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

Svensson, Valentine, Vento-Tormo, Roser, Teichmann, Sarah A·2018·Nature Protocols
DOI 10.1038/nprot.2017.149
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Abstract

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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  • The embedding module fully retained information from raw expression values, a notable improvement over discretized values used in previous models.

    “This module fully retained information from the raw expression values, a notable improvement over the discretized values used in previous models 9,15 .”

    From Large-scale foundation model on single-cell transcriptomics (2024) · cites this paper as [9]

  • Existing work on pre-training models on single-cell gene expression data had to restrict their models to a small list of selected genes.

    “Existing work had to restrict their models to a small list of selected genes.”

    From Large-scale foundation model on single-cell transcriptomics (2024) · cites this paper as [9]

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Research works printed but not linked: 50

  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). · DOI
  2. Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016). · DOI · Paper page
  3. 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). · DOI
  4. Orkin, S.H. Diversification of haematopoietic stem cells to specific lineages. Nat. Rev. Genet. 1, 57–64 (2000). · DOI
  5. Zhu, J. Transcriptional regulation of Th2 cell differentiation. Immunol. Cell Biol. 88, 244–249 (2010). · DOI
  6. Ivanov, I.I., Zhou, L. & Littman, D.R. Transcriptional regulation of Th17 cell differentiation. Semin. Immunol. 19, 409–417 (2007). · DOI
  7. Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015). · DOI · Paper page
  8. 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). · DOI · Paper page
  9. Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl. Acad. Sci. USA 89, 3010–3014 (1992). · DOI
  10. Lambolez, B., Audinat, E., Bochet, P., Crépel, F. & Rossier, J. AMPA receptor subunits expressed by single Purkinje cells. Neuron 9, 247–258 (1992). · DOI
  11. Peixoto, A., Monteiro, M., Rocha, B. & Veiga-Fernandes, H. Quantification of multiple gene expression in individual cells. Genome Res. 14, 1938–1947 (2004). · DOI
  12. Sheng, H.Z., Lin, P.X. & Nelson, P.G. Analysis of multiple heterogeneous mRNAs in single cells. Anal. Biochem. 222, 123–130 (1994). · DOI
  13. Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006). · DOI
  14. 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). · DOI
  15. 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). · DOI
  16. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009). · DOI · Paper page
  17. 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). · DOI
  18. Tang, F. et al. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS One 6, e21208 (2011). · DOI
  19. 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). · DOI · Paper page
  20. 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). · DOI
  21. 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). · DOI
  22. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011). · DOI · Paper page
  23. 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). · DOI · Paper page
  24. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013). · DOI · Paper page
  25. 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).
  26. Klein, C.A. et al. Combined transcriptome and genome analysis of single micrometastatic cells. Nat. Biotechnol. 20, 387–392 (2002). · DOI
  27. 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). · DOI
  28. 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). · DOI
  29. 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). · DOI · Paper page
  30. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). · DOI · Paper page
  31. Vickovic, S. et al. Massive and parallel expression profiling using microarrayed single-cell sequencing. Nat. Commun. 7, 13182 (2016). · DOI
  32. Muraro, M.J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016). · DOI
  33. 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). · DOI
  34. Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013). · DOI
  35. Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). · DOI · Paper page
  36. Gierahn, T.M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017). · DOI
  37. Bose, S. et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 16, 120 (2015). · DOI
  38. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012). · DOI · Paper page
  39. 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). · DOI
  40. Costea, P.I., Lundeberg, J. & Akan, P. TagGD: fast and accurate software for DNA Tag generation and demultiplexing. PLoS One 8, e57521 (2013). · DOI
  41. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017). · DOI
  42. 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). · DOI · Paper page
  43. 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). · DOI
  44. Lee, J.H. Quantitative approaches for investigating the spatial context of gene expression. Wiley Interdiscip. Rev. Syst. Biol. Med. 9, e1369 (2017). · DOI
  45. 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). · DOI · Paper page
  46. 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). · DOI
  47. 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). · DOI · Paper page
  48. 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). · DOI · Paper page
  49. Lee, J.H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). · DOI · Paper page
  50. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013). · DOI · Paper page

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Metadata

Authors
Svensson, V., Vento-Tormo, R., & Teichmann, S. A.
ORCID
Svensson, Valentine
Venue
Nature Protocols
Publisher
Springer Science and Business Media LLC
Published
2018-03-01
Volume
13
Issue
4
Pages
599-604
ISSN
  • 1754-2189
  • 1750-2799
DOI
10.1038/nprot.2017.149
Cited by
1,034
References
14
Language
en
Licence
www.springer.com/tdm (tdm)

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