be effectively identified due to the lack of markers.

[0004] Currently, monoclonal antibody-based enzyme-linked immunosorbent assay (ELISA) is commonly used to detect Gd-IgA1 in blood, such as specific monoclonal antibodies KM55 and 35A12. It has been reported that the peripheral blood of IgAN patients is rich in Gd-IgA1-positive B cells that express λ light chain-positive mucosal homing receptors; IgA-positive plasmacytoid precursor cells and CD27- B cells all express Gd-IgA1.

[0005] Therefore, there is currently a lack of clear markers to identify and characterize IgAN and its cell subsets, and an improved detection / prediction method for IgA nephropathy is urgently needed. Summary of the invention

[0006] In view of the above-mentioned technical problems existing in the prior art, the present invention provides a detection kit and a detection system for IgA nephropathy to predict / detect IgA nephropathy and realize non-invasive diagnosis.

[0007] The invention discloses a detection kit for IgA nephropathy, comprising Gd-IgA1 fluorescent antibody, IgA1 fluorescent antibody, CD19 fluorescent antibody, CD11c fluorescent antibody and CD27 fluorescent antibody.

[0008] Preferably, the detection kit further comprises any one of the following reagents or a combination thereof: Plasma Gd-IgA1 detection reagent, FcR blocking reagent and cell viability stain.

[0009] Preferably, the fluorescent antibody for detecting Gd-IgA1 is a rat anti-human Gd-IgA1 antibody combined with a PE anti-rat IgG2b antibody; IgA1 fluorescent antibody uses IgA1-FITC; CD19 fluorescent antibody uses CD19-APC-cy7; CD11c fluorescent antibody used was CD11c- PE / Dazzle 594; The CD27 fluorescent antibody used was CD27-BV510.

[0010] Preferably, the plasma Gd-IgA1 detection reagent is an ELISA reagent of Gd-IgA1; and the cell activity staining agent is 7-AAD.

[0011] Preferably, the method for using the detection kit comprises the following steps: Collect peripheral blood; Detect the level of Gd-IgA1 in the plasma of peripheral blood; separating mononuclear cells from the peripheral blood; Based on fluorescent dye-labeled antibodies (fluorescent antibodies), Gd-IgA1- and IgA1-positive B cells were screened to obtain the first cell subset; Selecting a second cell subset that is CD11c-positive and CD27-negative from the first cell subset; Through the prediction model, the probability of IgA nephropathy is predicted based on the Gd-IgA1 level, the proportion of the first cell subset and the proportion of the second cell subset.

[0012] Preferably, IgA nephropathy is judged according to the probability and the threshold; The prediction model is a logistic regression model, which is expressed as: Logit(p) = L0+L1*A1+L2*B1+L3*B2 Among them, p represents the probability of IgA nephropathy, A1 represents the level of Gd-IgA1, B1 represents the proportion of the first cell subset, B2 represents the proportion of the second cell subset in the first cell subset, and L0, L1, L2, and L3 are coefficients respectively.

[0013] Preferably, the method for obtaining the ratio of the first cell subpopulation to the second cell subpopulation comprises: Peripheral blood mononuclear cells were extracted by density gradient centrifugation; Mononuclear cells were resuspended in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin; Fc receptors were blocked using FcR blocking reagent for 10 min at 4°C; After incubation with rat anti-human Gd-IgA1 antibody at 37°C for 1 h, the cells were washed; After incubation with PE anti-rat IgG2b antibody and rat anti-human Gd-IgA1 at room temperature in the dark for 30 min, the cells were washed; Stain with anti-CD19 APC / cy7, anti-CD27 BV510, anti-CD11c PE / Dazzle 594, and anti-IgA1 FITC and incubate at 4°C in the dark for 30 minutes; 7-AAD activity staining solution was used to stain dead cells; After staining, flow cytometry was performed to obtain the detection signal; Characterize the detection signal based on the size of the cell and circle the cell; After excluding doublets and clumps to isolate single cells, live cells were identified; B cells were circled by CD19+; Gating by IgA1+ and Gd-IgA1+ in turn, screening out the first cell subpopulation, and obtaining the proportion of the first cell subpopulation; The second cell subset was screened out by gating on CD11c+ and CD27-, and the proportion of the second cell subset was obtained.

[0014] The present invention also provides a detection system, comprising a collection module and a prediction module; The acquisition module is used to obtain the detection value of the above detection kit; The prediction module is used to analyze the detection value according to the prediction model to obtain the probability of suffering from IgA nephropathy.

