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Functional genomics studies in model organisms and human cell lines provided important insights into gene functions and their context-dependent role in genetic circuits. However, our functional understanding of many of these genes and how they combinatorically regulate key biological processes, remains limited. To enable the SpCas9-dependent mapping of gene-gene interactions in human cells, we established 3Cs multiplexing for the generation of combinatorial gRNA libraries in a distribution-unbiased manner and demonstrate its robust performance. The optimal number for combinatorial hit calling was 16 gRNA pairs and the skew of a library’s distribution was identified as a critical parameter dictating experimental scale and data quality. Our approach enabled us to investigate 247,032 gRNA-pairs targeting 12,736 gene-interactions in human autophagy. We identified novel genes essential for autophagy and provide experimental evidence that gene-associated categories of phenotypic strengths exist in autophagy. Furthermore, circuits of autophagy gene interactions reveal redundant nodes driven by paralog genes. Our combinatorial 3Cs approach is broadly suitable to investigate unexpected gene-interaction phenotypes in unperturbed and diseased cell contexts.
Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.