Method for sequencing and subpopulation analysis of b cell immune repertoire of mice with sjogren's syndrome model

Abstract

The application relates to the field of molecular immunology and biotechnology, and particularly relates to a method for sequencing and analyzing a B cell immune repertoire of a mouse model of Sjogren's syndrome. The method comprises the following steps: S1, sample preparation: preparing a single cell suspension from preset tissues of a target autoimmune disease animal model and a control animal, and performing B cell enrichment treatment on the single cell suspension to obtain a B cell sample; S2, parallel analysis of multidimensional data: analyzing the B cell sample to construct parallel multidimensional data; S3, integrated correlation analysis: performing correlation analysis on the immune repertoire data, cell subpopulation data and signal pathway data obtained in the step S2 to establish the correlation between specific B cell clone characteristics, specific B cell subpopulation changes and specific signal pathway activity, and to analyze and generate a B cell immune response mechanism of an autoimmune disease in multiple levels. The application significantly improves the efficiency and accuracy of disease mechanism research and drug mechanism evaluation.

Description

Technical Field

[0001] This application relates to the fields of molecular immunology and biotechnology, and in particular to a method for sequencing and subpopulation analysis of B cell immune repertoires in a mouse model of Sjögren's syndrome. Background Technology

[0002] Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease characterized by lymphocyte infiltration of exocrine glands and the production of autoantibodies. Abnormal activation and proliferation of B lymphocytes are the core components of pSS pathogenesis, and the resulting autoantibodies (such as anti-SSA / Ro and anti-SSB / La) and excessive immunoglobulins lead to tissue damage and various clinical manifestations. Studies have shown that B cell activation is closely related to B cell activating factor (BAFF) and its mediated NF-κB signaling pathway.

[0003] Currently, research on pSS typically uses non-obese diabetic (NOD) mice as a classic animal model. This model can spontaneously generate salivary gland inflammation and functional decline similar to human pSS. When studying the role of B cells, conventional techniques such as flow cytometry (detecting surface markers such as CD19, CD27, and CD138) and ELISA / Western Blot (detecting BAFF and NF-κB pathway proteins) are widely used. However, these methods have the following limitations:

[0004] Limitations of flow cytometry: It can only perform superficial grouping of B cells (such as mature B cells, memory B cells, and plasma cells) based on a limited number of known surface markers, and cannot deeply analyze the diversity of B cell receptors (BCRs), clonal expansion, and antigen-driven specific immune response characteristics.

[0005] Mechanism studies are fragmented: Traditional methods struggle to systematically link changes in B cell clonal types (reflecting antigen specificity) with their functional states (activation, differentiation) and dynamic changes in downstream signaling pathways (such as BAFF-NF-κB), failing to provide a comprehensive view of the B cell immune profile remodeling process under disease progression or drug intervention.

[0006] Lack of standardized integrated analysis protocols: There is currently a lack of a standardized and efficient technical protocol for systematically obtaining B cells from complex tissues (such as salivary glands and spleen) of pSS model mice, performing immune repertoire sequencing, and integrating the sequencing data with multidimensional phenotypic and molecular pathway data for analysis.

[0007] Therefore, developing an integrated analysis method capable of deeply analyzing the clonal diversity, subpopulation characteristics, and associations with key signaling pathways of B cells in pSS model mice is of great significance for elucidating disease mechanisms, discovering new therapeutic targets, and evaluating drug efficacy. Summary of the Invention

[0008] This application provides a method for sequencing and subset analysis of B-cell immune repertoire in a mouse model of Sjögren's syndrome to solve the above-mentioned problems. The method includes:

[0009] S1. Sample preparation: Prepare single-cell suspensions from the target autoimmune disease animal model and the control animals, and perform B cell enrichment treatment on the single-cell suspensions to obtain B cell samples.

[0010] S2. Parallel Analysis of Multidimensional Data: Analyze the B cell samples and construct multidimensional data in parallel:

[0011] Immune repertoire data: High-throughput sequencing of the B cell samples was performed on the B cell receptor (BCR) to generate immune repertoire sequence data reflecting the composition and diversity of B cell clones;

[0012] Cell subpopulation data: Based on a preset combination of B cell differentiation and functional markers, multi-parameter flow cytometry analysis was performed on the single cell suspension or B cell sample to obtain quantitative proportion data of each B cell functional subpopulation.

[0013] Signaling pathway data: Detection of expression or activity levels of key signaling pathway molecules related to B cell activation and survival in the preset tissue;

[0014] S3. Integration and Correlation Analysis: The immune repertoire data, cell subpopulation data and signaling pathway data obtained in step S2 are subjected to correlation analysis to establish the correlation between specific B cell clonal characteristics, specific B cell subpopulation changes and specific signaling pathway activities, and to analyze and generate the B cell immune response mechanism of the autoimmune disease at multiple levels.

[0015] Through the aforementioned technical solutions, in terms of depth, the immune repertoire data provides fine-grained "identity information" at the B cell clonal level, enabling the identification of disease-related specific clones—something traditional flow cytometry cannot provide. In terms of breadth, it simultaneously acquires "functional state information" (flow cytometry subset data) and "environmental signaling information" (pathway data) of B cells, achieving a three-dimensional integration of multi-level data. This integrated correlation analysis (S3) can directly link specific B cell clonal expansion and plasma cell differentiation, as well as changes in the activity of key pathogenic signaling pathways such as BAFF-NF-κB. This provides a powerful technical tool for elucidating which pathways (such as ellamod) influence and thereby regulate which specific B cell clones to exert their therapeutic effects, demonstrating strong mechanism-revealing capabilities. Furthermore, this method provides a standardized workflow from animal model selection, sample processing, experimental techniques to data analysis, exhibiting standardization and scalability. This method can be extended to B cell immunology research in other autoimmune diseases (such as rheumatoid arthritis and systemic lupus erythematosus), significantly improving the efficiency and accuracy of disease mechanism research and drug action mechanism evaluation. Through quantitative analysis, this invention can quantitatively correlate B cell clonal expansion with molecular pathway activation, overcoming the data fragmentation defects in existing technologies and providing a new diagnostic and therapeutic target discovery tool for precision medicine.

[0016] Optionally, in step S1, the target autoimmune disease animal model is a primary Sjögren's syndrome model mouse.

[0017] The pre-defined tissues include salivary gland tissue as the primary target organ and spleen tissue as a secondary lymphatic organ.

[0018] The B cell enrichment process includes: specifically separating a population of B lymphocytes from the single-cell suspension using immunomagnetic sorting or flow cytometry based on pan-B cell surface markers, and using the B lymphocyte population as the B cell sample.

[0019] The above technical solutions clarify the animal model and tissue source, enhancing the specificity and targeting of the research. The combination of salivary gland tissue (lesion site) and spleen tissue (systemic immune center) comprehensively captures the local infiltration and systemic activation of B cells during the development of pSS. Immunomagnetic sorting or flow cytometry for B cell enrichment yields high-purity B cell samples, increasing the proportion of B cells in the samples to over 90%. This significantly reduces background noise in subsequent BCR high-throughput sequencing, improving the effective sequence ratio and clone identification accuracy of the immune repertoire data. Simultaneously, high-purity B cell samples ensure the accuracy of multi-parameter flow cytometry analysis, avoiding interference from non-B cells in the quantitative analysis of subpopulation proportions, allowing cell subpopulation data to more accurately reflect the functional state of B cells. This precise sample preparation is fundamental to the subsequent multidimensional data integration and correlation analysis (S3).

[0020] Optionally, the primary Sjögren's syndrome (pSS) model mice are non-obese diabetic mice;

[0021] The control animals were C57BL / 6 strain mice of the same age and sex;

[0022] The method further includes, before step S1, randomly dividing the non-obese diabetic mice into at least three groups: a disease model group, a positive drug control group, and a drug treatment group, and subjecting the drug treatment group and the positive drug control group to drug intervention for a predetermined period of time.

[0023] The grouped intervention design, employing the aforementioned technical approach, ensures the reliability and interpretability of the experimental results. Comparing the drug treatment group with the disease model group allows for direct assessment of the drug's therapeutic effect; comparing it with the positive drug control group allows for evaluation of the drug's efficacy intensity and the similarity or difference in its mechanism of action. This standardized drug intervention process makes this method not only suitable for disease mechanism research but also a highly efficient tool for drug screening and mechanism of action evaluation. Subsequent S3 integrative correlation analysis can directly reveal which specific B cell clones the drug inhibits and which key signaling pathways it regulates to achieve its therapeutic effect, significantly accelerating the new drug development process.

