Direct cDNA preamplification protocols developed for single-cell RNA-seq (scRNA-seq) have enabled transcriptome profiling of rare cells without having to pool multiple samples or to perform RNA extraction. We term this approach limiting-cell RNA-seq (lcRNA-seq). Unlike scRNA-seq, which focuses on cell-atlasing, lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups. This requires accounting for systems noise which can obscure biological differences. We present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for between-group comparisons. To develop CLEAR, we compared DEGs from FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patient at different input RNA levels. When using CLEAR transcripts vs. using all available transcripts, downstream analyses reveal more consistent DEGs, improved Principal Component Analysis separation by cell type, and consistency across input RNA amounts. CLEAR also performs well on external sc- and lcRNA-seq data and an internal murine neural cell lcRNA-seq data set. SOURCE: Pearlly Yan (Pearlly.Yan@osumc.edu) -  The Ohio State University

Pluto Bioinformatics

GSE115033: CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq (mouse)