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

Recent advances in single-cell DNA sequencing (scDNA-seq) and CRISPR technology have revolutionized gene therapy and drug discovery. However, data analysis requires expensive high-performance computing (HPC) clusters or large data servers, limiting reanalysis due to the lack of open-source software. 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. The efficiency, stability, and portability of scEDIT makes it an invaluable tool for uncovering new insights into the single cell data without requiring expensive computational resources.