Adriana Payan-Medina, a senior in chemical engineering at the University of Utah, used single-cell RNA sequencing analysis to investigate gene expression associations of autoimmune disease.
Autoimmune diseases, where the immune system’s response to self-antigens induces tissue damage or dysfunction, impact 23.5 million Americans. My nine-week chapter as a researcher at the Broad Institute of MIT and Harvard has been absolutely transformative. I’ve spent my days immersed in research with a cohort of passionate biomedical scientists who have helped me recognize the value of discovering my identity as I progress through my career. Collaborations with my empowering mentors and peers have enabled me to appreciate the complexity and variability of research, and have made me realize that the best rewards come from the conquest to understand medical conundrums. Systemic Lupus Erythematosus (SLE) and Rheumatoid Arthritis (RA) are two highly prevalent autoimmune diseases that cause widespread inflammation in affected tissues or joints, respectively. Understanding the fundamental mechanisms modulating autoimmune disease is imperative to optimize treatment and prevention strategies. Though these autoimmune diseases cause adverse health morbidity, direct disease origins are unknown. However, several studies have hypothesized that potential mechanisms of autoimmune disease could include genetic associations. We aim to compare the transcriptomic profiles of SLE and RA donor cells to elucidate shared transcriptional modules of autoimmune disease. Specifically, we used single-cell RNA sequencing (scRNA-seq) data from SLE and RA donor cases and controls to compare differential gene expression (DGE) across each autoimmune disease. With SLE donors’ peripheral blood mononuclear cells and RA donors’ synovial tissue cells, scRNA-seq data from each autoimmune disease were clustered to myeloid and lymphoid cell types. Pseudobulk profiles on each cell type were derived to leverage established bulk RNA sequencing DGE analysis methods. DGE analysis on ancestry-stratified pseudobulk profiles from the SLE data identified upregulated genes between diseased and healthy cell types. Future DGE analysis of the RA pseudobulk profiles would enable a comparison of differentially expressed genes between SLE and RA. Through this DGE analysis across SLE and RA autoimmune diseases, we anticipate that we will gain more insights into shared transcriptomic factors that may affect autoimmune disease incidence. Characterization of the underlying transcriptomic mechanisms in autoimmune disease guides the direction for the development of a more personalized approach to disease screening and treatment strategies.
Project: Identification of transcriptional modules underlying autoimmune disease
Mentor: Ayshwarya Subramanian, Kuchroo Lab