Skip to main content

Thank you for visiting 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.

  • Letter
  • Published:

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex


Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing1,2,3,4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

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

Access options

Buy this article

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

Figure 1: Capturing single cells and quantifying mRNA levels using the C1 Single-Cell Auto Prep System.
Figure 2: Low-coverage single-cell mRNA sequencing is sufficient to detect genes contributing to cell identity.
Figure 3: Low-coverage single-cell mRNA sequencing distinguishes diverse neural cell types and identifies biomarkers in heterogeneous tissue.
Figure 4: EGR1 and FOS are candidate targets of Notch signaling in human radial glia identified using low-coverage single-cell mRNA-seq.

Similar content being viewed by others

Accession codes

Primary accessions

Sequence Read Archive

Referenced accessions

NCBI Reference Sequence


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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Kawaguchi, A. et al. Single-cell gene profiling defines differential progenitor subclasses in mammalian neurogenesis. Development 135, 3113–3124 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Kriegstein, A., Noctor, S. & Martinez-Cerdeno, V. Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. Nat. Rev. Neurosci. 7, 883–890 (2006).

    Article  CAS  Google Scholar 

  10. Ross, M.E. & Walsh, C.A. Human brain malformations and their lessons for neuronal migration. Annu. Rev. Neurosci. 24, 1041–1070 (2001).

    Article  CAS  Google Scholar 

  11. Miller, J.A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  Google Scholar 

  12. Miyoshi, G. & Fishell, G. Dynamic FoxG1 expression coordinates the integration of multipolar pyramidal neuron precursors into the cortical plate. Neuron 74, 1045–1058 (2012).

    Article  CAS  Google Scholar 

  13. Tarcic, G. et al. EGR1 and the ERK-ERF axis drive mammary cell migration in response to EGF. FASEB J. 26, 1582–1592 (2012).

    Article  CAS  Google Scholar 

  14. Bièche, I. et al. Molecular profiling of inflammatory breast cancer: identification of a poor-prognosis gene expression signature. Clin. Cancer Res. 10, 6789–6795 (2004).

    Article  Google Scholar 

  15. Fischer, A.J., Scott, M.A., Ritchey, E.R. & Sherwood, P. Mitogen-activated protein kinase-signaling regulates the ability of Muller glia to proliferate and protect retinal neurons against excitotoxicity. Glia 57, 1538–1552 (2009).

    Article  Google Scholar 

  16. Krol, A.J. et al. Evolutionary plasticity of segmentation clock networks. Development 138, 2783–2792 (2011).

    Article  CAS  Google Scholar 

  17. Shimojo, H., Ohtsuka, T. & Kageyama, R. Oscillations in notch signaling regulate maintenance of neural progenitors. Neuron 58, 52–64 (2008).

    Article  CAS  Google Scholar 

  18. Hansson, M.L. et al. MAML1 acts cooperatively with EGR1 to activate EGR1-regulated promoters: implications for nephrogenesis and the development of renal cancer. PLoS ONE 7, e46001 (2012).

    Article  CAS  Google Scholar 

  19. Housden, B.E. et al. Transcriptional dynamics elicited by a short pulse of notch activation involves feed-forward regulation by E(spl)/Hes genes. PLoS Genet. 9, e1003162 (2013).

    Article  CAS  Google Scholar 

  20. Min, I.M. et al. The transcription factor EGR1 controls both the proliferation and localization of hematopoietic stem cells. Cell Stem Cell 2, 380–391 (2008).

    Article  CAS  Google Scholar 

  21. Okada, S., Fukuda, T., Inada, K. & Tokuhisa, T. Prolonged expression of c-fos suppresses cell cycle entry of dormant hematopoietic stem cells. Blood 93, 816–825 (1999).

    CAS  PubMed  Google Scholar 

  22. Bonnert, T.P. et al. Molecular characterization of adult mouse subventricular zone progenitor cells during the onset of differentiation. Eur. J. Neurosci. 24, 661–675 (2006).

    Article  Google Scholar 

  23. Kornack, D.R. & Rakic, P. Changes in cell-cycle kinetics during the development and evolution of primate neocortex. Proc. Natl. Acad. Sci. USA 95, 1242–1246 (1998).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  25. Deng, Q., Ramskold, 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  Google Scholar 

  26. Sims, D., Sudbery, I., Ilott, N.E., Heger, A. & Ponting, C.P. Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15, 121–132 (2014).

