A contamination focused approach for optimizing the single-cell RNA-seq experiment

Deronisha Arceneaux; Zhengyi Chen; Alan J Simmons; Cody N Heiser; Austin N Southard-Smith; Michael J Brenan; Yilin Yang; Bob Chen; Yanwen Xu; Eunyoung Choi; Joshua D Campbell; Qi Liu; Ken S Lau

doi:10.1016/j.isci.2023.107242

A contamination focused approach for optimizing the single-cell RNA-seq experiment

iScience. 2023 Jun 29;26(7):107242. doi: 10.1016/j.isci.2023.107242. eCollection 2023 Jul 21.

Authors

Deronisha Arceneaux^{1

2}, Zhengyi Chen^{1

3}, Alan J Simmons^{1

2}, Cody N Heiser^{1

3}, Austin N Southard-Smith⁴, Michael J Brenan⁵, Yilin Yang^{1

2}, Bob Chen^{1

3}, Yanwen Xu^{1

2}, Eunyoung Choi^{1

2

6}, Joshua D Campbell⁷, Qi Liu⁸, Ken S Lau^{1

2

3

6}

Affiliations

¹ Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
² Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
³ Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁴ McDonnell Genome Institute and Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
⁵ 1CellBio, Inc., Watertown, MA, USA.
⁶ Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷ Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
⁸ Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.

Abstract

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

Keywords: Biology experimental methods; Computational bioinformatics; Transcriptomics.

Abstract

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