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Recent technological advances are providing unprecedented opportunities to analyse the complexities of biological systems at the single-cell level. Various crucial biological phenomena are either invisible or only partially characterized when interrogated using standard analyses that average data across a bulk population of cells. However, high-throughput analyses of the genomes, transcriptomes and proteomes of single cells are providing novel and important insights into diverse processes such as development, gene-expression dynamics, tissue heterogeneity and disease pathogenesis.
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.
The functional interpretation of single-cell RNA sequencing (scRNA-seq) data can be enhanced by integrating additional data types beyond RNA-based gene expression. In this Review, Stuart and Satija discuss diverse approaches for integrative single-cell analysis, including experimental methods for profiling multiple omics types from the same cells, analytical approaches for extracting additional layers of information directly from scRNA-seq data and computational integration of omics data collected across different cell samples.
Single-cell RNA sequencing (scRNA-seq) enables transcriptome-based characterization of the constituent cell types within a heterogeneous sample. However, reliable analysis and biological interpretation typically require optimal use of clustering algorithms. This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.
As the genetic and phenotypic heterogeneities among cells become more appreciated, there is increasing demand for technologies that facilitate high-throughput and high-efficiency single-cell 'omic' analyses in miniaturized and automated formats. This Review discusses the diverse microfluidic methodologies — with a primary focus on valve-, droplet- and nanowell-based platforms — for characterization of the genomes, epigenomes, transcriptomes and proteomes of single cells, and addresses technical considerations and future opportunities.
Lineage analyses of multicellular organisms provide key insights into developmental mechanisms and how these developmental trajectories go awry in diverse diseases. This Review discusses the features, technical challenges and latest opportunities of an evolving range of sophisticated genetic techniques for tracking cell lineages in organisms. These strategies include methods for prospective tracking using engineered genetic constructs, as well as retrospective tracking based on naturally occurring somatic mutations.
Single-cell genome sequencing can provide detailed insights into the composition of single genomes that are not readily apparent when studying bulk cell populations. This Review discusses the considerable technical challenges of amplifying and interrogating genomes from single cells, emerging innovative solutions and various applications in microbiology and human disease, in particular in cancer.
Various methodologies have been developed to characterize diverse features of chromatin, but understanding how epigenomic states contribute to cellular heterogeneity requires adoption of these techniques at the single-cell level. This article discusses the technological developments driving single-cell epigenomics, including the practical and bioinformatic challenges and emerging biological insights.
High-throughput RNA sequencing (RNA-seq) is a powerful method for transcriptome-wide analysis that has recently been applied to single cells. This Review discusses the analytical and computational challenges of processing and analysing single-cell RNA-seq data, paying special consideration to differences relative to the analysis of RNA-seq data generated from bulk cell populations and discussing how single-cell-specific biological insights can be obtained.
Large-scale genetic perturbation screens have been central to many biological discoveries. This Review outlines the recent advances in the quantification of various perturbations across large numbers of single cells simultaneously and describes the use of genetic perturbation screens to infer functional interactions between genes and phenotypes.
Preserving spatial information in gene expression analyses is key for interpreting the single-cell tissue context (and even subcellular environments) of RNAs to achieve a more complete understanding of the underlying physiology. This Innovation article describes the emerging technologies of and biological insights from spatially resolved transcriptomics technologies, and how they set the stage for comprehensive investigations using complementary omic approaches.
Single-cell sequencing of uncultivated microbial species is rapidly providing a wealth of new information. Here, the authors provide an update on recent progress in capturing novel genomes, large-scale environmental studies and research relating to human health, as well as recent methodological improvements and remaining technical challenges.
Technologies that are based on next-generation sequencing are increasingly being used to study individual cells. The authors discuss the application of this approach to single-cell genomics and transcriptomics, and explore the implications for both basic research and medicine.
Microfluidic 'lab-on-a-chip' devices can be used to study the dynamics of gene networks in single cells. This Review discusses the various designs of these devices and the insights into modelling the complex dynamics of gene regulation that these new technologies have provided.