[0024] Optionally, the preparation of the single-cell suspension and the B-cell enrichment treatment of the single-cell suspension include:

[0025] The salivary gland tissue and spleen tissue were mechanically ground and filtered to obtain an initial cell suspension.

[0026] The initial cell suspension was treated with erythrocyte lysis buffer to remove erythrocytes;

[0027] The initial cell suspension after removing red blood cells was centrifuged using density gradient centrifugation to remove the lymphocyte population, resulting in the single-cell suspension for sorting.

[0028] The enrichment based on pan-B cell surface markers specifically involves using immunomagnetic beads conjugated with anti-CD19 monoclonal antibodies to positively sort the single-cell suspension to obtain high-purity CD19+B cell samples.

[0029] The above technical solutions ensured the standardization and high efficiency of sample preparation. Mechanical grinding and filtration guaranteed the homogeneity and cell viability of the single-cell suspension. Red blood cell lysis and density gradient centrifugation effectively removed red blood cells, dead cells, and tissue debris, avoiding interference with subsequent experiments. Positive sorting using anti-CD19 immunomagnetic beads is a highly efficient and gentle method for B cell enrichment, capable of obtaining high-purity CD19+ B cell samples in a short time, typically achieving a purity of over 90%. This significantly improves the effective sequence yield of BCR high-throughput sequencing, thereby ensuring the reliability of the immune repertoire data and laying a solid sample foundation for subsequent clonogenic analysis.

[0030] Optionally, the generation of the immune repertoire sequence data includes:

[0031] The raw sequence data obtained from high-throughput sequencing is subjected to quality control and filtering to remove low-quality reads and adapter sequences, resulting in a number of qualified reads.

[0032] The qualified reads are spliced ​​together and compared with the B cell receptor reference gene library to identify gene fragments in the variable region, diversity region, and linker region, as well as the nucleotide and amino acid sequences of complementarity-determining region 3.

[0033] Based on the uniqueness of the nucleotide and amino acid sequences of the complementarity-determining region 3, different B cell clones are identified, and the frequency distribution of each clone is calculated to construct the immune repertoire sequence data, which includes indicators of clonal composition, clonal amplification degree, and repertoire diversity.

[0034] The above technical solutions ensured the accuracy and comparability of the immune repertoire sequence data. Rigorous quality control effectively avoided the introduction of sequencing errors and false-positive clones. Clone identification based on CDR3 sequences accurately distinguished different antigen-specific B cell populations. By calculating clonal frequency and diversity indicators, the degree of clonal expansion of B cells and the remodeling of the immune repertoire under disease conditions could be quantified. In particular, it was able to identify "disease-associated clones" that significantly expanded in the disease model group; these clones are considered molecular markers of pathogenic B cells, providing crucial "identity information" for subsequent integrative association analysis (S3).

[0035] Optionally, the preset combination of B cell differentiation and functional markers is configured to simultaneously distinguish B cell subsets at different differentiation stages and functional states, including:

[0036] Markers for all mature B cells, markers for activated B cells, markers for memory B cells, and markers for antibody-secreting cells;

[0037] The multi-parameter flow cytometry analysis includes: simultaneously detecting the combination of biomarkers using specific monoclonal antibodies conjugated with different fluorescent dyes, quantitatively analyzing the proportion of each subpopulation of naive B cells, activated B cells, memory B cells and plasma cell-like cells in the sample, and obtaining the quantitative proportion data of each functional subpopulation of B cells.

[0038] The combination of biomarkers includes: CD19 as a pan-B cell biomarker; CD135 (FLT-3) as a mature / activated B cell biomarker; CD27 as a memory B cell biomarker; and plasma cell antigen-1 (PCA-1) or CD138 as a biomarker for antibody-secreting cells (plasma cells).

[0039] The aforementioned technical approach enabled the precise differentiation and quantification of core B-cell subsets in pSS pathology. In particular, the accurate quantification of the plasma cell subset (PCA-1+) provided crucial "functional status information" for subsequent integrative correlation analysis (S3). The high sensitivity and high throughput of multi-parameter flow cytometry ensured accurate acquisition of proportion data for each subset even with limited sample sizes. This detailed subdivision of B-cell functional subsets helps reveal the dynamic changes in the B-cell differentiation profile under disease progression or drug intervention, thereby providing a better understanding of disease mechanisms and drug targets.

[0040] Optionally, the key signaling pathway related to B cell activation and survival is the BAFF-NF-κB signaling pathway;

[0041] The acquisition of the signaling pathway data includes: detecting the molecular expression or activity levels of key nodes in the pathway using one or more of the following techniques: Western blotting, enzyme-linked immunosorbent assay (ELISA), and quantitative reverse transcription polymerase chain reaction (qRT-PCR).

[0042] The key nodes include: the upstream ligand BAFF, its receptor BAFFR, and the core transcription factor subunits p65 and p50 of the downstream NF-κB pathway, the regulatory protein IκBα, and the pathway-related effector cytokine IL-6.

[0043] By employing the aforementioned technical solutions and focusing on the core pathogenic pathway BAFF-NF-κB, and utilizing multiple technology platforms for detection, the comprehensiveness and accuracy of signaling pathway data are ensured. Simultaneous detection of upstream ligands, receptors, core transcription factors, and their activities (phosphorylation) provides a complete picture of the pathway's activation status. For example, quantifying p65 phosphorylation levels is the gold standard for assessing NF-κB pathway activation. This precise molecular-level quantification provides crucial "environmental signaling information" for subsequent S3 integration and association analysis, enabling the establishment of a direct quantitative correlation between B cell clonal expansion and specific molecular pathway activation, thereby deepening our understanding of the pathogenic mechanism.

[0044] Optionally, the molecular expression or activity levels of key nodes in the detection pathway include:

[0045] The concentrations of soluble BAFF and IL-6 in mouse serum or tissue homogenate supernatant were detected using an ELISA kit.

[0046] Western blotting was used to detect the protein expression level of BAFFR and the phosphorylation level of NF-κB p65 protein (p-p65) in lysates of spleen or salivary gland tissue. The total p65 protein was used as an internal reference to calculate its phosphorylation ratio to assess the activation status of the NF-κB pathway.

[0047] The mRNA expression levels of BAFF, BAFFR, and NF-κB1 (p50) in the pre-selected tissue were detected using qRT-PCR technology.

[0048] The above technical solutions ensured the accuracy and operability of signaling pathway data. Using the p-p65 / Totalp65 ratio as an indicator of NF-κB pathway activation status avoided biases arising from relying solely on total protein quantity or single phosphorylation levels. ELISA detection of soluble factors provided quantitative information on microenvironmental signals. qRT-PCR provided evidence at the transcriptional level. This multi-technology, multi-level detection strategy made the assessment of the BAFF-NF-κB pathway more comprehensive and accurate, providing high-quality molecular pathway data for subsequent S3 integration and association analysis.

[0049] Optionally, in step S3, the correlation analysis includes:

[0050] The frequency of "disease-related clones" was extracted from the immune repertoire data as the first key variable;

[0051] The proportion of plasma cell subpopulation (PCA-1+) was extracted from the cell subpopulation data as a second key variable;

[0052] Serum BAFF concentration or tissue p-p65 / p65 ratio was extracted from the signaling pathway data as a third key variable;

[0053] Using statistical correlation analysis, the correlation coefficients between the first key variable and the second and third key variables were calculated, and a quantitative correlation network among the three factors of "specific clonal expansion - plasma cell differentiation - BAFF-NF-κB pathway activation" was established at the data level.

[0054] The aforementioned technical solution overcomes the data fragmentation inherent in traditional research, enabling a systematic understanding of the pathogenesis of pSS. By calculating correlation coefficients, the strength of the biological links between specific clonal expansion, plasma cell differentiation, and pathway activation can be quantified, providing direct evidence for the origin, differentiation, and molecular pathway regulation mechanisms of pathogenic B cells. This correlation analysis capability allows this method to precisely pinpoint the core driving factors of the disease, providing strong data support for the discovery of new diagnostic biomarkers and therapeutic targets.

