Test kit and detection system for iga nephropathy

Abstract

The application discloses a detection kit and a detection system for IgA nephropathy, belongs to the technical field of kidney disease detection, and comprises plasma Gd-IgA1 detection reagents, Gd-IgA1 fluorescent antibodies, IgA1 fluorescent antibodies, CD19 fluorescent antibodies, CD11c fluorescent antibodies and CD27 fluorescent antibodies. The first cell subpopulation proportion and the second cell subpopulation proportion are detected by the detection kit, IgA nephropathy is predicted / diagnosed, and the prediction of the disease prognosis and the evaluation of the curative effect have important guiding values, thereby opening up a new field for the clinical management of IgA nephropathy. The application is a kind of non-invasive clinical prediction and can realize non-invasive diagnosis.

Description

Technical Field

[0001] This invention relates to the field of kidney disease detection technology, specifically to a detection kit and system for IgA nephropathy. Background Technology

[0002] IgA nephropathy (IgAN) is the most common primary glomerular disease, characterized by the deposition of IgA or IgA-dependent substances in the glomerular mesangial area, leading to hematuria and proteinuria. Some patients may develop renal failure. The etiology of IgA nephropathy is not fully understood, but the "quadruple hit theory" is currently the most widely accepted theory of its pathogenesis.

[0003] The overproduction of Gd-IgA1 is a significant factor, highlighting its importance in disease progression. However, studies have found that elevated Gd-IgA1 levels are not unique to IgAN patients; they can also be elevated in healthy individuals and other kidney disease patients, limiting Gd-IgA1's ability as a specific biomarker. Furthermore, the lack of biomarkers makes it difficult to effectively identify Gd-IgA1-secreting cellular subsets.

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

[0005] Therefore, there is currently a lack of clear biomarkers for the identification and characterization of IgAN and its cellular subsets, and there is an urgent need for an improved detection / prediction method for IgA nephropathy. Summary of the Invention

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

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

[0008] Preferably, the test kit further includes any one or a combination of the following reagents:

[0009] Plasma Gd-IgA1 detection reagent, FcR blocking reagent, and cell viability staining agent.

[0010] Preferably, the fluorescent antibody for Gd-IgA1 detection uses rat anti-human Gd-IgA1 antibody combined with PE anti-rat IgG2b antibody;

[0011] The IgA1 fluorescent antibody used was IgA1-FITC; the CD19 fluorescent antibody used was CD19-APC-cy7.

[0012] The CD11c fluorescent antibody used was CD11c-PE / Dazzle 594;

[0013] The CD27 fluorescent antibody used is CD27-BV510.

[0014] Preferably, the plasma Gd-IgA1 detection reagent is the Gd-IgA1 ELISA reagent; the cell viability staining agent is 7-AAD.

[0015] Preferably, the method of using the test kit includes the following steps:

[0016] Collect peripheral blood;

[0017] The level of Gd-IgA1 in peripheral blood plasma was measured.

[0018] Mononuclear cells were isolated from the peripheral blood;

[0019] Based on fluorescently labeled antibodies (fluorescent antibodies), Gd-IgA1 and IgA1-positive B cells were screened to obtain the first cell subset;

[0020] Screening for a second cell subset that is CD11c positive and CD27 negative from the first cell subset;

[0021] The probability of IgA nephropathy is predicted using a predictive model based on Gd-IgA1 levels, the proportion of the first cell subset, and the proportion of the second cell subset.

[0022] Preferably, IgA nephropathy is diagnosed based on the probability and threshold.

[0023] The prediction model is a logistic regression model, expressed as:

[0024] Logit(p) = L0+L1*A1+L2*B1+L3*B2

[0025] Where 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.

[0026] Preferably, the method for obtaining the proportion of the first cell subpopulation and the second cell subpopulation includes:

[0027] Peripheral blood mononuclear cells were extracted using density gradient centrifugation.

[0028] Mononuclear cells were resuspended in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin;

[0029] Fc receptors were blocked for 10 minutes at 4°C using an FcR blocking agent;

[0030] After incubating with rat anti-human Gd-IgA1 antibody at 37°C for 1 hour, the sample was washed.

