EditID: open-access workflow for the multiplexed, semi-automated identification of CRISPR edited cell clones.

The growing catalogue of genetic variants associated with human disease offers an unprecedented opportunity to harness CRISPR/Cas9-based gene editing to illuminate genotype-phenotype relationships. However, identifying and isolating appropriately edited cells among thousands of targeted cells remains a considerable challenge, especially if gene editing is performed in parallel at multiple loci. Here we describe a complete open-access workflow for sample tracking, deep sequencing of barcoded PCR amplicons, high-throughput protein analysis, and intuitive data visualisation that facilitates the rapid identification (ID) of cell clones carrying desired edits (GenEditID). To validate GenEditID, we first targeted STAT3 in the human breast cancer cell line MCF7, and found that loss of STAT3 protein correlated well with the total burden of frameshift mutations as determined by both Sanger and Illumina sequencing. To test its scalability, we targeted multiple genes in the FTO (fat mass and obesity associated gene) locus in human pluripotent stem cells (hPSCs), and demonstrate the rapid, parallel identification and prioritisation of knockout (KO) clones for each targeted gene. We further show that these tools can be used to rapidly optimise conditions for less efficient gene editing events, such as the targeted insertion of single-base changes by homology directed repair. Overall, GenEditID enables non-specialist groups to accelerate their gene targeting efforts, facilitating the modelling of genetically complex human disease.