[0055] Optionally, the method further includes:

[0056] The first key variable, the second key variable, and the third key variable of the drug treatment group to be tested are compared with the corresponding variables of the disease model group, respectively.

[0057] If all variable values ​​in the drug treatment group show a statistically significant decrease, and the degree of decrease is consistent with the trend of the positive drug control group, then the drug is determined to exert a therapeutic effect on Sjögren's syndrome by inhibiting the BAFF-NF-κB signaling pathway, thereby depleting pathogenic B cell clones and plasma cells.

[0058] The aforementioned technical solutions significantly improve the efficiency and depth of drug screening and mechanism research. By simultaneously monitoring clonal, phenotypic, and molecular pathway activity, the drug's target and pathway of action can be clearly revealed, avoiding the one-sidedness of mechanism research in traditional methods. For example, if a drug only reduces the proportion of plasma cells (the second variable) but does not significantly affect the frequency of specific clones (the first variable), it may indicate that the drug acts on plasma cell survival rather than clonal expansion. This method, through the linkage analysis of three variables, can systematically elucidate the mechanism by which drugs (such as ellamod) treat pSS by regulating specific signaling pathways and affecting specific B cell clones, providing a scientific basis for the clinical translation and precision medicine of drugs. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating a method for sequencing and subpopulation analysis of B-cell immune repertoires in a mouse model of Sjögren's syndrome, as provided in an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0062] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0063] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0064] Currently, research on pSS typically uses non-obese diabetic (NOD) mice as a classic animal model. This model can spontaneously generate salivary gland inflammation and functional decline similar to human pSS. When studying the role of B cells, conventional techniques such as flow cytometry (detecting surface markers such as CD19, CD27, and CD138) and ELISA / Western Blot (detecting BAFF and NF-κB pathway proteins) are widely used. However, these methods have the following limitations:

[0065] Limitations of flow cytometry: It can only perform superficial grouping of B cells (such as mature B cells, memory B cells, and plasma cells) based on a limited number of known surface markers, and cannot deeply analyze the diversity of B cell receptors (BCRs), clonal expansion, and antigen-driven specific immune response characteristics.

[0066] Mechanism studies are fragmented: Traditional methods struggle to systematically link changes in B cell clonal types (reflecting antigen specificity) with their functional states (activation, differentiation) and dynamic changes in downstream signaling pathways (such as BAFF-NF-κB), failing to provide a comprehensive view of the B cell immune profile remodeling process under disease progression or drug intervention.

[0067] Lack of standardized integrated analysis protocols: There is currently a lack of a standardized and efficient technical protocol for systematically obtaining B cells from complex tissues (such as salivary glands and spleen) of pSS model mice, performing immune repertoire sequencing, and integrating the sequencing data with multidimensional phenotypic and molecular pathway data for analysis.

[0068] Based on this, this application provides a method for sequencing and subpopulation analysis of B-cell immune repertoires in a mouse model of Sjögren's syndrome. In terms of depth, the immune repertoire data provides fine-grained "identity information" at the B-cell clonal level, enabling the identification of disease-specific clones, which is unavailable with traditional flow cytometry. In terms of breadth, it simultaneously acquires "functional state information" (flow cytometry subpopulation data) and "environmental signaling information" (pathway data) of B cells, achieving a three-dimensional integration of multi-level data. This integrated correlation analysis (S3) can directly correlate specific B-cell clonal expansion and plasma cell differentiation, as well as changes in the activity of key pathogenic signaling pathways such as BAFF-NF-κB. This provides a powerful technical tool for elucidating which pathways (such as ellamod) influence and thereby regulate which specific B-cell clones to exert therapeutic effects, demonstrating strong mechanism revealing capabilities. Furthermore, this method provides a standardized workflow from animal model selection, sample processing, experimental techniques to data analysis, exhibiting standardization and scalability. This method can be extended to B-cell immunological research in other autoimmune diseases (such as rheumatoid arthritis and systemic lupus erythematosus), significantly improving the efficiency and accuracy of disease mechanism research and drug action mechanism evaluation. Through quantitative analysis, this invention can quantitatively correlate B cell clonal expansion with molecular pathway activation, overcoming the data fragmentation defects in existing technologies and providing a new diagnostic and therapeutic target discovery tool for precision medicine.

[0069] For specific implementation details, please refer to the following examples.

[0070] Figure 1 A flowchart illustrating a method for sequencing and subset analysis of B-cell immune repertoire in a mouse model of Sjögren's syndrome, as provided in an embodiment of this application, is shown below. Figure 1 As shown, the method includes:

[0071] S1. Sample preparation: Prepare single-cell suspensions from the target autoimmune disease animal model and the control animals, and perform B cell enrichment treatment on the single-cell suspensions to obtain B cell samples.

[0072] S2. Parallel Analysis of Multidimensional Data: Analyze the B cell samples and construct multidimensional data in parallel:

[0073] Immune repertoire data: High-throughput sequencing of the B cell samples was performed on the B cell receptor (BCR) to generate immune repertoire sequence data reflecting the composition and diversity of B cell clones;

[0074] Cell subpopulation data: Based on a preset combination of B cell differentiation and functional markers, multi-parameter flow cytometry analysis was performed on the single cell suspension or B cell sample to obtain quantitative proportion data of each B cell functional subpopulation.

[0075] Signaling pathway data: Detection of expression or activity levels of key signaling pathway molecules related to B cell activation and survival in the preset tissue;

[0076] S3. Integration and Correlation Analysis: The immune repertoire data, cell subpopulation data and signaling pathway data obtained in step S2 are subjected to correlation analysis to establish the correlation between specific B cell clonal characteristics, specific B cell subpopulation changes and specific signaling pathway activities, and to analyze and generate the B cell immune response mechanism of the autoimmune disease at multiple levels.

[0077] Current research on autoimmune diseases such as primary Sjögren's syndrome (pSS) typically employs traditional techniques such as flow cytometry and ELISA / Western Blot, but these methods have limitations. Flow cytometry can only perform superficial B cell clustering based on limited surface markers, failing to deeply analyze the diversity and clonal expansion of B cell receptors (BCRs), thus failing to reflect the characteristics of antigen-driven specific immune responses. Furthermore, traditional methods struggle to systematically correlate B cell clonal changes (reflecting antigen specificity) with their functional states (activation, differentiation) and the dynamic changes in downstream signaling pathways (such as BAFF-NF-κB), leading to fragmented mechanistic studies and an inability to comprehensively reveal the remodeling of the B cell immune profile under disease progression or drug intervention. This invention, based on the principles of real-time signal processing and multi-omics data integration, achieves a deep and comprehensive analysis of the B cell immune response mechanism by constructing immune repertoire data, cell subset data, and signaling pathway data in parallel. The technical basis of this method lies in the fact that the specific immune response of B cells is determined by their BCR clonal type, their functional state (such as differentiation into plasma cells) is reflected by cell surface markers, and their activation and survival are regulated by key signaling pathways (such as BAFF-NF-κB). By establishing a quantitative correlation network among these three factors through S3 integrative correlation analysis, the limitations of data fragmentation in existing technologies can be overcome, thereby systematically elucidating the origin, differentiation, and molecular pathway regulation mechanisms of pathogenic B cells.

[0078] The method flow of the present invention is as follows: Figure 1 As shown, the process includes three core steps: sample preparation (S1), parallel multidimensional data analysis (S2), and integrated correlation analysis (S3). In S1, sample preparation, single-cell suspensions are prepared from pre-selected tissues of target autoimmune disease animal models (e.g., pSS mice) and control animals using methods such as mechanical grinding and enzymatic digestion. B cell enrichment is then performed to obtain high-purity B cell samples. B cell enrichment is a crucial step, designed to ensure sample quality for subsequent immune repertoire sequencing. In S2, parallel multidimensional data analysis, immune repertoire data, cell subset data, and signaling pathway data are constructed in parallel. Immune repertoire data is obtained through high-throughput sequencing of BCRs, reflecting the clonal composition and diversity of B cells, and is fundamental for identifying specific pathogenic clones. Cell subset data is obtained through multi-parameter flow cytometry analysis, used to quantitatively analyze the proportion of B cell subsets in different functional states (e.g., plasma cell subsets), reflecting the functional state of B cells. Signaling pathway data are obtained by detecting the expression or activity levels of molecules related to key signaling pathways (such as the BAFF-NF-κB pathway) in predefined tissues, reflecting the microenvironment signals of B cells. In the S3 integrative correlation analysis, three parallel datasets are analyzed for correlation to establish a quantitative correlation network between specific B cell clonal characteristics, changes in specific B cell subsets, and the activity of specific signaling pathways, achieving multi-level analysis. The innovation of this integrative analysis lies in its coupling of three independent dimensions of data reflecting B cell specificity (clonal type), phenotype (subpopulation ratio), and molecular mechanism (pathway activity) into a logical closed loop, thereby systematically revealing the B cell immune response mechanism in autoimmune diseases.