[0031] The rat anti-human Gd-IgA1 antibody was incubated with PE anti-rat IgG2b antibody and rat anti-human Gd-IgA1 at room temperature in the dark for 30 minutes, followed by washing.

[0032] The staining was performed using anti-CD19 APC / cy7, anti-CD27 BV510, anti-CD11c PE / Dazzle 594, and anti-IgA1 FITC, and incubated at 4°C in the dark for 30 minutes.

[0033] Dead cells were stained using 7-AAD active staining solution;

[0034] After staining, flow cytometry was performed to obtain the detection signal;

[0035] The detection signal is characterized based on cell size, and the cells are then circled.

[0036] After excluding double cells and clumps to separate single cells, live cells were identified.

[0037] B cells are identified by CD19+.

[0038] The first cell subpopulation was screened by gating with IgA1+ and Gd-IgA1+ in sequence, and the proportion of the first cell subpopulation was obtained.

[0039] By gating CD11c+ and CD27-, the second cell subpopulation was screened out, and the proportion of the second cell subpopulation was obtained.

[0040] The present invention also provides a detection system, including a data acquisition module and a prediction module;

[0041] The acquisition module is used to obtain the detection value obtained by the above-mentioned detection kit;

[0042] The prediction module is used to analyze the detection value according to the prediction model to obtain the probability of having IgA nephropathy.

[0043] Preferably, the acquisition module includes a plasma detection submodule, a flow cytometry submodule, and a gating submodule.

[0044] The plasma testing submodule is used to collect the level of Gd-IgA1 in plasma;

[0045] The flow cytometry submodule is used to acquire flow cytometry signals based on antibodies labeled with fluorescent dyes.

[0046] The gating submodule is used to filter out the first cell subpopulation and the second cell subpopulation based on the flow cytometry signal, and to calculate the proportion of the first cell subpopulation and the second cell subpopulation.

[0047] Preferably, the detection system further includes a training module, which is used to train the training set based on machine learning methods to obtain a prediction model.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: by detecting the proportion of the first cell subset and the proportion of the second cell subset, IgA nephropathy can be predicted / diagnosed, which has important guiding value for disease prognosis prediction and efficacy evaluation, and opens up a new vision for the clinical management of IgA nephropathy; it is a non-invasive clinical prediction method that can achieve non-invasive diagnosis. Attached Figure Description

[0049] Figure 1A This is a flow cytometry representation of cells;

[0050] Figure 1B This is a flow cytometry representation of a single cell;

[0051] Figure 1C This is a flow cytometry representation of live cells;

[0052] Figure 1D This is a flow cytometry representation of B cells;

[0053] Figure 1E This is a flow cytometry representation of IgA1+;

[0054] Figure 1F This is a flow cytometry representation of Gd-IgA1+;

[0055] Figure 2 This is a comparison chart of plasma Gd-IgA1 levels between the IgA nephropathy group and the healthy control group;

[0056] Figure 3 This is a comparison chart of Gd-IgA1 levels detected by combined flow cytometry in the IgA nephropathy group and the healthy control group;

[0057] Figure 4 This is a comparison chart showing the proportion of the first cell subset in the IgA nephropathy group and the healthy control group;

[0058] Figure 5 This is a comparison chart of the proportion of the second cell subset in B cells between the IgA nephropathy group and the healthy control group;

[0059] Figure 6 This is a comparison chart showing the proportion of the second cell subpopulation in the IgA nephropathy group and the healthy control group within the first cell subpopulation.

[0060] Figure 7 This is a flow cytometry comparison of the second cell subpopulation within the first cell subpopulation;

[0061] Figure 8 This is the ROC curve of the prediction model;

[0062] Figure 9 This is a logic block diagram of the IgA nephropathy detection system of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] The present invention will now be described in further detail with reference to the accompanying drawings:

[0065] The first aspect of this invention provides a method for detecting IgA nephropathy, comprising the following steps:

[0066] Step S1: Collect peripheral blood.

[0067] Step S2: Detect the level of Gd-IgA1 in peripheral blood plasma.