[0015] Preferably, the acquisition module includes a plasma detection submodule, a flow submodule and a gating submodule. The plasma detection submodule is used to collect the level of Gd-IgA1 in plasma; The flow submodule is used to collect flow signals based on fluorescent dye-labeled antibodies; The gating submodule is used to screen out the first cell subpopulation and the second cell subpopulation according to the flow signal, and to count the proportions of the first cell subpopulation and the second cell subpopulation.

[0016] Preferably, the detection system further comprises a training module, and the training module is used to train a training set based on a machine learning method to obtain a prediction model.

[0017] Compared with the prior art, the beneficial effects of the present invention are: by detecting the proportion of the first cell subpopulation and the proportion of the second cell subpopulation, IgA nephropathy is predicted / diagnosed, which has important guiding value for disease prognosis prediction and efficacy evaluation, and opens up new horizons for the clinical management of IgA nephropathy; it is a non-invasive clinical prediction and can achieve non-invasive diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A It is a flow cytometry representation of cells; Figure 1B This is a flow cytometry representation of a single cell; Figure 1C This is a flow cytometry representation of living cells; Figure 1D This is a flow cytometry representation of B cells; Figure 1E This is a representative flow cytometric image of IgA1+; Figure 1F It is a representative flow cytometric image of Gd-IgA1+; Figure 2 is a comparison chart of plasma Gd-IgA1 levels in the IgA nephropathy group and the healthy control group; Figure 3 This is a comparison chart of Gd-IgA1 levels in the IgA nephropathy group and the healthy control group by flow cytometry combined detection; Figure 4 This is a comparison chart of the proportion of the first cell subset in the IgA nephropathy group and the healthy control group; Figure 5 This is a comparison chart of the proportion of the second cell subset in the IgA nephropathy group and the healthy control group in B cells; Figure 6 This is a comparison chart of the proportion of the second cell subset in the first cell subset between the IgA nephropathy group and the healthy control group; Figure 7 It is a flow cytometry representative graph comparison graph of the second cell subpopulation in the first cell subpopulation; Figure 8 It is the ROC curve of the prediction model; Fig. 9 It is a logic block diagram of the detection system for IgA nephropathy of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings: The first aspect of the present invention provides a method for detecting IgA nephropathy, comprising the following steps: Step S1: Collect peripheral blood.

[0021] Step S2: Detecting the level of Gd-IgA1 in the plasma of peripheral blood.

[0022] Step S3: separating mononuclear cells from the peripheral blood.

[0023] Step S4: Based on the fluorescent dye-labeled antibody (fluorescent antibody), Gd-IgA1 and IgA1-positive B cells (IgA1+Gd-IgA1+ B cells) are screened and recorded as the first cell subset.

[0024] Among them, the related reagents labeled with fluorescent dyes are as follows: rat anti-human Gd-IgA1 antibody (Anti-Human Gd-IgA1(KM55) Rat IgG MoAb, 10777, immune - biological Laboratories, Japan), PE anti-rat IgG2b antibody (PE anti-rat IgG2b Antibody, 408213, BioLegend), 7-AAD, CD19- APC-cy7, CD11c- PE / Dazzle 594, IgA1-FITC, CD27-BV510; and FcR blocking reagent (130-059-901, Miltenyi Biotec, Germany), PBS containing 2% FBS (P2F).

[0025] Step S5: Based on fluorescent dye-labeled antibodies (fluorescent antibodies), a second cell subset (CD11c+CD27-), also called an atypical memory cell subset, which is CD11c-positive and CD27-negative is screened from the first cell subset.

[0026] Step S6: Predicting the probability of IgA nephropathy based on the Gd-IgA1 level, the proportion of the first cell subset, and the proportion of the second cell subset in the first subset through the prediction model.

[0027] Step S7: IgA nephropathy is determined based on the probability and the threshold value. For example, when the probability is greater than 50% or 80%, it is determined to be IgA nephropathy.

[0028] IgA nephropathy can be predicted / diagnosed through the three indicators of Gd-IgA1 level, the proportion of the first cell subset and the proportion of the second cell subset, which has important guiding value for disease prognosis prediction and efficacy evaluation, and opens up new horizons for the clinical management of IgA nephropathy. It is a non-invasive clinical prediction that can achieve non-invasive diagnosis.

