Comment in 2020

Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.

The field of single-cell RNA sequencing (scRNA-seq) has been paired with genomics, epigenomics, spatial omics, proteomics and imaging to achieve multimodal measurements of individual cellular phenotypes and genotypes. In its purest form, single-cell multimodal omics involves the simultaneous detection of multiple traits in the same cell. More broadly, multimodal omics also encompasses comparative pairing and computational integration of measurements made across multiple distinct cells to reconstruct phenotypes. Here I highlight some of the biological insights gained from multimodal studies and discuss the challenges and opportunities in this emerging field.

Advances in single-cell genomics technologies have enabled investigation of the gene regulation programs of multicellular organisms at unprecedented resolution and scale. Development of single-cell multimodal omics tools is another major step toward understanding the inner workings of biological systems.