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
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.