A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which obscure the detection of biologically distinct transcripts expressed at up to several orders of magnitude lower levels. To address this challenge, we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We then experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover novel biological signatures in different biological contexts. We further emphasize scCLEAN’s versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show, the projected utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable. This study generated scRNAseq using two primary cell samples derived from healthy human donors- PBMCs and vascular smooth muscle cells. For all samples, standard sample prep was completed per the manufacturer's protocol for 10X or PacBio, respectively, for the PBMCs and 10X Genomics for the VSMCs. Samples were aliquoted to allow for the standard prep and a prep using scCLEAN, which is a CRISPR/Cas9 based delpetion protocol that removes redundant reads to redistribute reads to allow for improved readouts. For VSMCs, matched donors were used for the isolation of coronary and pulmonary arteries, with a total of 4 donors utilized. For computational analysis, donors were pooled together using CellRanger for analysis using Seurat, Scanpy, MAGIC, PHATE, randomly, scFATES, and CellRank packages in R or python, as appropriate.