Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data.

Jordan Sicherman,Paul Pavlidis,Dwight F Newton,Shreejoy J Tripathy,Etienne Sibille

doi:10.3389/fnmol.2021.637143

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.

Highlights

Traditional RNA-sequencing, which occurs on homogenized bulk tissue samples, has made it possible to have a global view on the entire transcriptome of a great many tissues and species with relative ease and affordability
We applied an approach for characterizing off-target cellular contamination in RNA sequencing (RNA-seq)-based datasets of pooled cell types
By comparing these sctRNAseq samples to analogous high-purity scRNA-seq datasets, we were able to derive a series of contamination coefficients that yield an estimated amount of contamination for each off-target cell type

Summary

Introduction

Traditional RNA-sequencing, which occurs on homogenized bulk tissue samples, has made it possible to have a global view on the entire transcriptome of a great many tissues and species with relative ease and affordability. The technology is mature and its analysis is well-documented, it lacks the ability to capture changes in minor cellular populations For this purpose, newer technologies (single cell RNA-sequencing) are typically used for analysis at the single cell (rather than whole tissue) level (Kim et al, 2015). To balance between the sensitivity of single cell sequencing and the relative affordability of bulk tissue RNA sequencing (RNA-seq), cell type specific transcriptomes are being obtained using a variety of technologies and methods (Kim et al, 2015; Yuan et al, 2017) These methods typically involve the enrichment of a specific cell type of interest by morphological or fluorescencebased approaches, followed by the sequencing of a pool of these cells, usually resulting in one pooled cell type group per sample. They are collectively termed single cell type RNAseq (sctRNA-seq)

Methods

Results

Conclusion