Single-cell transcriptome sequencing: recent advances and remaining challenges

Abstract

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Keywords: Single-cell RNA-sequencing; single-cell transcriptomic profiling.

Figure 1.. Common applications of single-cell RNA sequencing.

( a) Deconvolving heterogeneous cell populations. Clustering by single-cell transcriptomic profiles can reveal population substructure and enable the identification of cell subtypes and rare cell species (e.g. red cells above). Clusters may be tight and well defined (purple, red) or diffuse (blue). ( b) Trajectory analysis of cell state transitions. Single-cell RNA sequencing time-series data can be used to map cell developmental trajectories over the course of dynamic processes such as differentiation or signaling responses to an external stimulus. Some computational suites (e.g. Monocle ) can also accommodate branching trajectories, enabling identification of lineage-specific gene expression and key genes that drive branching events. ( c) Dissecting transcription mechanics. Genes’ expression profiles across many cells can be compared to study transcriptional bursting and to model the kinetics of stochastic gene expression. ( d) Network inference. Genes can be clustered by expression profile to identify modules of putatively co-regulated genes, and gene-gene covariation relationships can be used to infer gene regulatory networks or subnetworks.