[0029] The specific detection methods are as follows: Step 101: Collect 10 ml of peripheral blood in an EDTA anticoagulant tube, centrifuge at 3000 rpm for 10 minutes, and keep the upper plasma and freeze it at -80°C for later use.

[0030] Step 102: Detect the plasma Gd-IgA1 level using ELISA reagent (Gd-IgA1 (Galactose-deficient IgA1) Assay Kit-IBL, IBL Japan, 27600). Perform the test according to the instructions. This test is a prior art and will not be described in detail in the present invention.

[0031] Step 103: Peripheral blood mononuclear cells (PBMCs) were extracted by density gradient centrifugation, frozen at -80°C and then transferred to liquid nitrogen.

[0032] Step 104: Resuspend the mononuclear cells in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin.

[0033] Step 105: Block Fc receptors using FcR blocking reagent at 4°C for 10 minutes to prevent nonspecific staining.

[0034] Step 106: After incubation with rat anti-human Gd-IgA1 antibody (Anti-Human Gd-IgA1 Rat IgGMoAb) at 37°C for 1 hour, P2F was washed twice. The specific working concentration was: 20 μg / ml P2F.

[0035] Step 107: After incubating with PE anti-rat IgG2b antibody and rat anti-human Gd-IgA1 at room temperature in the dark for 30 minutes, P2F was washed twice.

[0036] Step 108: Stain with anti-CD19 APC / cy7, anti-CD27 BV510, anti-CD11c PE / Dazzle594, and anti-IgA1 FITC, and incubate at 4°C in the dark for 30 minutes.

[0037] Step 109: Use 7-AAD Viability Staining Solution (BioLegend, USA) to stain dead cells.

[0038] Step 110: After staining, samples were detected and analyzed using a DxFlex cytometer (Beckman Coulter). The instrument was equipped with violet (405 nm), blue (488 nm), and red (638 nm) lasers. Detailed analysis was performed using FlowJo software version 10.8.1 (BD Life Sciences, USA).

[0039] Step 111: Screening the first cell subset.

[0040] First, circle the cells according to their size. Figure 1A , then exclude doublets and clumps to separate single cells (Singlets), such as Figure 1B Next, live cells were identified, such as Figure 1C ; Then select CD19+ cells (CD19+) to circle B cells, such as Figure 1D . The IgA1+ subpopulation of B cells was then gated (IgA1+), e.g. Figure 1E ; Finally, the Gd-IgA1+ subpopulation in the IgA1+ B cell subpopulation is gated (Gd-IgA1+), such as Figure 1F Among them, FSC (Forward scatter) represents forward scattered light; SSC (side scatter) represents side scattered light; H is the height of the electric pulse signal (H, height), representing the peak value of the pulse signal; W is the width of the pulse signal (W, width), referring to the time it takes for the cell to pass through the laser detection area; A (area) refers to the area of ​​the electric pulse signal.

[0041] Step 112: Screening the second cell subset.

[0042] The gating strategy for atypical memory cell subsets in the first cell subset is: CD11c+CD27-, that is, screening cells that are CD11c positive and CD27 negative.

[0043] Step 113: Build a prediction model.

[0044] The specific prediction indicators used are: Gd-IgA1 level, the proportion of the first cell subpopulation and the proportion of the second cell subpopulation in the first subpopulation. The prediction model can be trained using a machine learning method. In a specific embodiment, a logistic regression method is used for training, but it is not limited thereto.

[0045] In the specific training, the plasma Gd-IgA1 levels of 57 patients with IgA nephropathy (IgAN) and 22 healthy controls (HC) (large sample cohort) were tested, see Figure 2 The plasma Gd-IgA1 level in the IgA nephropathy group was higher than that in the healthy control group, and the difference was statistically significant. This indicates that the increased plasma Gd-IgA1 level may serve as a marker for IgA nephropathy, but the heterogeneity between individuals is large.

[0046] Blood samples from 14 IgA nephropathy patients and 11 healthy subjects (small sample cohort) were randomly selected for plasma fluorescent antibody combined with flow cytometry detection. Figure 3 There was no significant difference in Gd-IgA1 levels between the two groups. Figure 4 , the proportion of the first cell subset (IgA1+Gd-IgA1+ B cells) in the IgA nephropathy group increased. Figure 5 In the B cells, the proportion of the second cell subset in the IgA nephropathy group increased significantly. Figure 6 The proportion of the second cell subset in the first cell subset in the IgA nephropathy group was significantly increased. Figure 7 It is a flow cytometry representative graph comparison diagram showing the second cell subset in the first cell subset of the IgA nephropathy group and the healthy control group, namely the first cell subset gate (Gate on gA1+Gd-IgA1+ B Cells), wherein the horizontal axis is the CD11c signal and the vertical axis is the CD27 signal.

