Article Figures & Data
Figures
- 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.
Article
- Article
- Abstract
- Introduction
- Generating single‐cell transcriptomic data
- State‐of‐the‐art analysis techniques
- The contribution of single‐cell expression data to developmental biology
- The importance of perturbations in single‐cell analyses
- The future of single‐cell transcriptomics in developmental biology
- Acknowledgements
- Conflict of interest
- References
- Figures & Data
- Transparent Process