Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics

Abstract

Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.

Figure 1

Overview of single-cell-genomic data generation and evaluation, showing examples of experimental and computational analysis steps and specific methods along with citations. Sample generation uses a specific tissue to obtain single cells for analysis and ultimately a digital gene-expression matrix of transcript counts per gene per cell. This matrix is analyzed to identify relevant genes and cell types and to describe the complex relationships between these identities. These models can be used, for instance, to select genes to alter and control cellular transitions. To see this figure in color, go online.

Figure 2

Landscape picture of states and transitions: cell types remain embedded within a high-dimensional gene expression landscape and are available as fixed points or stable states of a cost function accounting for gene expression variability and mutual interactions between genes. Development initiates from a stable precursor (or progenitor) state (P) and as development proceeds intermediate unstable states (U) enable novel stable cell states (A and B) to emerge. Transitions between these stable states occur via “tilting” the landscape (a) where cell type B transitions to cell type A. Such transitions can occur because of the presence of “fields” such as growth factors and morphogens or because of genetic mutations. (b) Depending on the resolution used to describe the model, one may be able to resolve further types of cells that appear as one group at lower resolutions. For example, cell type C might have a bimodal population C’ and C”, and each subpopulation might have a preferential transition to neighboring minima (stable cell types) such as C’ to A and C” to B. Genetic and epigenetic changes may also be interpreted to create such changes in the landscape by creating additional cell types such as the splitting of one wide basin (C) to two subpopulations C’ and C”, thereby changing the developmental program of cells and lineage evolutions. t-SNE plots are shown for different resolutions, where points represent individual cell transcriptomes embedded in two-dimensional space, and point color corresponds to landscape regions. To see this figure in color, go online.