[0068] Step S3: Isolate mononuclear cells from the peripheral blood.

[0069] Step S4: Based on the fluorescent dye-labeled antibody (fluorescent antibody), screen for Gd-IgA1 and IgA1-positive B cells (IgA1+Gd-IgA1+ B cells), and record them as the first cell subset.

[0070] The 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), and PBS containing 2% FBS (P2F).

[0071] Step S5: Based on fluorescent dye-labeled antibodies (fluorescent antibodies), screen the first cell subpopulation for a second cell subpopulation (CD11c+CD27-) that is CD11c positive and CD27 negative, also known as atypical memory cell subpopulation.

[0072] Step S6: Using a predictive model, predict the probability of IgA nephropathy based on Gd-IgA1 levels, the proportion of the first cell subset, and the proportion of the second cell subset within the first subset.

[0073] Step S7: Determine if IgA nephropathy is diagnosed based on the probability and threshold. For example, if the probability is greater than 50% or 80%, it is diagnosed as IgA nephropathy.

[0074] Using three indicators—Gd-IgA1 level, the proportion of the first cell subset, and the proportion of the second cell subset—to predict / diagnose IgA nephropathy has significant guiding value for disease prognosis prediction and efficacy evaluation, opening up new horizons for the clinical management of IgA nephropathy; it is a non-invasive clinical prediction method that can achieve non-invasive diagnosis.

[0075] The specific testing methods are as follows:

[0076] Step 101: Collect 10ml of peripheral blood in an EDTA anticoagulant tube, centrifuge at 3000rpm for 10 minutes, and freeze the obtained supernatant plasma at -80℃ for later use.

[0077] Step 102: Detect plasma Gd-IgA1 levels using an ELISA kit (Gd-IgA1 (Galactose-deficient IgA1) Assay Kit-IBL, IBL Japan, 27600). The detection was performed according to the instructions; this detection method is prior art and will not be described further in this invention.

[0078] Step 103: Peripheral blood mononuclear cells (PBMCs) were extracted using density gradient centrifugation, frozen at -80°C, and then transferred to liquid nitrogen.

[0079] Step 104: Resuspend the mononuclear cells in complete RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin.

[0080] Step 105: Block Fc receptors at 4°C for 10 minutes using an FcR blocking reagent to prevent nonspecific staining.

[0081] Step 106: After incubating with rat anti-human Gd-IgA1 antibody (Anti-Human Gd-IgA1 Rat IgGMoAb) at 37°C for 1 hour, wash twice with P2F. The specific working concentration is 20 μg / ml of P2F.

[0082] Step 107: Incubate rat anti-human Gd-IgA1 with PE anti-rat IgG2b antibody at room temperature in the dark for 30 minutes, then wash twice with P2F.

[0083] Step 108: Stain and label 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.

[0084] Step 109: Stain the dead cells using 7-AAD active staining solution (BioLegend, USA).

[0085] Step 110: After staining, the samples were detected and analyzed using a DxFlex cytometer (Beckman Coulter). This instrument is equipped with violet (405 nm), blue (488 nm), and red (638 nm) lasers. Detailed analysis was performed using FlowJo software version 10.8.1 (BDLife Sciences, USA).

[0086] Step 111: Screening for the first cell subpopulation.

[0087] First, perform feature analysis based on cell size to delineate the cells (Cells), such as... Figure 1A Then, double cells and clumps are excluded to separate single cells (Singlets), such as... Figure 1B Next, live cells are identified, such as... Figure 1C Then select CD19+ cells (CD19+) and circle the B cells, such as... Figure 1D Subsequently, gating (IgA1+) subsets of B cells was performed, such as... Figure 1E Finally, gating (Gd-IgA1+) of the Gd-IgA1+ subset within the IgA1+ B cell subset was performed, such as... Figure 1F Wherein, FSC (Forward scatter) represents forward scattered light; SSC (Side scatter) represents side scattered light; H is the height of the electrical 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 a cell to pass through the laser detection area; and A (area) refers to the area of ​​the electrical pulse signal.

[0088] Step 112: Screening for the second cell subpopulation.