[0079] This invention achieves a combination of depth and breadth through the innovative integration of three steps: S1, S2, and S3. In terms of depth, the immune repertoire data provides fine-grained "identity information" at the B cell clonal level, enabling the identification of disease-specific clones—something traditional flow cytometry cannot provide. In terms of breadth, it simultaneously acquires "functional state information" (flow cytometry subset data) and "environmental signaling information" (pathway data) of B cells, achieving a three-dimensional integration of multi-level data. This integrated correlation analysis (S3) directly links specific B cell clonal expansion and plasma cell differentiation, as well as changes in the activity of key pathogenic signaling pathways such as BAFF-NF-κB. This provides a powerful technical tool for elucidating which pathways (such as ellamod) influence and thereby regulate which specific B cell clones to exert their therapeutic effects, demonstrating strong mechanistic revelation capabilities. Furthermore, this method provides a standardized process from animal model selection, sample processing, experimental techniques to data analysis, exhibiting standardization and scalability. This method can be extended to B-cell immunology research in other autoimmune diseases (such as rheumatoid arthritis and systemic lupus erythematosus), significantly improving the efficiency and accuracy of disease mechanism research and drug action mechanism evaluation. Through quantitative analysis, this invention can quantitatively correlate B-cell clonal expansion with molecular pathway activation, overcoming the data fragmentation defects in existing technologies and providing a new diagnostic and therapeutic target discovery tool for precision medicine.

[0080] In some embodiments, in step S1, the target autoimmune disease animal model is a primary Sjögren's syndrome model mouse; the preset tissue includes salivary gland tissue as the main target organ and spleen tissue as a secondary lymphoid organ; the B cell enrichment treatment includes: specifically separating a population of B lymphocytes from the single-cell suspension based on pan-B cell surface markers using immunomagnetic sorting technology or flow cytometry, and using the population of B lymphocytes as the B cell sample.

[0081] In studies of primary Sjögren's syndrome (pSS), abnormal activation and proliferation of B cells are a core pathological element. To accurately capture the characteristics of B cell immune responses under disease conditions, appropriate animal models and tissue samples must be selected. Mice with primary Sjögren's syndrome, such as non-obese diabetic (NOD) mice, can spontaneously generate salivary gland inflammation and functional decline similar to human pSS, making them ideal models. Salivary gland tissue is a major target organ of pSS, exhibiting lymphocyte infiltration, reflecting the local immunopathology of the disease; spleen tissue, as a secondary lymphoid organ, is an important site for B cell maturation, activation, and differentiation, reflecting the systemic immune response. Therefore, simultaneously collecting samples from both salivary gland and spleen tissues can comprehensively cover the local and systemic immune states of the disease. B cell enrichment is a key technology to ensure the accuracy of subsequent high-throughput sequencing and flow cytometry analysis. Through specific isolation based on pan-B cell surface markers (such as CD19), a high-purity B lymphocyte population can be obtained, avoiding interference from non-B cells, thereby improving the effective sequence ratio of immune repertoire sequencing and the accuracy of cell subset analysis.

[0082] This embodiment specifically defines step S1. The target autoimmune disease animal model is defined as a primary Sjögren's syndrome model mouse. The preset tissues are clearly defined as salivary gland tissue and spleen tissue. In the sample preparation stage S1, salivary gland tissue and spleen tissue are first obtained from the model mouse and control animal, and single-cell suspensions are prepared by mechanical grinding and enzymatic digestion. Subsequently, B cell enrichment is performed. This enrichment is based on specific separation of pan-B cell surface markers (such as CD19), aiming to isolate B lymphocyte populations from complex single-cell suspensions. The enrichment technique can be either immunomagnetic sorting or flow cytometry. Immunomagnetic sorting utilizes magnetic beads coupled with anti-CD19 antibodies to specifically bind to B cells, separating them through a magnetic field, and is characterized by its simplicity and large processing capacity. Flow cytometry offers higher purity and accuracy, enabling more refined sorting. The isolated high-purity B lymphocyte population serves as the B cell sample required for subsequent multidimensional data parallel analysis S2. This tissue selection and enrichment process ensures the sample quality and representativeness of subsequent immune repertoire sequencing and cell subset analysis, thus guaranteeing the reliability of the data source.

[0083] This implementation method enhances the specificity and targeting of the study by clearly defining the animal model and tissue source. The combination of salivary gland tissue (lesion site) and spleen tissue (systemic immune center) allows for comprehensive capture of the local infiltration and systemic activation of B cells during the development of pSS. Immunomagnetic sorting or flow cytometry-based B cell enrichment techniques yield high-purity B cell samples, increasing the proportion of B cells in the samples to over 90%. This significantly reduces background noise in subsequent high-throughput BCR sequencing, improving the effective sequence ratio and clone identification accuracy of the immune repertoire data. Simultaneously, the high-purity B cell samples ensure the accuracy of multi-parameter flow cytometry analysis, avoiding interference from non-B cells in the quantitative analysis of subpopulation proportions, allowing cell subpopulation data to more accurately reflect the functional state of B cells. This precise sample preparation is fundamental to the subsequent multidimensional data integration and correlation analysis (S3).

[0084] In some embodiments, the primary Sjögren's syndrome (pSS) model mice are non-obese diabetic mice; the control animals are age- and sex-matched C57BL / 6 mice; the method further includes, before step S1, randomly dividing the non-obese diabetic mice into at least three groups: a disease model group, a positive drug control group, and a test drug treatment group, and subjecting the test drug treatment group and the positive drug control group to drug intervention for a predetermined period of time.

[0085] This implementation further clarifies the specific strains of animal models and the grouping strategy of experimental design, aiming to provide a standardized method for evaluating drug mechanisms of action. Non-obese diabetic (NOD) mice are a recognized classic animal model of primary Sjögren's syndrome (pSS), whose spontaneous salivary gland inflammation and functional decline are highly similar to the pathology of human pSS, making them representative for research. Selecting age- and sex-matched C57BL / 6 mice as normal controls is to eliminate the interference of genetic background and physiological differences on the experimental results. Grouping and drug intervention before sample preparation in S1 are key steps in applying this method to drug mechanism evaluation. By setting up a disease model group (no intervention or given solvent), a positive drug control group (given a known effective drug, such as leflunomide), and a test drug treatment group, changes in the immune repertoire, subsets, and signaling pathways of B cells before and after drug intervention can be systematically compared. This grouping design follows the gold standard of pharmacodynamic research, ensuring that the mechanistic changes revealed in the subsequent integrative correlation analysis S3 are caused by drug intervention, rather than natural disease progression or individual differences.

[0086] This implementation method adds animal grouping and drug intervention steps before S1 sample preparation. Week-old SPF-grade female NOD mice are selected as the disease model, as these mice have begun to show significant salivary gland lymphocyte infiltration. Age- and sex-matched C57BL / 6 mice are selected as control animals, serving as healthy controls with normal genetic backgrounds. NOD mice are randomly divided into at least three groups: a disease model group, a positive drug control group (e.g., using leflunomide), and a test drug treatment group (e.g., using ellamod). Drug intervention is performed via continuous gavage administration, typically for a predetermined period of 6 to 8 weeks, to ensure sufficient time for the drug to exert its effects and induce significant changes in immune status. After drug intervention, S1 sample preparation is performed, obtaining B cell samples from the salivary gland and spleen tissues of each group of mice. This rigorous experimental design ensures the comparability of data results from subsequent S2 multidimensional parallel data analysis and S3 integrated correlation analysis. In particular, by comparing the trends of key variables (such as disease-related clone frequency, plasma cell subset ratio, and BAFF concentration / p-p65 ratio) between the drug treatment group, the disease model group, and the positive drug control group, the efficacy and mechanism of action of the drug can be evaluated.

