Single cell Edit Detection and Identification Tool (scEDIT): computational workflow for efficient and economical single cell analysis of CRISPR edited cells

Advances in CRISPR technology are revolutionizing gene therapy and drug discovery. The advent of single-cell DNA sequencing (scDNA-seq) is generating granular level insight into CRISPR induced genomic changes both intended and unintended. However, analysis of single cell data necessitates expensive high-performance computing (HPC) clusters or large data servers. Analysis of the single cell data is currently limited unaffordable computational cost and the lack of open-source software tools. To address this, we present scEDIT, a fast, lightweight, portable, and standalone software for pre- and post-processing CRISPR editing data from the Tapestri single-cell DNA-seq platform. scEDIT is memory-efficient, multithreaded, and compatible with most UNIX based systems. Tests using a low-cost desktop and public single cell CRISPR data demonstrate that the tool can efficiently process raw sequences, identify cell barcodes, count unedited and edited amplicons per cell, and outputs detailed filtered reads. Analysis of the single cell CRISPR data reveals indel patterns shared between in vitro experiments and unique indel profiles detected for in vivo study. Results further demonstrate the ability of single cell analysis in providing quantitative insights into the true zygosity of edited cell population. Although data shows a linear relation between indel frequencies by read count and cell count details of indel share between difference cells can only be truly explored with single cell data. Furthermore, scEDIT can analyze massively parallel base editing in human hematopoietic stem cells providing insights into the base editing patterns for hundreds of gRNAs targeting GATA1 and HbF gene. Importantly, scEDIT detected low frequency unintended and potentially harmful deletions introduced during the base editing. The reanalysis of two independent studies demonstrated the efficiency, stability, and portability of scEDIT making it an invaluable tool for uncovering new insights into the single cell data with limited and inexpensive computational resources.