    Article  CAS  Google Scholar 

  27. Faddah, D.A. et al. Single-cell analysis reveals that expression of nanog is biallelic and equally variable as that of other pluripotency factors in mouse ESCs. Cell Stem Cell 13, 23–29 (2013).

    Article  CAS  Google Scholar 

  28. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  CAS  Google Scholar 

  29. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    Article  CAS  Google Scholar 

  30. 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 

  31. 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).

    Article  CAS  Google Scholar 

  32. Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 509, 363–369 (2014).

    Article  Google Scholar 

  33. Fan, J.B. et al. Highly parallel genome-wide expression analysis of single mammalian cells. PLoS ONE 7, e30794 (2012).

    Article  CAS  Google Scholar 

  34. Fujita, P.A. et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res. 39, D876–D882 (2011).

    Article  CAS  Google Scholar 

  35. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  36. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  37. Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).

    Article  CAS  Google Scholar 

  38. Wallace, V.A. & Raff, M.C. A role for Sonic hedgehog in axon-to-astrocyte signalling in the rodent optic nerve. Development 126, 2901–2909 (1999).

    CAS  PubMed  Google Scholar 

Download references


We thank M. Bershteyn (University of California, San Francisco (UCSF)), E. Di Lullo (UCSF), C. Gertz (UCSF), C. McLean (23andMe), F. Ng (Fluidigm Corporation), J. Liu (UCSF), C. Lowe (Stanford University), M. Oldham (UCSF), A. Diaz (UCSF) and H. Retallack (UCSF) for reading the manuscript and for useful suggestions, S. Wang and Y. Wang for technical assistance, K. Davies (University of Oxford) for providing the antibody to syncoilin, A. Simeone (Institute of Genetics and Biophysics, Adriano Buzzati-Traverso) for providing the Emx2 probe, S. Yao and S. Ku (Allen Institute for Brain Research) for assistance in the neural progenitor induction protocol, G. Wang (Stanford University) for providing human induced pluripotent stem cells (hiPSCs) and the staff at the San Francisco General Hospital for providing access to donated fetal tissue. L.S., N.L. and M.A.U. are partially supported by California Institute for Regenerative Medicine Tools and Technologies II grant RT2-02052 to M.A.U., and the generation and maintenance of the hiPSCs and neural progenitor cells was also supported by RT2-02052 to M.A.U. A.A.P. is supported by a Damon Runyon Cancer Research Foundation postdoctoral fellowship (DRG-2166-13). This research was also supported by National Institute of Neurological Disorders and Stroke awards R01NS075998 and R01NS072630 to A.R.K.

Author information

Authors and Affiliations



A.A.P., A.A.L., J.A.A.W., J.S., T.J.N. and X.W. conceived and designed the study. A.A.P., A.A.L., B.A., J.H.L., J.S., J.W., L.K., L.S., N.L., P.C. and T.J.N. performed experiments. A.A.P., A.A.L., J.S., S.W., T.J.N. and X.W. analyzed the data. A.A.L., A.P.M., B.C., B.F., B.N.J., D.W.K., D.T., G.S., J.S., J.A.A.W., J.W., M.T., M.N., M.W., N.R., P.C., R.L., L.S.W. and X.W. were involved in system development. A.P.M., A.R.K., J.A.A.W., M.A.U. and R.C.J. supervised the project, helped with design and interpretation, and provided laboratory space and financial support. A.A.P., J.S. and T.J.N. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Alex A Pollen or Arnold R Kriegstein.

Ethics declarations

Competing interests

A.A.L., A.P.M., B.A., B.C., B.F., B.N.J., D.W.K., D.T., G.S., J.A.A.W., J.S., J.W., L.K., L.S., M.A.U., M.N., M.T., M.W., N.L., N.R., P.C., R.C.J., R.L., L.S.W., S.W. and X.W. have a financial interest in Fluidigm Corporation as employees and/or stockholders.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 (PDF 41060 kb)

Supplementary Table 1

Overview of cell types studied, sequencing depth, and alignment rates (XLSX 17 kb)

Supplementary Table 2

Single cell capture efficiency data (XLSX 9 kb)

Supplementary Table 3

Biological properties of PCA gene clusters (XLSX 37 kb)

Supplementary Table 4

Comparison of the top 500 PCA genes identified in low- and high-coverage mRNA Seq data across 301 cells. (XLSX 68 kb)

Supplementary Table 5

Top 500 PCA genes explaining variation across 65 neural cells (XLSX 16 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pollen, A., Nowakowski, T., Shuga, J. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32, 1053–1058 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing