Single-Cell RNA-seq Transcriptomic Profiling of Immune Cells in Rheumatoid Arthritis
Poster Number
65
Faculty Mentor Name
Dr. Julia Olivieri, jolivieri@pacific.edu
Research or Creativity Area
Engineering & Computer Science
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects approximately 1% of the global population, leading to joint inflammation, pain, and progressive disability. In this study, we applied a computational pipeline to analyze a publicly available scRNA-seq dataset of peripheral blood mononuclear cells (PBMCs) from RA patients and healthy controls (Binvignat et al., 2024). Our goal was to identify transcriptional signatures associated with disease activity and explore the potential of gene expression features to distinguish between disease states.
Purpose
We aim to identify differentially expressed genes and co-expressed gene pairs across immune cell populations, compare correlation patterns between disease states, and explore the feasibility of predicting RA disease status based on gene expression profiles. This analysis provides a framework for discovering transcriptional signatures that may inform future biomarker development and deepen our understanding of immune dysregulation in RA.
Results
Differential expression analysis revealed upregulation of pro-inflammatory genes such as EGR1 and TNF in RA samples. Correlation analysis uncovered gene pairs with significantly different co-expression patterns between disease states. Principal component analysis (PCA) of single-cell gene expression data revealed clear separation of major immune cell types.
Significance
By analyzing gene expression at the single-cell level, our findings contribute to a deeper understanding of RA pathogenesis and support the development of transcriptome-based disease classifiers.
Location
University of the Pacific, DeRosa University Center
Start Date
26-4-2025 10:00 AM
End Date
26-4-2025 1:00 PM
Single-Cell RNA-seq Transcriptomic Profiling of Immune Cells in Rheumatoid Arthritis
University of the Pacific, DeRosa University Center
Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects approximately 1% of the global population, leading to joint inflammation, pain, and progressive disability. In this study, we applied a computational pipeline to analyze a publicly available scRNA-seq dataset of peripheral blood mononuclear cells (PBMCs) from RA patients and healthy controls (Binvignat et al., 2024). Our goal was to identify transcriptional signatures associated with disease activity and explore the potential of gene expression features to distinguish between disease states.