[0047] Although there was no significant difference in plasma Gd-IgA1 levels in the small sample cohort, considering that plasma Gd-IgA1 is currently a generally recognized IgAN disease marker and the difference was statistically significant in the large sample cohort, the plasma Gd-IgA1 level, the proportion of IgA1+Gd-IgA1+ B cells, and the percentage of CD11c+CD27- subpopulation in IgA1+Gd-IgA1+ B cells of 14 IgAN patients and 11 healthy controls (HC) were included in the binary logistic regression analysis, and the prediction model based on logistic regression was obtained: Logit(p) = -21.923+0.341*A1+136.051*B1+0.451*B2.

[0048] Among them, p represents the probability of IgA nephropathy, A1 represents the level of Gd-IgA1, B1 represents the proportion of the first cell subset (%), and B2 represents the proportion of the second cell subset in the first cell subset (%). The ROC curve was used to calculate the AUC of each univariate and logistic regression model. Except for the plasma Gd-IgA1 level, the other two indicators were statistically significant, such as Figure 8 The AUC of the logistic regression prediction model combining the three indicators was as high as 0.981 (95%CI: 0.935-1.000), the AUC of the proportion of the second cell subset in the first cell subset was 0.922, and the AUC of the proportion of the first cell subset was 0.838, where the horizontal axis is: 1-specificity (1-specificity), and the vertical axis is sensitivity (sensitivity). It can be seen that the AUC of the proportion of the first cell subset and / or the proportion of the second cell subset in the first cell subset can meet the needs of the prediction model, and one or two of them can be used to train the prediction model.

[0049] It should be noted that the coefficients of various indicators vary depending on the training samples; the present invention uses the proportion of the second cell subpopulation in the first cell subpopulation; but when it is replaced with the proportion of the second cell subpopulation in B cells, the prediction model can also be trained. In general, the prediction model can be expressed as: Logit(p) = L0+L1*A1+L2*B1+L3*B2 Among them, L0, L1, L2, and L3 are respectively expressed as coefficients.

[0050] Other machine learning methods may also be used to train the prediction model, such as random forest model (RF), support vector machine model (SVM) and generalized linear model (GLM), etc. The training method is a prior art and will not be described in detail in the present invention.

[0051] The second aspect of the present invention provides a detection kit for implementing the above detection method, comprising Gd-IgA1 fluorescent antibody, IgA1 fluorescent antibody, CD19 fluorescent antibody, CD11c fluorescent antibody, and CD27 fluorescent antibody.

[0052] The detection kit also includes: plasma Gd-IgA1 detection reagent, FcR blocking reagent and cell activity staining agent.

[0053] Among them, the fluorescent antibody for Gd-IgA1 detection uses rat anti-human Gd-IgA1 antibody combined with PE anti-rat IgG2b antibody; IgA1 fluorescent antibody uses IgA1-FITC; CD19 fluorescent antibody uses CD19-APC-cy7; CD11c fluorescent antibody uses CD11c-PE / Dazzle 594; CD27 fluorescent antibody uses CD27-BV510. Plasma Gd-IgA1 detection reagent uses Gd-IgA1 ELISA reagent; cell activity stain uses 7-AAD.

[0054] A third aspect of the present invention provides a detection system for IgA nephropathy, such as Fig. 9 As shown, it includes a collection module 1 and a prediction module 5; The acquisition module 1 is used to obtain the detection value of the detection kit; The prediction module 5 is used to analyze the detection value based on the prediction model to obtain the probability of suffering from IgA nephropathy.

[0055] The acquisition module 1 includes a plasma detection submodule 2, a flow submodule 3 and a gating submodule 4. The plasma detection submodule is used to collect the level of Gd-IgA1 in plasma. The flow submodule 3 is used to collect flow signals based on fluorescent dye-labeled antibodies. The gating submodule 4 is used to screen out the first cell subpopulation and the second cell subpopulation according to the flow signals, and to count the proportions of the first cell subpopulation and the second cell subpopulation.

