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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

Sequence Read Archive

Referenced accessions

NCBI Reference Sequence


  1. 1.

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

  2. 2.

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

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

    , & Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. Nat. Rev. Neurosci. 7, 883–890 (2006).

  10. 10.

    & Human brain malformations and their lessons for neuronal migration. Annu. Rev. Neurosci. 24, 1041–1070 (2001).

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

    , , & Mitogen-activated protein kinase-signaling regulates the ability of Muller glia to proliferate and protect retinal neurons against excitotoxicity. Glia 57, 1538–1552 (2009).

  16. 16.

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

  17. 17.

    , & Oscillations in notch signaling regulate maintenance of neural progenitors. Neuron 58, 52–64 (2008).

  18. 18.

    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).

  19. 19.

    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).

  20. 20.

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

  21. 21.

    , , & Prolonged expression of c-fos suppresses cell cycle entry of dormant hematopoietic stem cells. Blood 93, 816–825 (1999).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

    , , & Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

  26. 26.

    , , , & Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15, 121–132 (2014).

  27. 27.

    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).

  28. 28.

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

  29. 29.

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

  30. 30.

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

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  36. 36.

    , & TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

  37. 37.

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

  38. 38.

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

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

Author notes

    • Alex A Pollen
    • , Tomasz J Nowakowski
    • , Joe Shuga
    •  & Xiaohui Wang

    These authors contributed equally to this work.


  1. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA.

    • Alex A Pollen
    • , Tomasz J Nowakowski
    • , Jan H Lui
    •  & Arnold R Kriegstein
  2. Department of Neurology, University of California, San Francisco, San Francisco, California, USA.

    • Alex A Pollen
    • , Tomasz J Nowakowski
    • , Jan H Lui
    •  & Arnold R Kriegstein
  3. Fluidigm Corporation, South San Francisco, California, USA.

    • Joe Shuga
    • , Xiaohui Wang
    • , Anne A Leyrat
    • , Nianzhen Li
    • , Lukasz Szpankowski
    • , Brian Fowler
    • , Peilin Chen
    • , Naveen Ramalingam
    • , Gang Sun
    • , Myo Thu
    • , Michael Norris
    • , Ronald Lebofsky
    • , Dominique Toppani
    • , Darnell W Kemp II
    • , Michael Wong
    • , Barry Clerkson
    • , Brittnee N Jones
    • , Shiquan Wu
    • , Lawrence Knutsson
    • , Beatriz Alvarado
    • , Jing Wang
    • , Lesley S Weaver
    • , Andrew P May
    • , Robert C Jones
    • , Marc A Unger
    •  & Jay A A West


  1. Search for Alex A Pollen in:

  2. Search for Tomasz J Nowakowski in:

  3. Search for Joe Shuga in:

  4. Search for Xiaohui Wang in:

  5. Search for Anne A Leyrat in:

  6. Search for Jan H Lui in:

  7. Search for Nianzhen Li in:

  8. Search for Lukasz Szpankowski in:

  9. Search for Brian Fowler in:

  10. Search for Peilin Chen in:

  11. Search for Naveen Ramalingam in:

  12. Search for Gang Sun in:

  13. Search for Myo Thu in:

  14. Search for Michael Norris in:

  15. Search for Ronald Lebofsky in:

  16. Search for Dominique Toppani in:

  17. Search for Darnell W Kemp in:

  18. Search for Michael Wong in:

  19. Search for Barry Clerkson in:

  20. Search for Brittnee N Jones in:

  21. Search for Shiquan Wu in:

  22. Search for Lawrence Knutsson in:

  23. Search for Beatriz Alvarado in:

  24. Search for Jing Wang in:

  25. Search for Lesley S Weaver in:

  26. Search for Andrew P May in:

  27. Search for Robert C Jones in:

  28. Search for Marc A Unger in:

  29. Search for Arnold R Kriegstein in:

  30. Search for Jay A A West in:


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.

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.

Corresponding authors

Correspondence to Alex A Pollen or Arnold R Kriegstein.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15

Excel files

  1. 1.

    Supplementary Table 1

    Overview of cell types studied, sequencing depth, and alignment rates

  2. 2.

    Supplementary Table 2

    Single cell capture efficiency data

  3. 3.

    Supplementary Table 3

    Biological properties of PCA gene clusters

  4. 4.

    Supplementary Table 4

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

  5. 5.

    Supplementary Table 5

    Top 500 PCA genes explaining variation across 65 neural cells

About this article

Publication history






Further reading