Simultaneous single cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships

Identifying gene functions and interactions in specific cellular contexts has been greatly enabled with functional genomics studies. CRISPR-based genetic screens have been proven invaluable in elucidating gene function in mammalian cells. Single cell functional genomics methods, such as Perturb-seq and Spear-ATAC, have made it possible to achieve high-throughput mapping of the functional effects of gene perturbations by profiling transcriptomes and DNA accessibility, respectively. Combining single-cell chromatin accessibility and transcriptomic data via multiomics technologies facilitates the discovery of novel cis-regulatory and gene regulatory interactions, however, bulk readouts complicate this analysis due to a heterogeneous response of cells to the same genetic perturbation/s, which could be mitigated by using transcriptional profiles of single cells to subset the ATAC-seq data. Existing methods to capture CRISPR guide RNAs to simultaneously assess the impact of genetic perturbations on RNA and ATAC profiles either require cloning gRNA libraries in specialized vectors or use complex protocols with multiple rounds of barcoding. Here, we introduce CAT-ATAC, a technique that simply captures CRISPR gRNAs within the 10X Genomics Multiome assay, generating paired transcriptome, chromatin accessibility and perturbation identity data from the same cells. We demonstrate up to 77% guide capture efficiency for both arrayed and pooled lentiviral transductions of sgRNAs in induced pluripotent stem cells (iPSCs) and cancer cell lines. This capability allows us to construct gene regulatory networks (GRNs) in cells under drug and genetic perturbation. We identified a GRN associated for dasatinib resistance, indirectly activated by the HIC2 gene. Using loss of function experiments, we further validated that the gene, ZFPM2, a component of the predicted GRN, also contributes to dasatinib resistance. CAT-ATAC can thus be used to generate high-content multidimensional genotype-phenotype maps to reveal novel gene and cellular interactions and functions.