Abstract Text: Single-cell profiling sheds light on heterogeneity and dynamics of individual cells that conventional bulk-population measurements cannot reveal. In this pilot study, our goal is to establish a pipeline using simultaneous single-cell profiling of gene expression, surface protein, and chromatin accessibility to generate data accurately and rapidly from clinical trial samples. We analyzed peripheral blood mononuclear cells collected prior to treatment from four participants in each of two Immune Tolerance Network (ITN) clinical trials: recently diagnosed Type 1 Diabetes patients from the AbATE clinical trial and multiple sclerosis patients from the HALT-MS clinical trial. We collected an equal number of sorted T cells (CD3+CD19-CD14-CD56-) and B cells (CD3-CD19+CD14-CD56-) from each subject and pooled together for droplet-based single-cell assays and downstream analyses. The multiplexed samples were sequenced through single-cell multiome ATAC+Gene Expression and CITE-seq (RNA/163 Proteins)+TCR/BCR repertoire. We performed demultiplexing and quality-control on the pooled samples based on dense genotyping of the subjects and obtained, on average, 918 cells per sample for the former platform and 4056 cells per sample for the latter platform. We performed canonical correlation analysis on the multimodal CITE-seq data and produced refined clusters using RNA and 45 informative protein markers simultaneously to identify global B and T cell populations, and subsets including naïve and memory T cells. The clusters were further merged with multiome data for linkage to epigenetic states. This work highlights the quality and potential utility of simultaneous multiomic single-cell profiling of T and B cell states and tracking changes over time during immune-modulating therapies.
