The effects of tissue processing and microfluidic factors on single-cell RNAsequencing data quality in colorectal tissues.

Abstract Our HTAN (Human Tumor Atlas Network) study presented in Chen et al. documented single-cell RNA-sequencing (scRNA-seq) data on 128 human colonic specimens for mapping out distinct paths for polyp to cancer transformations. What we have not detailed is our optimizations on the droplet-based scRNA-seq approach that generate high quality data from challenging tissues such as the colonic epithelium. In this work, we provide a systematic dissection of tissue processing and microfluidic encapsulation factors that affect data quality generated on colonic epithelial cells using an open droplet-based scRNA-seq platform (inDrops). We optimized our process based on these factors to significantly decrease dying cell bodies and ambient RNA from being co-encapsulated with true cells, which led to the development of the current scRNA-seq protocol used for human specimens. We have also developed pre-filtering data metrics to score data quality stemming from degree of ambient contamination. We envision that these experimental optimizations reduce the amount of introduced artifacts, leading to more rigorous biological interpretations. Citation Format: Deronisha L. Arceneaux, Zhengyi Chen, Joey Simmons, Bob Chen, Cody Heiser, Yanwen Xu. The effects of tissue processing and microfluidic factors on single-cell RNA-sequencing data quality in colorectal tissues [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr B031.