An integrated multiplexed genomic assay for low input clinical samples1

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 DESCRIPTION (provided by applicant): The heterogeneity of the human immune system is essential for protecting us against myriad pathogens, but also poses measurement challenges, since we typically must either deeply profile samples as heterogeneous mixtures or measure only a few pre-selected variables in single cells. To understand the composition of limiting clinical immune isolates and their relation to disease, we must combine the breadth of genomic profiling with the resolution of single cell assays. Emerging single-cell profiling methods, such a single-cell RNA-seq (scRNA-Seq), provide an extraordinary opportunity to overcome these challenges. In recent proof-of-concept studies, we applied scRNA-Seq to murine immune and human tumor samples: we developed experimental and computational approaches for generating and analyzing scRNA-Seq data, and used them to examine >50,000 cells from diverse systems and samples. However, to realize the promise of these strategies for translational immunology, we must create, optimize and implement a scRNA-Seq platform that can be deployed for diverse clinical samples from core biopsies to FNAs to fluids. Here, we will optimize current protocols to create standard operating procedures (SOPs) and merge individual components into a standardized pipeline that can be provided to the immunology community. We will wrap and release SOPs for scRNA-Seq and realize protocols for linking single cell genomic data to more conventional immunology measurement, such as FACS or CyTOF (Aim 1). From our driving clinical collaborators (Kwon, Tsokos, Walker, Xavier), we will receive diverse clinical specimens (including biopsies, PBMCs, synovial) in IBD, HIV, TB, and SLE. We will develop and deploy experimental protocols to best extract disaggregated individual cells from each sample type for successful scRNA-Seq (Aim 2). We will optimize our existing computational pipelines for QC, analysis and visualization, wrap them into a streamlined package, and release it as a publicly available tool (Aim 3). Our project will create a robust workflow for single-cell transcriptome analysis applicable across diverse clinical sample types, and accessible to the entire immunology community.