Combinatorial CRISPR-Cas screens have advanced the mapping of genetic interactions, but their experimental scale limits the number of targetable gene combinations. Here, we describe 3Cs multiplexing, a rapid and scalable method to generate highly diverse and uniformly distributed combinatorial CRISPR libraries. We demonstrate that the library distribution skew is the critical determinant of its required screening coverage. By circumventing iterative cloning of PCR-amplified oligonucleotides, 3Cs multiplexing facilitates the generation of combinatorial CRISPR libraries with low distribution skews. We show that combinatorial 3Cs libraries can be screened with minimal coverages, reducing associated efforts and costs at least 10-fold. We apply a 3Cs multiplexing library targeting 12,736 autophagy gene combinations with 247,032 paired gRNAs in viability and reporter-based enrichment screens. In the viability screen, we identify, among others, the synthetic lethal WDR45B-PIK3R4 and the proliferation-enhancing ATG7-KEAP1 genetic interactions. In the reporter-based screen, we identify over 1,570 essential genetic interactions for autophagy flux, including interactions among paralogous genes, namely ATG2A-ATG2B, GABARAP-MAP1LC3B and GABARAP-GABARAPL2. However, we only observe few genetic interactions within paralogous gene families of more than two members, indicating functional compensation between them. This work establishes 3Cs multiplexing as a platform for genetic interaction screens at scale.
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.