[0089] Gating strategy for atypical memory cell subsets in the first cell subset: CD11c+CD27-. That is, screening for CD11c-positive and CD27-negative cells.

[0090] Step 113: Build a prediction model.

[0091] The specific prediction indicators used are: Gd-IgA1 level, the proportion of the first cell subpopulation, and the proportion of the second cell subpopulation within the first subpopulation. The prediction model can be trained using machine learning methods; in one specific embodiment, logistic regression is used for training, but it is not limited to this.

[0092] In the specific training, plasma Gd-IgA1 levels were measured in 57 patients with IgA nephropathy (IgAN) and 22 healthy individuals (healthy control, HC) (a large cohort). See [link to relevant documentation]. Figure 2 The plasma Gd-IgA1 level in the IgA nephropathy group was significantly higher than that in the healthy control group. This indicates that elevated plasma Gd-IgA1 levels may serve as a biomarker for IgA nephropathy, but there is considerable heterogeneity among individuals.

[0093] Blood samples from 14 patients with IgA nephropathy and 11 healthy individuals (small sample cohort) were randomly selected for plasma fluorescent antibody detection combined with flow cytometry. 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) is increased in the IgA nephropathy group. For example... Figure 5 The proportion of the second cell subset in B cells from the IgA nephropathy group was significantly increased. For example... Figure 6 The proportion of the second cell subset in the IgA nephropathy group was significantly increased in the first cell subset. Figure 7This is a flow cytometry comparison of the second cell subpopulation in the IgA nephropathy group and the healthy control group in the first cell subpopulation, namely the first cell subpopulation gate (Gate on gA1+Gd-IgA1+ B Cells), where the horizontal axis represents the signal of CD11c and the vertical axis represents the signal of CD27.

[0094] Although there was no significant difference in plasma Gd-IgA1 levels in the small sample cohort, considering that plasma Gd-IgA1 is a widely accepted biomarker for IgAN and that the difference was statistically significant in the large sample cohort, three indicators—plasma Gd-IgA1 levels, the proportion of IgA1+Gd-IgA1+ B cells, and the percentage of CD11c+CD27- subsets in IgA1+Gd-IgA1+ B cells—were included in a binary logistic regression analysis to obtain a predictive model based on logistic regression:

[0095] Logit(p) = -21.923+0.341*A1+136.051*B1+0.451*B2.

[0096] Where p represents the probability of IgA nephropathy, A1 represents the Gd-IgA1 level, B1 represents the percentage of the first cell subset (%), and B2 represents the percentage of the second cell subset within the first cell subset (%). The AUC of each univariate and logistic regression model was calculated using ROC curves. Except for the plasma Gd-IgA1 level, the other two indicators were statistically significant, such as... Figure 8 The logistic regression prediction model combining three indicators achieved an AUC of 0.981 (95% CI: 0.935-1.000). The AUC for the proportion of the second cell subpopulation within the first cell subpopulation reached 0.922, and the AUC for the proportion of the first cell subpopulation was 0.838. The x-axis represents 1-specificity, and the y-axis represents sensitivity. This demonstrates that using the AUC of the proportion of the first cell subpopulation and / or the proportion of the second cell subpopulation within the first cell subpopulation is sufficient to meet the requirements of the prediction model, and one or both of these indicators can be used for training the prediction model.

[0097] It should be noted that the coefficients of various indicators differ depending on the training samples; this invention uses the proportion of the second cell subpopulation in the first cell subpopulation; however, the prediction model can still be trained by replacing it with the proportion of the second cell subpopulation in B cells. Overall, the prediction model can be expressed as:

[0098] Logit(p) = L0+L1*A1+L2*B1+L3*B2

[0099] Where L0, L1, L2, and L3 represent coefficients.

[0100] Other machine learning methods can also be used to train the prediction model, such as the Random Forest (RF) model, the Support Vector Machine (SVM) model, and the Generalized Linear Model (GLM). These training methods are existing technologies and will not be elaborated upon in this invention.

[0101] A second aspect of the present invention provides a detection kit for implementing the above-described detection method, comprising Gd-IgA1 fluorescent antibody, IgA1 fluorescent antibody, CD19 fluorescent antibody, CD11c fluorescent antibody, and CD27 fluorescent antibody.