[0087] By using NOD mice as a model and setting up multiple control groups, the method of this invention can accurately assess the impact of test drugs on the pathological process of pSS. The grouped intervention design ensures the reliability and interpretability of the experimental results. Comparing the test drug treatment group with the disease model group allows for direct assessment of the drug's therapeutic effect; comparing it with the positive drug control group allows for assessment of the similarity or difference in the therapeutic intensity and mechanism of action of the test drug. This standardized drug intervention process makes this method not only suitable for disease mechanism research but also a highly efficient tool for drug screening and mechanism of action evaluation. Subsequent S3 integrative correlation analysis can directly reveal which specific B cell clones the drug inhibits the expansion of and which key signaling pathways it regulates to achieve its therapeutic effect, greatly accelerating the new drug development process.

[0088] In some embodiments, the preparation of the single-cell suspension and the enrichment of the single-cell suspension with B cells include: mechanically grinding and filtering the salivary gland tissue and spleen tissue respectively to obtain an initial cell suspension; treating the initial cell suspension with erythrocyte lysis buffer to remove erythrocytes; centrifuging the initial cell suspension after removing erythrocytes using density gradient centrifugation to remove the lymphocyte population, obtaining the single-cell suspension for sorting; the enrichment based on pan-B cell surface markers specifically involves positively sorting the single-cell suspension using immunomagnetic beads conjugated with anti-CD19 monoclonal antibodies to obtain high-purity CD19+ B cell samples.

[0089] This implementation details the specific operational procedures of step S1, aiming to ensure the acquisition of high-quality, high-purity B-cell samples from complex tissues. Salivary gland and spleen tissues are rich in extracellular matrix, erythrocytes, and various immune cells. Preparing a high-quality single-cell suspension is crucial for the success of subsequent experiments. Mechanical grinding and filtration are used to disperse the tissue into single cells and remove tissue debris. Erythrocyte lysis buffer treatment is used to remove erythrocytes from the blood, avoiding interference with subsequent sorting and sequencing. Density gradient centrifugation (e.g., using Ficoll or Percoll) is used to separate lymphocyte populations and remove dead cells and tissue debris. B-cell enrichment processing employs immunomagnetic bead positive sorting technology, specifically capturing B cells based on the CD19 marker. CD19 is a pan-marker specific to B cells and is expressed at all stages of B-cell development. Using immunomagnetic beads conjugated with anti-CD19 monoclonal antibodies for positive sorting can efficiently and rapidly separate high-purity CD19+B cell samples from complex single-cell suspensions, with a purity typically exceeding 90%, which is crucial for the success of subsequent BCR high-throughput sequencing.

[0090] In S1 sample preparation, the collected salivary gland and spleen tissues were first placed in sterile culture dishes, and an appropriate amount of buffer was added. The tissues were then mechanically homogenized using a scalpel or tissue homogenizer, followed by filtration through 70µm and 40µm cell sieves to obtain the initial cell suspension. For tissues rich in erythrocytes, such as the spleen, erythrocyte lysis buffer (e.g., ACK lysis buffer) was added and incubated on ice for 5-10 minutes to selectively lyse the erythrocytes. The lysis reaction was then terminated by centrifugation. Next, density gradient centrifugation was used. The cell suspension was carefully spread on a density gradient centrifugation buffer (e.g., Ficoll-Paque) and centrifuged at 400g for 20 minutes to remove dead cells and debris. The lymphocyte population located at the density interface was collected to obtain a single-cell suspension for sorting. Finally, B cell enrichment was performed. Immunomagnetic beads conjugated with anti-CD19 monoclonal antibodies were used to incubate the single-cell suspension according to the kit instructions. Anti-CD19 magnetic beads specifically bind to B cells. Positive sorting is performed using a magnetic separation column, while non-B cells are washed away, ultimately yielding a high-purity CD19+ B cell sample. This CD19+ B cell sample is used for subsequent immune repertoire sequencing and cell subset analysis.

[0091] The detailed steps in this implementation method ensure standardized and efficient sample preparation. Mechanical grinding and filtration guarantee the homogeneity and cell viability of the single-cell suspension. Red blood cell lysis and density gradient centrifugation effectively remove red blood cells, dead cells, and tissue debris, avoiding interference with subsequent experiments. Positive sorting using anti-CD19 immunomagnetic beads is a highly efficient and gentle method for B cell enrichment, enabling the acquisition of high-purity CD19+ B cell samples in a short time, typically exceeding 90% purity. This significantly improves the effective sequence yield of BCR high-throughput sequencing, thereby ensuring the reliability of the immune repertoire data and laying a solid sample foundation for subsequent clonogenic analysis.

[0092] In some embodiments, the generation of the immune repertoire sequence data includes: performing quality control and filtering on the raw sequence data obtained from high-throughput sequencing to remove low-quality reads and adapter sequences, obtaining several qualified reads; splicing the several qualified reads together and comparing them with a B cell receptor reference gene library to identify gene fragments in their variable region, diversity region, and linker region, as well as the nucleotide and amino acid sequences of complementarity-determining region 3; based on the uniqueness of the nucleotide and amino acid sequences of the complementarity-determining region 3, identifying different B cell clones, calculating the frequency distribution of each clone, and constructing the immune repertoire sequence data containing indicators of clonal composition, clonal amplification degree, and library diversity.

[0093] Immunome repertoire sequencing (BCR-seq) is a core technology for revealing B cell clonal diversity and specific immune responses. The specificity of BCRs is primarily determined by the combination of gene fragments from their heavy and light chains, specifically the variable region (V), diversity region (D), and linker region (J). Complementarity-determining region 3 (CDR3) is the most critical region for BCR antigen recognition, and its sequence uniqueness defines a unique B cell clone. This implementation details the bioinformatics analysis workflow for immunome repertoire data. First, rigorous quality control and filtering ensure the reliability of the data source. Then, CDR3 sequences are accurately identified through sequence assembly and alignment. Finally, B cell clones are identified based on the uniqueness of CDR3, and their frequency and diversity are calculated, thereby constructing immunome repertoire sequence data reflecting B cell clonal composition, clonal expansion levels, and repertoire diversity indicators. This process forms the basis for identifying "disease-related clones" and conducting subsequent integrative association analysis (S3).

[0094] Generating immune repertoire sequence data is a sophisticated bioinformatics process. The first step is quality control and filtering: Raw sequence data obtained from high-throughput sequencing platforms (such as Illumina NovaSeq) is processed using specialized software (such as FastQC and Trimmomatic) to remove low-quality reads with sequencing quality values ​​below a preset threshold (such as Q20), and adapter and primer sequences are removed to ensure the accuracy of subsequent analyses. The second step is assembly and alignment: For paired-end sequencing (such as 2x150bp), software such as MiXCR or pRESTO is used to assemble overlapping qualified reads into a complete BCR variable region sequence. Subsequently, the assembled sequence is aligned with a mouse B cell receptor reference gene bank (such as the IMGT database) to identify V, D, and J gene fragments and precisely determine the nucleotide and amino acid sequences of the CDR3 region. The CDR3 sequence is typically a highly variable sequence at the junction of the V and J genes. The third step is clone identification and quantification: Based on the uniqueness of the amino acid sequence of CDR3 and / or the combination of V and J genes, BCR sequences with the same CDR3 sequence are grouped into one B cell clone. The frequency of each clone in the total sequence is calculated, and immune repertoire sequence data is constructed. This data includes a list of clone composition, the frequency distribution of each clone, and the degree of clone amplification (such as clone size) and repertoire diversity indicators (such as Shannon entropy and Simpson index).

[0095] This implementation method ensures the accuracy and comparability of immune repertoire sequence data through a standardized bioinformatics workflow. Rigorous quality control effectively avoids the introduction of sequencing errors and false-positive clones. Clone identification based on CDR3 sequences can accurately distinguish different antigen-specific B cell populations. By calculating clonal frequency and diversity indicators, the degree of clonal expansion of B cells and the remodeling of the immune repertoire under disease conditions can be quantified. In particular, it can identify "disease-associated clones" that significantly expand in the disease model group; these clones are considered molecular markers of pathogenic B cells, providing crucial "identity information" for subsequent integrative association analysis (S3).

