Single‐Cell Transcriptomics


Single‐cell transcriptomics is an emerging field with a variety of applications, from revealing tissue heterogeneity and cell identity to deepening our understanding of complex biological systems. Single‐cell RNA‐sequencing assays the transcriptome of individual cells, allowing for a refined resolution of transcription compared to the averaged profiles from bulk RNA‐sequencing. Advances in the technology have resulted in a surplus of protocols, computational methods and studies. Multiple experimental approaches to isolate and sequence single cells provide many levels of coverage and throughput, allowing for the growth of computational pipelines and methods to assess and analyse this plethora. These advances will allow for the transcriptional documentation of all cells and cell types, which brings forth yet unknown opportunities for understanding all cellular life.

Key Concepts

  • Single‐cell transcriptomics has revolutionised the field of functional genomics, allowing for the evaluation of the transcriptome at cellular resolution.
  • Novel technologies have made it simple to sequence 1000s–10 000s of cells at a time.
  • Computational methods continue to advance in order to analyse and integrate the millions of cell samples.
  • Cellular temporal resolutions, rather than bulk averages, have allowed for lineage tracing to study development and differentiation, the discovery of subpopulations and rare cell types to assess tissue heterogeneity, with particular applications to disease.
  • Technical and biological noise still remains a major challenge, along with computational scalability and costs.
  • Integration of transcriptional information along with genomic, epigenomic and spatial data at the single‐cell level will expand our understanding further.

Keywords: single‐cell transcriptomics; scRNA‐seq; heterogeneity; cellular genomics; meta‐analysis

Figure 1. Single‐cell transcriptomics: an overview. (a) The transcriptome is all the RNA of a cell or genome, from nascent RNA to mature messengers. (b) A typical single‐cell experiment measures the transcriptome at cellular resolution. (c) The number of cells measured per study across the last decade. The introduction of droplet methods (dark spots) has meant that studies can reach cell counts close to millions. Adapted from Valentine Svensson et al. .
Figure 2. Single‐cell pipelines: from design to library preparation. (a) As in all gene expression experiments, sourcing of samples and experimental design share important tradeoffs. Depending on the organism, the cell origin and the location of the RNA, design choices will vary. Biological replicates are important but may generate batch effects if not carefully sequenced. The number of cells depends on cost and the questions being asked, as does sequencing depth. (b) Reverse transcription, amplification and library constructs differ by technology. Either polyA tailing or template switching generates cDNA. Amplification is either IVT or PCR. Library constructs for popular methods are listed.
Figure 3. Single‐cell pipelines: computational pipeline. (a) Sequenced reads are demultiplexed based on their type and then mapped using single‐cell specific aligners that are barcode aware. Normalisation, feature selection and then dimension reduction are followed by visualisation. Computational methods and analyses to assess gene expression. (b) The number of tools available are rapidly growing. (c) The most popular platforms are R and Python. (d) A number of functionalities exist, and most tools include clustering and visualisation.
Figure 4. The various themes and applications of single‐cell transcriptomics.


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Further Reading

Angerer P, Simon L, Tritschler S, et al. (2017) Single cells make big data: new challenges and opportunities in transcriptomics. Current Opinion in Systems Biology 4: 85–91.

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Hwang B, Lee JH and Bang D (2018) Single‐cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine 50 (8): 96.

Kanter I and Kalisky T (2015) Single cell transcriptomics: methods and applications. Frontiers in Oncology 5: 53–53.

Shapiro E, Biezuner T and Linnarsson S (2013) Single‐cell sequencing‐based technologies will revolutionize whole‐organism science. Nature Reviews Genetics 14: 618.

Stegle O, Teichmann SA and Marioni JC (2015) Computational and analytical challenges in single‐cell transcriptomics. Nature Reviews Genetics 16: 133.

Stuart T and Satija R (2019) Integrative single‐cell analysis. Nature Reviews Genetics 20 (5): 257–272.

Papatheodorou I, Moreno P, Manning J, et al. (2019) Expression Atlas update: from tissues to single cells. Nucleic Acids Research 48 (D1): D77–D83.

Regev A, Teichmann SA, Lander ES, et al. (2017) Science forum: the human cell atlas. Elife 6: e27041.

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How to Cite close
Ballouz, Sara(Feb 2020) Single‐Cell Transcriptomics. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028528]