[0102] The test kit also includes: plasma Gd-IgA1 detection reagent, FcR blocking reagent, and cell viability staining agent.

[0103] The Gd-IgA1 detection fluorescent antibody used was a combination of rat anti-human Gd-IgA1 antibody and PE anti-rat IgG2b antibody; the IgA1 fluorescent antibody used was IgA1-FITC; the CD19 fluorescent antibody used was CD19-APC-cy7; the CD11c fluorescent antibody used was CD11c-PE / Dazzle 594; and the CD27 fluorescent antibody used was CD27-BV510. The plasma Gd-IgA1 detection reagent used was the Gd-IgA1 ELISA reagent; and the cell viability staining agent used was 7-AAD.

[0104] A third aspect of this invention provides a detection system for IgA nephropathy, such as... Figure 9 As shown, it includes a data acquisition module 1 and a prediction module 5;

[0105] The acquisition module 1 is used to obtain the detection value of the test kit;

[0106] The prediction module 5 is used to analyze the detection value based on the prediction model to obtain the probability of having IgA nephropathy.

[0107] The acquisition module 1 includes a plasma detection submodule 2, a flow cytometry submodule 3, and a gating submodule 4. The plasma detection submodule is used to acquire the level of Gd-IgA1 in plasma. The flow cytometry submodule 3 is used to acquire flow cytometry signals based on fluorescently labeled antibodies. The gating submodule 4 is used to screen out a first cell subpopulation and a second cell subpopulation based on the flow cytometry signals, and to calculate the proportion of the first cell subpopulation and the second cell subpopulation.

[0108] The detection system also includes a training module 6, which is used to train the training set based on machine learning methods to obtain a prediction model.

[0109] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A detection system, characterized in that, Includes a data acquisition module and a prediction module; The acquisition module is used to obtain the detection value of the detection kit; the detection kit includes Gd-IgA1 fluorescent antibody, IgA1 fluorescent antibody, CD19 fluorescent antibody, CD11c fluorescent antibody, CD27 fluorescent antibody, plasma Gd-IgA1 detection reagent, FcR blocking reagent, and cell viability staining agent. The acquisition module includes a plasma testing submodule, a flow cytometry submodule, and a gating submodule. The plasma testing submodule is used to collect the level of Gd-IgA1 in plasma; The flow cytometry submodule is used to acquire flow cytometry signals based on antibodies labeled with fluorescent dyes. The gating submodule is used to filter out the first cell subpopulation and the second cell subpopulation based on the flow cytometry signal, and to count the proportion of the first cell subpopulation and the second cell subpopulation. The acquisition module uses CD19+ to identify B cells; it uses IgA1+ and Gd-IgA1+ to screen out the first cell subpopulation and obtain the percentage of the first cell subpopulation; it uses CD11c+ and CD27- to screen out the second cell subpopulation and obtain the percentage of the second cell subpopulation as well as the percentage of the second cell subpopulation in the first cell subpopulation. The prediction module is used to analyze the detection value according to the prediction model to obtain the probability of having IgA nephropathy. The prediction model is a logistic regression model, expressed as: Logit(p) = -21.923+0.341×A1+136.051×B1+0.451×B2; Where p represents the probability of IgA nephropathy, A1 represents the Gd-IgA1 level, B1 represents the proportion of the first cell subset, and B2 represents the proportion of the second cell subset in the first cell subset.

2. The detection system according to claim 1, characterized in that, The Gd-IgA1 fluorescent antibody is a combination of rat anti-human Gd-IgA1 antibody and PE anti-rat IgG2b antibody; The IgA1 fluorescent antibody is IgA1-FITC; the CD19 fluorescent antibody is CD19-APC-cy7; The CD11c fluorescent antibody is CD11c-PE / Dazzle 594; The CD27 fluorescent antibody is CD27-BV510.

3. The detection system according to claim 1, characterized in that, The plasma Gd-IgA1 detection reagent was the Gd-IgA1 ELISA reagent; the cell viability staining agent used was 7-AAD.

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