SINGLE-CELL RNA SEQUENCING UNVEILS PROGRESSIVE IMMUNE DYSREGULATION FROM GENERAL POPULATION, PRECLINICAL SLE TO SLE PATIENTS

PT018 / #183 Topic: AS12 - Genetics, Epigenetics, Transcriptomics POSTER TOUR 05: SLE PATHOGENESIS 24-05-2025 10:00 AM - 10:20 AM Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex immunological disturbances. Early detection is challenging due to heterogeneous clinical manifestations. Understanding cellular and molecular changes from preclinical (pre-SLE) to clinical stages is essential for early intervention. Polygenic risk score (PRS) has been widely used to identify subjects at risk. However, the immune dysregulation of subjects with high SLE-PRS has never been demonstrated. This study aims to delineate the cellular transcriptomic landscapes of healthy controls, pre-SLE, and SLE patients using single-cell RNA sequencing (scRNA-seq) to identify molecular signatures associated with disease progression. Methods Peripheral blood mononuclear cells (PBMCs) were collected from 10 healthy controls, 23 pre-SLE patients with top 5% SLE-PRS without previous diagnosis of SLE, and 12 SLE patients. scRNA-seq was performed using the BD Rhapsody. Data was processed and analyzed with Seurat and other bioinformatics tools to identify differentially expressed genes and pathway enrichments across cell types and patient groups. Results The analysis revealed distinct transcriptional profiles among the 3 groups. PBMCs (peripheral blood mononuclear cells) were clustered and annotated into 5 major cell types: B cells, CD4+ T cells, CD8+ T cells, monocytes, and NK cells, as shown by UMAP (Uniform Manifold Approximation and Projection). Additionally, the cells were further categorized into myeloid and lymphoid lineages. The myeloid-to-lymphoid (M/L) ratio progressively increased in the healthy controls to pre-SLE and SLE patients, indicating an elevated myeloid cell presence as the disease progresses (Figure 1). To identify key immune cell types within the lymphoid subsets, further clustering and analysis of immune cell were performed to resolve immune subpopulations (Figure 2). Differential gene expression between pre-SLE patients and healthy controls was visualized using volcano plots across key immune cell populations (Figure 3). Notably, pre-SLE patients exhibited significant upregulation of genes associated with early immune activation and dysregulation, such as IFI44L and IGKC, suggesting that these genes may act as potential molecular drivers in the pathogenesis of SLE. Figure 1. UMAP clustering of immune cells from healthy controls, pre-SLE, and SLE patients with distinct color-coded cell types. The accompanying table and scatter plot demonstrate an elevated myeloid-to-lymphoid ratio in pre-SLE and SLE, highlighting immune composition shifts. Figure 2. UMAP with subcluster analysis of immune cell types, displaying specific populations such as memory B cells and T cell subsets. Figure 3. Volcano plots show differentially expressed genes in various immune cell populations between pre-SLE and healthy controls. Conclusions Our findings demonstrate progressive immune dysregulation at the single-cell level from pre-SLE to SLE patients. The identified molecular signatures, altered cell subsets, and immune composition shifts provide insights into SLE pathogenesis and suggest potential biomarkers for early diagnosis and therapeutic targets.