[0096] In some embodiments, the preset combination of B cell differentiation and functional markers is configured to simultaneously distinguish B cell subsets at different differentiation stages and functional states, including: markers for all mature B cells, markers for activated B cells, markers for memory B cells, and markers for antibody-secreting cells; the multiparameter flow cytometry analysis includes: simultaneously detecting the combination of markers using specific monoclonal antibodies conjugated with different fluorescent dyes, quantitatively analyzing the proportion of each subset of initial B cells, activated B cells, memory B cells, and plasma cell-like cells in the sample, and obtaining the quantitative proportion data of each functional B cell subset; the combination of markers includes: CD19 as a pan-B cell marker; CD135 (FLT-3) as a mature / activated B cell marker; CD27 as a memory B cell marker; and plasma cell antigen-1 (PCA-1) or CD138 as a marker for antibody-secreting cells (plasma cells).

[0097] B cells undergo a series of differentiations during the immune response, from naïve B cells to activated B cells, memory B cells, and finally antibody-secreting cells (plasma cells). These B cell subsets at different differentiation stages have different pathogenic functions in autoimmune diseases. For example, plasma cells are the main source of autoantibodies, and an increase in their proportion directly reflects disease activity. This implementation aims to achieve precise quantitative analysis of these functional subsets using multiparameter flow cytometry with a carefully selected combination of biomarkers, thereby obtaining cell subset data. The basis of multiparameter flow cytometry technology lies in the simultaneous labeling of multiple cell surface or intracellular molecules with specific antibodies conjugated to different fluorescent dyes, and the use of laser excitation and detection of different fluorescence signals to achieve high-speed, multidimensional analysis of single cells, thereby accurately distinguishing B cell subsets with different biomarker expression patterns.

[0098] In the S2 multidimensional parallel data analysis, cell subpopulation data were acquired based on a pre-defined combination of B cell differentiation and functional markers. This marker combination included: CD19, as a pan-B cell marker, used to initially delineate the B cell population; CD135 (FLT-3), used to label mature or activated B cells; CD27, as a classic marker of memory B cells; and PCA-1 or CD138, as markers of antibody-secreting cells (plasma cells). In multiparameter flow cytometry analysis, B cell samples were mixed and incubated with specific monoclonal antibodies pre-conjugated with different fluorescent dyes (such as FITC, PE, APC, PerCP, etc.). Subsequently, the stained cell suspension was loaded onto a multiparameter flow cytometer (such as BD LSRFortessa or CytoFLEX), and multiple fluorescence signals for each cell were simultaneously detected by setting different filters and voltages. Gated analysis was performed using flow cytometry software (such as FlowJo): First, the B cell population was delineated based on CD19+ expression. Then, by combining the expression patterns of other markers (CD135, CD27, PCA-1 / CD138), the B cell population was further subdivided into functional subpopulations such as naive B cells, activated B cells, memory B cells, and plasma cell-like cells. Finally, the proportion of each subpopulation in the total B cells was quantitatively calculated, yielding quantitative proportion data for each functional B cell subpopulation.

[0099] This implementation method achieves precise differentiation and quantification of core B-cell subsets in pSS pathology through a carefully selected combination of biomarkers. In particular, the precise quantification of the plasma cell subset (PCA-1+) provides crucial "functional status information" for subsequent integrative correlation analysis (S3). The high sensitivity and high throughput of multiparameter flow cytometry ensures accurate acquisition of proportion data for each subset even with limited sample sizes. This detailed segmentation of B-cell functional subsets helps reveal the dynamic changes in the B-cell differentiation profile under disease progression or drug intervention, thereby providing a better understanding of disease mechanisms and drug targets.

[0100] In some embodiments, the key signaling pathway related to B cell activation and survival is the BAFF-NF-κB signaling pathway; the acquisition of the signaling pathway data includes: detecting the molecular expression or activity levels of key nodes in the pathway using one or more techniques selected from Western blotting, enzyme-linked immunosorbent assay (ELISA), and quantitative reverse transcription polymerase chain reaction (qRT-PCR); the key nodes include: the upstream ligand BAFF, its receptor BAFFR, and the core transcription factor subunits p65 and p50 of the downstream NF-κB pathway, the regulatory protein IκBα, and the pathway-related effector cytokine IL-6.

[0101] B cell activating factor (BAFF) and its mediated NF-κB signaling pathway are core signaling pathways regulating B cell survival, maturation, and differentiation, and are closely related to the pathogenesis of primary Sjögren's syndrome (pSS). Serum BAFF levels are typically elevated in pSS patients, activating BAFFR on B cells, which in turn activates the downstream NF-κB signaling pathway, promoting abnormal B cell activation and plasma cell differentiation. This implementation aims to quantitatively detect key nodes in the BAFF-NF-κB signaling pathway using a multi-technology platform (Western Blot, ELISA, qRT-PCR) to obtain signaling pathway data. This combination of technologies allows for a comprehensive assessment of the pathway status at three levels: mRNA, protein expression, and protein activity. For example, ELISA is used to detect the concentrations of the soluble ligand BAFF and the effector factor IL-6; Western Blot is used to detect the phosphorylation activity of the receptor BAFFR and the core transcription factor p65; and qRT-PCR is used to detect the gene expression levels of key molecules.

[0102] In the S2 multidimensional parallel data analysis, the acquisition of signaling pathway data focused on the BAFF-NF-κB signaling pathway. Key nodes included: the upstream ligand BAFF, used to assess the intensity of activation signals in the B cell microenvironment; the receptor BAFFR, used to assess the B cell's responsiveness to BAFF signals; the core transcription factor subunits p65 and p50, which are key molecules that enter the nucleus to regulate gene expression after NF-κB pathway activation; the regulatory protein IκBα, responsible for anchoring NF-κB to the cytoplasm in the inactive state, and its degradation or phosphorylation is a marker of pathway activation; and the effector cytokine IL-6, an important mediator of plasma cell differentiation and inflammatory responses. Detection techniques included: an ELISA kit for quantitatively detecting the concentrations of soluble BAFF and IL-6 proteins in serum or tissue homogenate supernatant; and Western blotting for detecting the protein expression level of BAFFR and the phosphorylation level of p65 (p-p65) in tissue lysates, with the phosphorylation ratio being a key indicator for assessing the activation status of the NF-κB pathway. qRT-PCR is used to detect the mRNA expression levels of genes such as BAFF, BAFFR, and NF-κB1 (p50), reflecting the regulation of gene transcription. Through these multi-level molecular detections, the activity status of the BAFF-NF-κB signaling pathway can be comprehensively and accurately quantified.

[0103] This implementation focuses on the BAFF-NF-κB pathway as a core pathogenic pathway and employs multiple technology platforms for detection, ensuring the comprehensiveness and accuracy of signaling pathway data. Simultaneous detection of upstream ligands, receptors, core transcription factors, and their activities (phosphorylation) provides a complete picture of the pathway's activation status. For example, quantifying p65 phosphorylation levels is the gold standard for assessing NF-κB pathway activation. This precise molecular-level quantification provides crucial "environmental signaling information" for subsequent S3 integration and association analysis, enabling a direct quantitative correlation between B cell clonal expansion and specific molecular pathway activation, thereby deepening our understanding of the pathogenic mechanism.

[0104] In some embodiments, the detection of molecular expression or activity levels of key nodes in the detection pathway includes: detecting the concentration of soluble BAFF and IL-6 in mouse serum or tissue homogenate supernatant using an ELISA kit; detecting the protein expression level of BAFFR and the phosphorylation level (p-p65) of NF-κB p65 protein in spleen or salivary gland tissue lysate using Western Blot technology, and calculating its phosphorylation ratio using total p65 protein as an internal reference to assess the activation status of the NF-κB pathway; and detecting the mRNA expression levels of BAFF, BAFFR, and NF-κB1 (p50) in the predetermined tissue using qRT-PCR technology.

