Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation

Cells in a multicellular organism fulfill specific functions by enacting cell-type-specific programs of gene regulation. Single-cell RNA sequencing technologies have provided a transformative view of cell-type-specific gene expression, the output of cell-type-specific gene regulatory programs. This review discusses new single-cell genomic technologies that complement single-cell RNA sequencing by providing additional readouts of cellular state beyond the transcriptome. We highlight regression models as a simple yet powerful approach to relate gene expression to other aspects of cellular state, and in doing so, gain insights into the biochemical mechanisms that are necessary to produce a given gene expression output. Regression models offer a simple yet powerful framework for integrating single-cell transcriptomic, genetic, and epigenetic data to identify mechanisms of gene regulation. New protocols for CRISPR loss-of-function screens read out gene expression and genetic perturbations in the same single cells. Regressing expression (phenotype) versus genotype can provide insights into gene function and epistasis. Antibodies conjugated to barcoded oligonucleotides have been used to read out gene expression and protein epitope abundance in the same single cells. Regression modeling of such data may facilitate the reconstruction of cell signaling networks. Emerging single-cell ATAC-seq technologies measure chromatin accessibility in single cells and can facilitate the identification of noncoding DNA elements, sequence features, and transcription factors that drive gene expression dynamics.