Using single-cell genomics to understand developmental processes and cell fate decisions

Abstract

High-throughput -omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision-making is inherently a unicellular process to which "bulk" -omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single-cell methods bridge this gap, allowing high-throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single-cell gene expression data and highlight areas of developmental biology where single-cell techniques have made important contributions. These include understanding of cell-to-cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.

Keywords: cell fate; development; differentiation; single‐cell RNA‐seq; transcriptome.

Figure 1. Single‐cell library preparation summary

There are two primary methods for generating single‐cell transcriptomics data: plate‐based and droplet‐based methods, shown above. In summary, droplet‐based approaches offer high cell throughput, while plate‐based approaches provide higher resolution in each individual cell. Note that different implementations of these methods provide slightly different outputs and that some steps are excluded for clarity (e.g. cDNA amplification).

Figure 2. Pseudotime recapitulates developmental trajectories

(A) By observing similarities between the expression profiles of cells, it is possible to order cells along an axis of pseudotime that recapitulates developmental processes. (B) Having established this ordering, genes that show significant changes in expression along the developmental pathway may be identified.

Figure 3. scRNA‐seq resolves cellular heterogeneity

(A) While bulk gene expression assays provide an average read‐out of transcription over many cells, single‐cell RNA‐seq allows the assaying of gene expression in individual cells. (B) Single‐cell approaches facilitate working with complex systems such as embryos, where groups of cells with radically different expression profiles can be analysed without contamination from neighbouring tissues.

Figure 4. Allele‐specific expression at single‐cell resolution

By exploiting single nucleotide polymorphisms in single‐cell RNA‐seq reads, it is possible to quantify how much individual alleles contribute to a gene's total expression. For developmental biology, this can be applied to study, for example, when monoallelic expression patterns become set during embryonic development and how they relate to fate decision, as in the case of X chromosome inactivation (Chen et al, 2016).

Figure 5. Lineage tracing

Understanding how cells are related to each other is central to understanding how developmental processes work. However, comparison of transcriptomic profiles does not allow the reconstruction of these lineage relationships. Recent approaches used CRISPR/Cas9 to mutate a synthetic DNA construct, providing a genomic or transcriptional read‐out containing cell lineage information.

Figure 6. Spatial gene expression data

(A) Most single‐cell gene expression assays require dissociation of tissues, destroying locational information. New in situ hybridisation methods, however, offer high‐throughput transcriptomic quantification captured alongside intra‐ and inter‐cellular localisation. (B) In the absence of such techniques, others have used reference “atlases” to map back sequenced cells onto structures with known expression patterns.