Biology subjects

Nititham, J.

Publications and source records attributed to Nititham, J..

3 recordsLinked to original sources

bioRxiv2025

Single-cell RNA sequencing of peripheral blood defines two immunological subtypes of Sjögren's disease

Sjogrens disease (SjD) is a heterogeneous autoimmune disorder with significant clinical and molecular diversity. While anti-SSA antibodies serve as a hallmark serological biomarker, nearly half of patients lack them, raising fundamental questions about distinct pathogenic mechanisms. To address this, we performed single-cell RNA sequencing with surface protein profiling on 1.5 million PBMCs from 333 participants in the Sjogrens International Collaborative Clinical Alliance cohort, comparing those with and without SjD and stratifying them by anti-SSA status. Our analysis identified two immunologically distinct subtypes of SjD, with SSA-positive patients exhibiting a dominant and persistent IFN-I signature, profoundly impacting immune cell maturation. Notably, transitional B cells were disproportionately affected, displaying signs of premature maturation, reduced BCR diversity, and shorter CDR3 regions, reinforced interactions with proinflammatory cells, thereby fostering an environment conducive to autoreactivity. By contrast, SSA-negative SjD participants lacked an upregulated IFN-I signature, challenging prevailing pathogenic models and suggesting alternative immune dysregulation pathways. These findings support a two-disease model of SjD and highlight transitional B cells as both a key biomarker and a potential therapeutic target. Targeting IFN-I signaling and transitional B cell maturation may represent a novel therapeutic avenue to modulate immune dysregulation and prevent autoreactivity in SjD.

genomics↗

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2020

Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

bioinformatics↗