Single-cell omics

Single-cell omics approaches are providing unprecedented insights into cellular function and dysfunction. This Editorial highlights the remarkable potential of these technologies and their profound impact on our understanding of biology and disease.

Tanja Woyke highlights a 2014 study by Kashtan et al., who applied single-cell genomics to populations of the marine cyanobacterium Prochlorococcus, revealing hundreds of subpopulations with distinct genomic backbones of this wild uncultured microorganism.

In this Journal Club, Celine Vallot discusses two 2015 papers that introduced the concept of high-throughput RNA barcoding, which paved the way for today’s plethora of single-cell omic approaches.

Roser Vento-Tormo highlights the synergy of single-cell omics and organoids by Camp et al., who used single-cell RNA sequencing to characterize the cell–cell communication events driving tissue formation in human liver organoids.

Marja Timmermans recalls a series of papers published back-to-back in Science in 2018 that reported the use of single-cell RNA sequencing to obtain a more complete picture of the expression landscapes describing early vertebrate development.

Practitioners in the field of single-cell omics are now faced with diverse options for analytical tools to process and integrate data from various molecular modalities. In an Expert Recommendation article, the authors provide guidance on robust single-cell data analysis, including choices of best-performing tools from benchmarking studies.

Single-cell, spatial and multi-omic profiling technologies generate large-scale data that reveal the output of genome-scale experiments across diverse cells, tissues and organisms. Cole Trapnell reviews the underlying core statistical challenges that need to be tackled to harness the power of these technologies and advance our understanding of gene function in health and disease.

In this Review, the authors discuss the latest advances in profiling multiple molecular modalities from single cells, including genomic, transcriptomic, epigenomic and proteomic information. They describe the diverse strategies for separately analysing different modalities, how the data can be computationally integrated, and approaches for obtaining spatially resolved data.

In this Review, Gaulton et al. discuss how single-cell epigenomic methods generate cell type-, subtype- and state-resolved maps of candidate cis-regulatory elements in heterogeneous human tissues that can help to interpret the genetic basis of common traits and diseases.

In this Review, the authors describe the emerging field of single-cell genetics, which lies at the intersection of single-cell genomics and human genetics. They review the first single-cell expression quantitative trait loci studies, which combine single-cell information with genotype data at the population scale and thereby link genetic variation to the cellular processes underpinning key aspects of human biology and disease.

Regulatory circuits of gene expression can be represented as gene regulatory networks (GRNs) that are useful to understand cellular identity and disease. Here, the authors review the computational methods used to infer GRNs — in particular from single-cell multi-omics data — as well as the biological insights that they can provide, and methods for their downstream analysis and experimental assessment.

In this Perspective, Lim et al. discuss the potential benefits of, and the challenges associated with, translating single-cell genomic approaches from research to clinical settings.

In this Review, Preissl, Gaulton and Ren discuss single-cell epigenomic methods and data analysis tools, their readiness for profiling cis-regulatory elements in human tissues and the insight they can provide into dynamic, context-specific gene regulation.

Single-cell transcriptomics is beginning to systematically define commonalities but also heterogeneity within and between organs for multiple human cell types. Here, the authors review emerging biological insights from cross-tissue single-cell transcriptomic studies into epithelial, fibroblast, vascular and immune cells.

In this Review, Ding, Sharon and Bar-Joseph discuss how dynamic features can be incorporated into single-cell transcriptomics studies, using both experimental and computational strategies to provide biological insights.

Combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics can localize transcriptionally characterized single cells within their native tissue context. This Review discusses methodologies and tools to integrate scRNA-seq with spatial transcriptomics approaches, and illustrates the types of insights that can be gained.

In this Perspective, Teschendorff and Feinberg describe how single-cell analysis methods based on statistical mechanics can provide valuable insights into developmental phenomena, such as differentiation potency and lineage trajectories, as well as disruption of these processes in cancer.

In this Review, Carter and Zhao discuss how single-cell sequencing technologies are being applied to investigate epigenetic heterogeneity among seemingly homogeneous populations of cells and how this epigenetic variability relates to cell–cell differences in gene expression.

Both genetic and non-genetic factors underlie the intratumoural heterogeneity that fuels cancer evolution. This Review discusses the application of single-cell multi-omics technologies to the study of cancer evolution, which capture and integrate the different layers of heritable information and reveal their complex interplay.

Understanding developmental trajectories has recently been enabled by progress in modern lineage-tracing methods that combine genetic lineage analysis with omics-based characterization of cell states (particularly transcriptomes). In this Review, Wagner and Klein discuss the conceptual underpinnings, experimental strategies and analytical considerations of these approaches, as well as the biological insights gained.