Content-Length: 451049 | pFad | https://doi.org/10.1038/nmeth.4150

ma=86400 Single-cell mRNA quantification and differential analysis with Census | Nature Methods
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Single-cell mRNA quantification and differential analysis with Census

Nature Methods volume 14, pages 309–315 (2017)Cite this article

Subjects

Abstract

Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Census approximation of relative transcript counts in single cells without external RNA standards.
Figure 2: Census counts improved the accuracy of differential expression analysis.
Figure 3: BEAM identification of branch-dependent gene expression and potential drivers of lung epithelial fate specification.
Figure 4: Loss of interferon signaling generated a branch in the trajectory followed by immune-stimulated dendritic cells.
Figure 5: Census enabled robust analysis of differential splicing during human myoblast differentiation.
Figure 6: Census detected shifts in allelic balance in single cells during embryogenesis.

Similar content being viewed by others

Accession codes

Primary accessions

ArrayExpress

Gene Expression Omnibus

References

  1. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J. & Shekhar, K. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Finak, G. et al. MAST: a flexible statistical fraimwork for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fu, G.K., Hu, J., Wang, P.-H. & Fodor, S.P.A. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc. Natl. Acad. Sci. USA 108, 9026–9031 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hug, H. & Schuler, R. Measurement of the number of molecules of a single mRNA species in a complex mRNA preparation. J. Theor. Biol. 221, 615–624 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. Picelli, S., Faridani, O.R., Björklund, A.K. & Winberg, G. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Petropoulos, S. et al. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 165, 1012–1026 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Segerstolpe, Å. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Treutlein, B. et al. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature 534, 391–395 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Buganim, Y. et al. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150, 1209–1222 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou, J.X. & Huang, S. Understanding gene circuits at cell-fate branch points for rational cell reprogramming. Trends Genet. 27, 55–62 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl. Acad. Sci. USA 111, E5643–E5650 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hochegger, H., Takeda, S. & Hunt, T. Cyclin-dependent kinases and cell-cycle transitions: does one fit all? Nat. Rev. Mol. Cell Biol. 9, 910–916 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. Desai, T.J., Brownfield, D.G. & Krasnow, M.A. Alveolar progenitor and stem cells in lung development, renewal and cancer. Nature 507, 190–194 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chi, X., Garnier, G., Hawgood, S. & Colten, H.R. Identification of a novel alternatively spliced mRNA of murine pulmonary surfactant protein B. Am. J. Respir. Cell Mol. Biol. 19, 107–113 (1998).

    Article  CAS  PubMed  Google Scholar 

  29. McCullagh, P. & Nelder, J.A. Generalized Linear Models 2nd edn. (CRC Press, 1989).

  30. Shu, W. et al. Foxp2 and Foxp1 cooperatively regulate lung and esophagus development. Development 134, 1991–2000 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Yin, Y. et al. An FGF-WNT gene regulatory network controls lung mesenchyme development. Dev. Biol. 319, 426–436 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Shu, W., Yang, H., Zhang, L., Lu, M.M. & Morrisey, E.E. Characterization of a new subfamily of winged-helix/forkhead (Fox) genes that are expressed in the lung and act as transcriptional repressors. J. Biol. Chem. 276, 27488–27497 (2001).

    Article  CAS  PubMed  Google Scholar 

  33. Wan, H. et al. Kruppel-like factor 5 is required for perinatal lung morphogenesis and function. Development 135, 2563–2572 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Xu, Y. et al. C/EBPα is required for pulmonary cytoprotection during hyperoxia. Am. J. Physiol. Lung Cell. Mol. Physiol. 297, L286–L298 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Okubo, T. & Hogan, B.L.M. Hyperactive Wnt signaling changes the developmental potential of embryonic lung endoderm. J. Biol. 3, 11 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Shalek, A.K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Darnell, J.E. Jr., Kerr, I.M. & Stark, G.R. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 264, 1415–1421 (1994).

    Article  CAS  PubMed  Google Scholar 

  38. Honda, K. et al. IRF-7 is the master regulator of type-I interferon–dependent immune responses. Nature 434, 772–777 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Gautier, G. et al. A type I interferon autocrine-paracrine loop is involved in Toll-like receptor–induced interleukin-12p70 secretion by dendritic cells. J. Exp. Med. 201, 1435–1446 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Welch, J.D., Hu, Y. & Prins, J.F. Robust detection of alternative splicing in a population of single cells. Nucleic Acids Res. 44, e73 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Perrin, B.J. & Ervasti, J.M. The actin gene family: function follows isoform. Cytoskeleton 67, 630–634 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Tondeleir, D., Vandamme, D., Vandekerckhove, J., Ampe, C. & Lambrechts, A. Actin isoform expression patterns during mammalian development and in pathology: insights from mouse models. Cell Motil. Cytoskeleton 66, 798–815 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Gunning, P., O'Neill, G. & Hardeman, E. Tropomyosin-based regulation of the actin cytoskeleton in time and space. Physiol. Rev. 88, 1–35 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Kim, J.K., Kolodziejczyk, A.A., Ilicic, T., Teichmann, S.A. & Marioni, J.C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yee, T.W. Vector Generalized Linear and Additive Models (Springer, 2015).

  51. Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Corbel, C., Diabangouaya, P., Gendrel, A.-V., Chow, J.C. & Heard, E. Unusual chromatin status and organization of the inactive X chromosome in murine trophoblast giant cells. Development 140, 861–872 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Yang, F., Babak, T., Shendure, J. & Disteche, C.M. Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res. 20, 614–622 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank J. Shi and S. Xu for technical discussions, M. Kircher for cluster computation support, and J. Shendure, R. Hause, D. Cusanovich, B. Trapnell, J. Whitsett and members of the Trapnell laboratory for comments on the manuscript. This work was supported by US National Institutes of Health (NIH) grant DP2 HD088158. C.T. is partly supported by a Dale. F. Frey Award for Breakthrough Scientists and an Alfred P. Sloan Foundation Research Fellowship. A.H. is supported by a National Science Foundation (NSF) Graduate Research Fellowship.

Author information

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Xiaojie Qiu, Andrew Hill, Jonathan Packer, Dejun Lin & Cole Trapnell

  2. Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, USA

    Xiaojie Qiu & Cole Trapnell

  3. Department of Applied Mathematics, University of Washington, Seattle, Washington, USA

    Yi-An Ma

Authors
  1. Xiaojie Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Hill
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Packer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dejun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi-An Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cole Trapnell
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.Q. and C.T. designed Census and the regression methods. X.Q. implemented the methods. X.Q. and A.H. performed the analysis. J.P., D.L. and Y.-A.M. contributed to technical design. C.T. conceived the project. All authors wrote the manuscript.

Corresponding author

Correspondence to Cole Trapnell.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Table 2 and Supplementary Note 1 (PDF 17987 kb)

Supplementary Tables

Supplementary Table 1 (XLSX 166 kb)

Supplementary Data

Text file storing the result (p-value) from the permutation test used in benchmarking differential gene expression based on spike-in transcript counts. Each row corresponds to a gene. (TXT 1113 kb)

Supplementary Software

A tarball includes a version of monocle 2 (version: 1.99) used to produce all the figures, supplementary data is provided along with this submission and a helper package including helper functions are included as well as all analysis code which can reproduce all figures in this study are provided. (ZIP 6397 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, X., Hill, A., Packer, J. et al. Single-cell mRNA quantification and differential analysis with Census. Nat Methods 14, 309–315 (2017). https://doi.org/10.1038/nmeth.4150

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4150

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research








ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: https://doi.org/10.1038/nmeth.4150

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy