Exponential scaling of single-cell RNA-seq in the past decade

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Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.

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Figure 1: Scaling of scRNA-seq experiments.

Change history

  • 22 March 2018

    In the version of this article initially published, the sentence "This method was also used for MARS-seq (massively parallel scRNA-seq)33, and later for InDrop (droplet sequencing)34 and Seq-Well (a well-array-based variation on MARS-seq)35" was incorrect. The words "and Seq-Well (a well-array-based variation on MARS-seq)35" should have been deleted. This error has been corrected in the HTML and PDF versions of the article. In addition, refs. 36–41 in the original paper have been renumbered as 35–40 and original ref. 35 has been renumbered as ref. 41 throughout the text and in Figure 1.


  1. 1

    Gest, H. The discovery of microorganisms by Robert Hooke and Antoni Van Leeuwenhoek, fellows of the Royal Society. Notes Rec. R. Soc. Lond. 58, 187–201 (2004).

    Article  Google Scholar 

  2. 2

    Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016).

    CAS  Article  Google Scholar 

  3. 3

    Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J.M. & Awatramani, R. Disentangling neural cell diversity using single-cell transcriptomics. Nat. Neurosci. 19, 1131–1141 (2016).

    Article  Google Scholar 

  4. 4

    Mosmann, T.R., Cherwinski, H., Bond, M.W., Giedlin, M.A. & Coffman, R.L. Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins. J. Immunol. 136, 2348–2357 (1986).

    CAS  Google Scholar 

  5. 5

    Orkin, S.H. Diversification of haematopoietic stem cells to specific lineages. Nat. Rev. Genet. 1, 57–64 (2000).

    CAS  Article  Google Scholar 

  6. 6

    Zhu, J. Transcriptional regulation of Th2 cell differentiation. Immunol. Cell Biol. 88, 244–249 (2010).

    CAS  Article  Google Scholar 

  7. 7

    Ivanov, I.I., Zhou, L. & Littman, D.R. Transcriptional regulation of Th17 cell differentiation. Semin. Immunol. 19, 409–417 (2007).

    CAS  Article  Google Scholar 

  8. 8

    Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).

    CAS  Article  Google Scholar 

  9. 9

    Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C. & Teichmann, S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    CAS  Article  Google Scholar 

  10. 10

    Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl. Acad. Sci. USA 89, 3010–3014 (1992).

    CAS  Article  Google Scholar 

  11. 11

    Lambolez, B., Audinat, E., Bochet, P., Crépel, F. & Rossier, J. AMPA receptor subunits expressed by single Purkinje cells. Neuron 9, 247–258 (1992).

    CAS  Article  Google Scholar 

  12. 12

    Peixoto, A., Monteiro, M., Rocha, B. & Veiga-Fernandes, H. Quantification of multiple gene expression in individual cells. Genome Res. 14, 1938–1947 (2004).

    CAS  Article  Google Scholar 

  13. 13

    Sheng, H.Z., Lin, P.X. & Nelson, P.G. Analysis of multiple heterogeneous mRNAs in single cells. Anal. Biochem. 222, 123–130 (1994).

    CAS  Article  Google Scholar 

  14. 14

    Tietjen, I. et al. Single-cell transcriptional analysis of neuronal progenitors. Neuron 38, 161–175 (2003).

    CAS  Article  Google Scholar 

  15. 15

    Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).

    Article  Google Scholar 

  16. 16

    Kurimoto, K., Yabuta, Y., Ohinata, Y. & Saitou, M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protoc. 2, 739–752 (2007).

    CAS  Article  Google Scholar 

  17. 17

    Esumi, S. et al. Method for single-cell microarray analysis and application to gene-expression profiling of GABAergic neuron progenitors. Neurosci. Res. 60, 439–451 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  Article  Google Scholar 

  19. 19

    Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468–478 (2010).

    CAS  Article  Google Scholar 

  20. 20

    Tang, F. et al. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS One 6, e21208 (2011).

    CAS  Article  Google Scholar 

  21. 21

    Ramsköld, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  Google Scholar 

  22. 22

    Brouilette, S. et al. A simple and novel method for RNA-seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase. Dev. Dyn. 241, 1584–1590 (2012).

    CAS  Article  Google Scholar 

  23. 23

    Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675–685 (2010).

    CAS  Article  Google Scholar 

  24. 24

    Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    CAS  Article  Google Scholar 

  25. 25

    Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    CAS  Article  Google Scholar 

  26. 26

    Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    CAS  Article  Google Scholar 

  27. 27

    Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

    CAS  Article  Google Scholar 

  28. 28

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Boon, W.C. et al. Increasing cDNA yields from single-cell quantities of mRNA in standard laboratory reverse transcriptase reactions using acoustic microstreaming. J. Vis. Exp. 3144, e3144 (2011).

