Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer–gene regulatory interactions from single-cell datasets has been challenging. Here we introduce a family of classification models, scE2G, to predict enhancer–gene regulation. These models use features from single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element–gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped expression quantitative trait loci and genome-wide association study variant–gene associations and demonstrate state-of-the-art performance at prediction tasks across several cell types and categories of perturbations. We apply scE2G to build maps of enhancer–gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte count. The scE2G models will enable accurate mapping of enhancer–gene regulatory interactions across thousands of human cell types. scE2G is a family of models that predict enhancer–gene regulatory interactions from single-cell datasets and enable mapping of these interactions across diverse cell types and tissues.
Maya U. Sheth, Wei-Lin Qiu, X. Ma et al.· Nature Genetics· 0 citations
Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes – suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.
Samuel Maffa, Isabella Boyle, Lie Ward et al.· bioRxiv· 0 citations
An encyclopedia of enhancer–gene regulatory interactions in the human genome is built, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes, and improving analyses linking noncoding variants to target genes and cell types for common, complex diseases.
A. Gschwind, Kristy S. Mualim, Alireza Karbalayghareh et al.· Nature· 5 citations
