The adoption of single-cell technologies has highlighted important cell-to-cell heterogeneity in a wide range of research areas and has led to the realization that to truly understand human biology and disease we need to understand how the behavior of individual cells shapes biological processes.
Single-cell RNA-Seq (scRNA-Seq) has emerged as a powerful tool in this effort as it allows whole genome gene expression profiling of hundreds to thousands of individual cells in a single experiment. scRNA-Seq has enabled a wide range of discoveries including identification of new cell types linked to diabetes and Alzheimer’s disease (Keren-Shaul et al. 2017, Segerstolpe et al. 2016), investigation of developmental processes such as stem cell differentiation (Han X et al. 2018) and human embryo development (Petropoulos et al. 2016), and tracking of heterogeneity in cell response to drug treatment (Levitin et al. 2018) or during disease progression (Potter 2018). Key to these important discoveries was the ability to study cells in their true biological context (that is, living tissue) rather than studying these processes in immortalized cell lines.
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Here we demonstrate that the resolution of single-cell RNA sequencing (RNA-Seq) can be greatly enhanced by adding a cell sorting step prior to RNA-Seq analysis. By pairing the S3e Cell Sorter with the Illumina® Bio-Rad Single-Cell Sequencing Solution, we were able isolate an extremely rare peripheral blood mononuclearcell population and characterize unexpected heterogeneity within this population.”