    Google Scholar 

  30. 30

    Klein, C.A. et al. Combined transcriptome and genome analysis of single micrometastatic cells. Nat. Biotechnol. 20, 387–392 (2002).

    CAS  Article  Google Scholar 

  31. 31

    Zhu, Y.Y., Machleder, E.M., Chenchik, A., Li, R. & Siebert, P.D. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30, 892–897 (2001).

    CAS  Article  Google Scholar 

  32. 32

    Baugh, L.R., Hill, A.A., Brown, E.L. & Hunter, C.P. Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Res. 29, E29 (2001).

    CAS  Article  Google Scholar 

  33. 33

    Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  Article  Google Scholar 

  34. 34

    Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  Article  Google Scholar 

  35. 35

    Vickovic, S. et al. Massive and parallel expression profiling using microarrayed single-cell sequencing. Nat. Commun. 7, 13182 (2016).

    CAS  Article  Google Scholar 

  36. 36

    Muraro, M.J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).

    CAS  Article  Google Scholar 

  37. 37

    Lönnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria. Sci. Immunol. 2, eaal2192 (2017).

    Article  Google Scholar 

  38. 38

    Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  Article  Google Scholar 

  39. 39

    Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).

    CAS  Article  Google Scholar 

  40. 40

    Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  41. 41

    Gierahn, T.M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    CAS  Article  Google Scholar 

  42. 42

    Fan, H.C., Fu, G.K. & Fodor, S.P.A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  Google Scholar 

  43. 43

    Bose, S. et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 16, 120 (2015).

    Article  Google Scholar 

  44. 44

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  Article  Google Scholar 

  45. 45

    Zheng, G.X.Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    CAS  Article  Google Scholar 

  46. 46

    Hochgerner, H. et al. STRT-seq-2i: dual-index 5′ single cell and nucleus RNA-seq on an addressable microwell array. Preprint at https://www.biorxiv.org/content/early/2017/04/20/126268 (2017).

  47. 47

    Costea, P.I., Lundeberg, J. & Akan, P. TagGD: fast and accurate software for DNA Tag generation and demultiplexing. PLoS One 8, e57521 (2013).

    CAS  Article  Google Scholar 

  48. 48

    Cusanovich, D.A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  Article  Google Scholar 

  49. 49

    Vitak, S.A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).

    CAS  Article  Google Scholar 

  50. 50

    Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    CAS  Article  Google Scholar 

  51. 51

    Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Preprint at https://www.biorxiv.org/content/early/2017/02/02/104844 (2017).

  52. 52

    Rosenberg, A.B. et al. Scaling single cell transcriptomics through split pool barcoding. Preprint at https://www.biorxiv.org/content/early/2017/02/02/105163 (2017).

  53. 53

    Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    CAS  Article  Google Scholar 

  54. 54

    Lake, B.B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    CAS  Article  Google Scholar 

  55. 55

    Lake, B.B. et al. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep. 7, 6031 (2017).

    Article  Google Scholar 

  56. 56

    Lee, J.H. Quantitative approaches for investigating the spatial context of gene expression. Wiley Interdiscip. Rev. Syst. Biol. Med. 9, e1369 (2017).

    Article  Google Scholar 

  57. 57

    Ståhl, P.L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  Google Scholar 

  58. 58

    Moffitt, J.R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl. Acad. Sci. USA 113, 14456–14461 (2016).

    CAS  Article  Google Scholar 

  59. 59

    Moffitt, J.R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. USA 113, 11046–11051 (2016).

    CAS  Article  Google Scholar 

  60. 60

    Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  Article  Google Scholar 

  61. 61

    Lee, J.H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).

    CAS  Article  Google Scholar 

  62. 62

    Lee, J.H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    CAS  Article  Google Scholar 

  63. 63

    Svensson, V., Teichmann, S.A. & Stegle, O. Spatial DE: identification of spatially variable genes. Preprint at https://www.biorxiv.org/content/early/2017/11/08/143321 (2017).

  64. 64

    Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    CAS  Article  Google Scholar 

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We thank Z.-S. Chong, K.N. Natarajan, T. Gomes, N. Edner and K. Meyer for reading the manuscript and giving helpful feedback. R.V.-T. is supported by an EMBO Long-Term Fellowship and a Human Frontier Science Program Long-Term Fellowship.

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V.S., R.V.-T. and S.A.T. wrote the manuscript. V.S. prepared Figure 1.

Corresponding authors

Correspondence to Valentine Svensson or Sarah A Teichmann.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Sample sizes from representative scRNA-seq studies. This table details the number of single cells reported in selected scRNA-seq studies from the literature where increases in scale or particularly large data sets are mentioned. Also indicated are the protocols used and the cell isolation strategies. (XLSX 12 kb)

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Svensson, V., Vento-Tormo, R. & Teichmann, S. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc 13, 599–604 (2018). https://doi.org/10.1038/nprot.2017.149

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