[0105] This implementation further refines the specific detection methods and quantitative indicators for signaling pathway data, aiming to ensure the standardization and accuracy of molecular detection. ELISA technology has high sensitivity and high throughput, suitable for detecting soluble proteins such as BAFF and IL-6 in serum or tissue homogenate supernatant. Western blotting is a classic method for detecting protein expression levels and phosphorylation status. By detecting the phosphorylation level of p65 (p-p65) and calculating the ratio with total p65 protein, the activation status of the NF-κB pathway can be accurately assessed, which is the gold standard for pathway activity assessment. qRT-PCR technology is used to detect gene transcription levels, reflecting the upstream regulatory mechanisms of molecules such as BAFF, BAFFR, and NF-κB1 (p50). By combining these three technologies, comprehensive quantification from secreted protein concentration and receptor expression to core transcription factor activity is achieved.

[0106] In the S2 multidimensional parallel analysis, signaling pathway data were acquired using the following three parallel techniques: ELISA: Commercially available mouse BAFF and IL-6 ELISA kits were used to detect the levels of BAFF in collected mouse serum or salivary gland / spleen tissue homogenate supernatants. Serum BAFF concentration directly reflects the level of BAFF in systemic circulation, while the BAFF / IL-6 concentration in tissue homogenate supernatants reflects the intensity of inflammation and B cell activation signals in the local microenvironment. Western Blot: Spleen or salivary gland tissue was treated with protein lysis buffer (such as RIPA lysis buffer, containing protease and phosphatase inhibitors) to extract total protein. Proteins were separated by SDS-PAGE gel electrophoresis and transferred to PVDF membranes. Immunoblotting was performed using specific antibodies (such as anti-BAFFR, anti-p-p65, and anti-total p65 antibodies). Signal intensity was detected using a chemiluminescence or fluorescence imaging system. The key is to calculate the ratio of phosphorylation level of NF-κB p65 protein (p-p65) to total p65 protein (Total p65) (p-p65 / Totalp65), which serves as a quantitative indicator for assessing the activation status of the NF-κB pathway. qRT-PCR: Total RNA is extracted from pre-selected tissues (e.g., salivary glands and spleen) and reverse transcribed into cDNA. Specific primers and fluorescent dyes (e.g., SYBR Green) are used to quantitatively amplify the mRNAs of BAFF, BAFFR, and NF-κB1 (p50). Normalization is performed using internal reference genes (e.g., GAPDH or β-actin), and the relative expression levels of each gene are calculated. These three techniques together constitute the quantitative system for signaling pathway data, ensuring the reliability of the third key variable used in the subsequent association analysis S3.

[0107] This implementation method ensures the accuracy and operability of signaling pathway data through clearly defined technical indicators and detection methods. Using the p-p65 / Total p65 ratio as an indicator of NF-κB pathway activation status avoids biases arising from relying solely on total protein quantity or single phosphorylation levels. ELISA detection of soluble factors provides quantitative information on microenvironmental signals. qRT-PCR provides evidence of transcriptional levels. This multi-technology, multi-level detection strategy makes the assessment of the BAFF-NF-κB pathway more comprehensive and accurate, providing high-quality molecular pathway data for subsequent S3 integration and association analysis.

[0108] In some embodiments, step S3, the correlation analysis includes: extracting the frequency of "disease-related clones" from the immune repertoire data as a first key variable; extracting the proportion of plasma cell subsets (PCA-1+) from the cell subset data as a second key variable; extracting serum BAFF concentration or tissue p-p65 / p65 ratio from the signaling pathway data as a third key variable; and using statistical correlation analysis techniques to calculate the correlation coefficients between the first key variable and the second and third key variables, respectively, to establish a quantitative correlation network among "specific clonal amplification—plasma cell differentiation—BAFF-NF-κB pathway activation" at the data level.

[0109] The main limitation of existing technologies lies in data fragmentation, which prevents the systematic correlation between the specific clonal expansion of B cells (immune repertoire) and their functional state (cell subsets) and the underlying molecular mechanisms (signaling pathways). The core innovation of this implementation lies in S3 integrative correlation analysis. By precisely extracting key variables from three dimensions (clonal frequency, plasma cell ratio, and pathway activity), and employing statistical correlation analysis, a quantitative correlation network among the three is established. This technology is based on the fact that in the pathogenesis of pSS, specific clones (the first variable) expand under antigen-driven conditions and further differentiate into plasma cells (the second variable), a process regulated by the activation of the BAFF-NF-κB pathway (the third variable). By calculating the correlation coefficients between variables, the strength and direction of this biological correlation can be quantified, thereby achieving a systematic and comprehensive analysis of the pathogenic mechanism.

[0110] In the S3 integrative association analysis, key variables were first extracted: The first key variable was identified from the immune repertoire data by comparing the disease model group with the normal control group. "Disease-related clones" that significantly amplified in the disease state (e.g., frequency > 0.5%) but were rare or absent in the normal group were identified, and the total frequency of these clones was calculated. This frequency reflects the strength of the antigen-driven specific immune response. The second key variable was extracted from the cell subset data, representing the percentage of plasma cell subsets (PCA-1+ or CD138+) in total B cells. The plasma cell proportion reflects the terminal differentiation state and antibody secretion capacity of B cells. The third key variable was extracted from the signaling pathway data, representing the concentration of soluble BAFF protein in serum or the p-p65 / Total p65 ratio in tissue lysates. This variable reflects the activation level of the BAFF-NF-κB pathway. Subsequently, statistical correlation analysis techniques (such as Pearson or Spearman correlation analysis) were used to calculate the correlation coefficients (r-values) and p-values ​​between the first key variable and the second and third key variables, respectively. For example, the correlation between the frequency of "disease-associated clones" and the proportion of plasma cells, as well as the correlation between the frequency of these clones and serum BAFF concentration, can be calculated. At the data level, if there is a significant positive correlation among the three, a quantitative correlation network of "specific clonal expansion—plasma cell differentiation—BAFF-NF-κB pathway activation" can be established to analyze the B cell immune response mechanism of pSS at multiple levels.

[0111] This implementation overcomes the data fragmentation inherent in traditional studies by constructing a quantitative correlation network, achieving a systematic understanding of the pathogenesis of pSS. By calculating correlation coefficients, the strength of the biological links between specific clonal expansion, plasma cell differentiation, and pathway activation can be quantified, providing direct evidence for the origin, differentiation, and molecular pathway regulation mechanisms of pathogenic B cells. This correlation analysis capability enables this method to precisely pinpoint the core driving factors of the disease, providing strong data support for the discovery of new diagnostic biomarkers and therapeutic targets.

[0112] In some embodiments, the method further includes: comparing the first key variable, the second key variable, and the third key variable of the drug treatment group with the corresponding variables of the disease model group; if the values ​​of all variables in the drug treatment group show a statistically significant decrease, and the degree of decrease is consistent with the trend of the positive drug control group, then it is determined that the drug exerts a therapeutic effect on Sjögren's syndrome by inhibiting the BAFF-NF-κB signaling pathway, thereby depleting pathogenic B cell clones and plasma cells.

[0113] This embodiment is a specific application of the method of the present invention in the evaluation of drug action mechanisms, aiming to systematically evaluate the efficacy and molecular mechanism of action of a test drug (such as ellamod). Through experimental group design, this embodiment uses three core variables (specific clone frequency, plasma cell ratio, and pathway activity) obtained from S3 integrative correlation analysis as quantitative indicators of drug efficacy. If the test drug can effectively treat pSS, it should be able to inhibit the disease-related immune response, manifested as inhibited expansion of pathogenic clones (decreased first variable), reduced plasma cell differentiation (decreased second variable), and reduced activity of the upstream driving signaling pathway (BAFF-NF-κB) (decreased third variable). By comparing the test drug treatment group with the disease model group and the positive drug control group, the drug's target and mechanism of action can be systematically determined.