[0056] The detection system also includes a training module 6, which is used to train the training set based on a machine learning method to obtain a prediction model.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A detection kit for IgA nephropathy, characterized in that: Including Gd-IgA1 fluorescent antibody, IgA1 fluorescent antibody, CD19 fluorescent antibody, CD11c fluorescent antibody and CD27 fluorescent antibody.

2. The detection kit according to claim 1, characterized in that Also includes any of the following reagents or their combination: Plasma Gd-IgA1 detection reagent, FcR blocking reagent and cell viability stain.

3. The detection kit according to claim 2, characterized in that The fluorescent antibody for Gd-IgA1 detection was rat anti-human Gd-IgA1 antibody combined with PE anti-rat IgG2b antibody; IgA1 fluorescent antibody uses IgA1-FITC; CD19 fluorescent antibody uses CD19-APC-cy7; The CD11c fluorescent antibody used was CD11c-PE / Dazzle 594; The CD27 fluorescent antibody used was CD27-BV510.

4. The detection kit according to claim 2, characterized in that The plasma Gd-IgA1 detection reagent uses Gd-IgA1 ELISA reagent; the cell activity stain uses 7-AAD.

5. The detection kit according to any one of claims 1 to 4, characterized in that The method for using the detection kit comprises the following steps: Collect peripheral blood; Detect the level of Gd-IgA1 in the plasma of peripheral blood; separating mononuclear cells from the peripheral blood; Gd-IgA1 and IgA1-positive B cells were screened by fluorescent antibodies to obtain the first cell subset; Selecting a second cell subset that is CD11c-positive and CD27-negative from the first cell subset; Through the prediction model, the probability of IgA nephropathy is predicted based on the Gd-IgA1 level, the proportion of the first cell subset and the proportion of the second cell subset.

6. The detection kit according to claim 5, characterized in that According to the probability and the threshold, IgA nephropathy is judged; The prediction model is a logistic regression model, which is expressed as: Logit(p) = L0+L1*A1+L2*B1+L3*B2 Among them, p represents the probability of IgA nephropathy, A1 represents the Gd-IgA1 level, B1 represents the proportion of the first cell subset, B2 represents the proportion of the second cell subset in the first cell subset, and L0, L1, L2 and L3 are coefficients respectively.

7. The detection kit according to claim 5, characterized in that The method for obtaining the proportions of the first cell subpopulation and the second cell subpopulation includes: Peripheral blood mononuclear cells were extracted by density gradient centrifugation; Mononuclear cells were resuspended in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin; Fc receptors were blocked using FcR blocking reagent for 10 min at 4°C; After incubation with rat anti-human Gd-IgA1 antibody at 37°C for 1 h, the cells were washed; After incubation with PE anti-rat IgG2b antibody and rat anti-human Gd-IgA1 at room temperature in the dark for 30 min, the cells were washed; Stain with anti-CD19 APC / cy7, anti-CD27 BV510, anti-CD11c PE / Dazzle 594, and anti-IgA1 FITC and incubate at 4°C in the dark for 30 minutes; Use 7-AAD activity staining solution to stain dead cells; After staining, flow cytometry was performed to obtain the detection signal; Characterize the detection signal based on the size of the cell and circle the cell; After excluding doublets and clumps to isolate single cells, live cells were identified; B cells were circled by CD19+; Through gating by IgA1+ and Gd-IgA1+, the first cell subpopulation is screened out, and the proportion of the first cell subpopulation is obtained; The second cell subset was screened out by gating on CD11c+ and CD27-, and the proportion of the second cell subset was obtained.

8. A detection system, characterized in that: Includes acquisition module and prediction module; The acquisition module is used to obtain the detection value of the detection kit according to any one of claims 1 to 7; The prediction module is used to analyze the detection value according to the prediction model to obtain the probability of suffering from IgA nephropathy.

9. The detection system according to claim 8, characterized in that: The acquisition module includes a plasma detection submodule, a flow submodule and a gating submodule. The plasma detection submodule is used to collect the level of Gd-IgA1 in plasma; The flow submodule is used to collect flow signals based on fluorescent dye-labeled antibodies; The gating submodule is used to screen out the first cell subpopulation and the second cell subpopulation according to the flow signal, and to count the proportions of the first cell subpopulation and the second cell subpopulation.

10. The detection system according to claim 8, characterized in that: It also includes a training module, which is used to train the training set based on a machine learning method to obtain a prediction model.

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