Title: CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq,
Abstract: 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 RNA extracted from
FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia
patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at
ultralow input levels are known to be noisy. When using CLEAR transcripts vs.
using all available transcripts, downstream analyses reveal more shared DEGs,
improved Principal Component Analysis separation of cell type, and increased
similarity between results across different input RNA amounts. CLEAR was
applied to two publicly available ultralow input RNA-seq data and an in-house
murine neural cell lcRNA-seq dataset. CLEAR provides a novel way to visualize
the public datasets while validates cell phenotype markers for astrocytes,
neural stem and progenitor cells.