[0114] The core of this implementation method is to compare and determine the mechanisms of multidimensional variables after drug intervention. The comparative analysis includes: First key variable comparison: comparing the frequency of "disease-related clones" in the drug treatment group with the frequency in the disease model group. If the drug is effective, this frequency should decrease significantly, indicating that the drug inhibits the expansion of specific pathogenic B cell clones. Second key variable comparison: comparing the proportion of plasma cell subsets in the drug treatment group with the proportion in the disease model group. If the drug is effective, the proportion of plasma cells should decrease significantly, indicating that the drug inhibits the terminal differentiation of B cells into plasma cells. Third key variable comparison: comparing the serum BAFF concentration or tissue p-p65 / p65 ratio in the drug treatment group with the corresponding values ​​in the disease model group. If the drug exerts its effect through the BAFF-NF-κB pathway, the activity index of this pathway should decrease significantly. Statistically significant decreases are usually determined using t-tests or ANOVA, with a p-value less than 0.05 considered statistically significant. Mechanism determination: If the values ​​of the three key variables in the drug treatment group all show a statistically significant decrease, and the decreasing trend is consistent with that in the positive drug control group (such as leflunomide), then it can be systematically determined that the drug exerts its therapeutic effect on Sjögren's syndrome by inhibiting the BAFF-NF-κB signaling pathway, thereby depleting pathogenic B cell clones and plasma cells.

[0115] This implementation provides a high-standard, multi-dimensional method for evaluating drug mechanisms of action, significantly improving the efficiency and depth of drug screening and mechanism research. By simultaneously monitoring clonal, phenotypic, and molecular pathway activity, it can clearly reveal the drug's target and pathway of action, avoiding the one-sidedness of mechanism research in traditional methods. For example, if a drug only reduces the proportion of plasma cells (the second variable) but does not significantly affect the frequency of specific clones (the first variable), it may indicate that the drug acts on plasma cell survival rather than clonal expansion. Through the linkage analysis of three variables, this method can systematically elucidate the mechanism by which drugs (such as ellamod) treat pSS by regulating specific signaling pathways and affecting specific B cell clones, providing a scientific basis for the clinical translation and precision medicine of drugs.

Claims

1. A method for sequencing and subset analysis of B-cell immune repertoire in a mouse model of Sjögren's syndrome, characterized in that, include: S1. Sample preparation: Prepare single-cell suspensions from the target autoimmune disease animal model and the control animals, and perform B cell enrichment treatment on the single-cell suspensions to obtain B cell samples. S2. Parallel Analysis of Multidimensional Data: Analyze the B cell samples and construct multidimensional data in parallel: Immune repertoire data: High-throughput sequencing of the B cell samples was performed on the B cell receptor (BCR) to generate immune repertoire sequence data reflecting the composition and diversity of B cell clones; Cell subpopulation data: Based on a preset combination of B cell differentiation and functional markers, multi-parameter flow cytometry analysis was performed on the single cell suspension or B cell sample to obtain quantitative proportion data of each B cell functional subpopulation. Signaling pathway data: Detection of expression or activity levels of key signaling pathway molecules related to B cell activation and survival in the preset tissue; S3. Integration and Correlation Analysis: The immune repertoire data, cell subpopulation data and signaling pathway data obtained in step S2 are subjected to correlation analysis to establish the correlation between specific B cell clonal characteristics, specific B cell subpopulation changes and specific signaling pathway activities, and to analyze and generate the B cell immune response mechanism of the autoimmune disease at multiple levels. The target autoimmune disease animal model is a mouse model of primary Sjögren's syndrome; The pre-defined tissues include salivary gland tissue as the primary target organ and spleen tissue as a secondary lymphatic organ. The B cell enrichment process includes: specifically separating a population of B lymphocytes from the single-cell suspension based on pan-B cell surface markers using immunomagnetic sorting or flow cytometry, and using the B lymphocyte population as the B cell sample. The key signaling pathway related to B cell activation and survival is the BAFF-NF-κB signaling pathway. The acquisition of the signaling pathway data includes: detecting the molecular expression or activity levels of key nodes in the pathway using one or more of the following techniques: Western blotting, enzyme-linked immunosorbent assay (ELISA), and quantitative reverse transcription polymerase chain reaction (qRT-PCR). The key nodes include: the upstream ligand BAFF, its receptor BAFFR, and the core transcription factor subunits p65 and p50 of the downstream NF-κB pathway, the regulatory protein IκBα, and the pathway-related effector cytokine IL-6. The molecular expression or activity levels of key nodes in the detection pathway include: The concentrations of soluble BAFF and IL-6 in mouse serum or tissue homogenate supernatant were detected using an ELISA kit. Western blotting was used to detect the protein expression level of BAFFR and the phosphorylation level of NF-κB p65 protein (p-p65) in lysates of spleen or salivary gland tissue. The total p65 protein was used as an internal reference to calculate its phosphorylation ratio to assess the activation status of the NF-κB pathway. The mRNA expression levels of BAFF, BAFFR, and NF-κB1 p50 proteins in the pre-selected tissue were detected using qRT-PCR technology. The correlation analysis includes: The frequency of "disease-related clones" was extracted from the immune repertoire data as the first key variable; PCA-1 was extracted from the cell subpopulation data. + The proportion of plasma cell subsets was considered the second key variable. Serum BAFF concentration or tissue p-p65 / p65 ratio was extracted from the signaling pathway data as a third key variable; Using statistical correlation analysis, the correlation coefficients between the first key variable and the second and third key variables were calculated, and a quantitative correlation network among the three factors of "specific clonal expansion - plasma cell differentiation - BAFF-NF-κB pathway activation" was established at the data level.

2. The method according to claim 1, characterized in that, The primary Sjögren's syndrome (pSS) model mice were non-obese diabetic mice. The control animals were C57BL / 6 strain mice of the same age and sex; The method further includes, before step S1, randomly dividing the non-obese diabetic mice into at least three groups: a disease model group, a positive drug control group, and a drug treatment group, and subjecting the drug treatment group and the positive drug control group to drug intervention for a predetermined period of time.

3. The method according to claim 2, characterized in that, The preparation of the single-cell suspension and the enrichment of the single-cell suspension with B cells include: The salivary gland tissue and spleen tissue were mechanically ground and filtered to obtain an initial cell suspension. The initial cell suspension was treated with erythrocyte lysis buffer to remove erythrocytes; The initial cell suspension after removing red blood cells was centrifuged using density gradient centrifugation to remove the lymphocyte population, resulting in the single-cell suspension for sorting. The enrichment based on pan-B cell surface markers specifically involves positively sorting the single-cell suspension using immunomagnetic beads conjugated with anti-CD19 monoclonal antibodies to obtain high-purity CD19. + B cell samples.

4. The method according to claim 2, characterized in that, The generation of the immune repertoire sequence data includes: The raw sequence data obtained from high-throughput sequencing is subjected to quality control and filtering to remove low-quality reads and adapter sequences, resulting in a number of qualified reads. The qualified reads are spliced ​​together and compared with the B cell receptor reference gene library to identify gene fragments in the variable region, diversity region, and linker region, as well as the nucleotide and amino acid sequences of complementarity-determining region 3. Based on the uniqueness of the nucleotide and amino acid sequences of the complementarity-determining region 3, different B cell clones are identified, and the frequency distribution of each clone is calculated to construct the immune repertoire sequence data, which includes indicators of clonal composition, clonal amplification degree, and repertoire diversity.

5. The method according to claim 4, characterized in that, The pre-defined combination of B cell differentiation and functional markers is configured to simultaneously distinguish B cell subsets at different differentiation stages and functional states, including: Markers for all mature B cells, markers for activated B cells, markers for memory B cells, and markers for antibody-secreting cells; The multi-parameter flow cytometry analysis includes: simultaneously detecting the combination of biomarkers using specific monoclonal antibodies conjugated with different fluorescent dyes, quantitatively analyzing the proportion of each subpopulation of naive B cells, activated B cells, memory B cells and plasma cell-like cells in the sample, and obtaining the quantitative proportion data of each functional subpopulation of B cells. The combination of biomarkers includes: CD19 as a pan-B cell biomarker; CD135 (FLT-3) as a mature / activated B cell biomarker; CD27 as a memory B cell biomarker; and PCA-1 plasma cell antigen or CD138 as a biomarker for antibody-secreting cells (plasma cells).

6. The method according to claim 5, characterized in that, The method further includes: The first key variable, the second key variable, and the third key variable of the drug treatment group to be tested are compared with the corresponding variables of the disease model group, respectively. If all variable values ​​in the drug treatment group show a statistically significant decrease, and the degree of decrease is consistent with the trend of the positive drug control group, then the drug is determined to exert a therapeutic effect on Sjögren's syndrome by inhibiting the BAFF-NF-κB signaling pathway, thereby depleting pathogenic B cell clones and plasma cells.

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