AUTHOR=Sicherman Jordan , Newton Dwight F. , Pavlidis Paul , Sibille Etienne , Tripathy Shreejoy J. TITLE=Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data JOURNAL=Frontiers in Molecular Neuroscience VOLUME=14 YEAR=2021 URL=https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2021.637143 DOI=10.3389/fnmol.2021.637143 ISSN=1662-5099 ABSTRACT=

Transcriptionally profiling minor cellular populations remains an ongoing challenge in molecular genomics. Single-cell RNA sequencing has provided valuable insights into a number of hypotheses, but practical and analytical challenges have limited its widespread adoption. A similar approach, which we term single-cell type RNA sequencing (sctRNA-seq), involves the enrichment and sequencing of a pool of cells, yielding cell type-level resolution transcriptomes. While this approach offers benefits in terms of mRNA sampling from targeted cell types, it is potentially affected by off-target contamination from surrounding cell types. Here, we leveraged single-cell sequencing datasets to apply a computational approach for estimating and controlling the amount of off-target cell type contamination in sctRNA-seq datasets. In datasets obtained using a number of technologies for cell purification, we found that most sctRNA-seq datasets tended to show some amount of off-target mRNA contamination from surrounding cells. However, using covariates for cellular contamination in downstream differential expression analyses increased the quality of our models for differential expression analysis in case/control comparisons and typically resulted in the discovery of more differentially expressed genes. In general, our method provides a flexible approach for detecting and controlling off-target cell type contamination in sctRNA-seq datasets.