Letter | Published:

A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex

Nature volume 555, pages 524528 (22 March 2018) | Download Citation


The mammalian prefrontal cortex comprises a set of highly specialized brain areas containing billions of cells and serves as the centre of the highest-order cognitive functions, such as memory, cognitive ability, decision-making and social behaviour1,2. Although neural circuits are formed in the late stages of human embryonic development and even after birth, diverse classes of functional cells are generated and migrate to the appropriate locations earlier in development. Dysfunction of the prefrontal cortex contributes to cognitive deficits and the majority of neurodevelopmental disorders; there is therefore a need for detailed knowledge of the development of the prefrontal cortex. However, it is still difficult to identify cell types in the developing human prefrontal cortex and to distinguish their developmental features. Here we analyse more than 2,300 single cells in the developing human prefrontal cortex from gestational weeks 8 to 26 using RNA sequencing. We identify 35 subtypes of cells in six main classes and trace the developmental trajectories of these cells. Detailed analysis of neural progenitor cells highlights new marker genes and unique developmental features of intermediate progenitor cells. We also map the timeline of neurogenesis of excitatory neurons in the prefrontal cortex and detect the presence of interneuron progenitors in early developing prefrontal cortex. Moreover, we reveal the intrinsic development-dependent signals that regulate neuron generation and circuit formation using single-cell transcriptomic data analysis. Our screening and characterization approach provides a blueprint for understanding the development of the human prefrontal cortex in the early and mid-gestational stages in order to systematically dissect the cellular basis and molecular regulation of prefrontal cortex function in humans.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from $8.99

All prices are NET prices.


Primary accessions

Gene Expression Omnibus


  1. 1.

    & Evolution of the brain and intelligence in primates. Prog. Brain Res. 195, 413–430 (2012)

  2. 2.

    & Significant features in the early prenatal development of the human brain. Ann. Anat. 190, 105–118 (2008)

  3. 3.

    Evolution of the neocortex: a perspective from developmental biology. Nat. Rev. Neurosci. 10, 724–735 (2009)

  4. 4.

    , & Direct neuronal reprogramming: learning from and for development. Development 143, 2494–2510 (2016)

  5. 5.

    , , , & Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015)

  6. 6.

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

  7. 7.

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

  8. 8.

    et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017)

  9. 9.

    et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017)

  10. 10.

    et al. Subcortical origins of human and monkey neocortical interneurons. Nat. Neurosci. 16, 1588–1597 (2013)

  11. 11.

    et al. Transcriptional networks controlled by NKX2-1 in the development of forebrain GABAergic neurons. Neuron 91, 1260–1275 (2016)

  12. 12.

    et al. Diversity of cortical interneurons in primates: the role of the dorsal proliferative niche. Cell Rep. 9, 2139–2151 (2014)

  13. 13.

    et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013)

  14. 14.

    & Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat. Rev. Neurosci. 15, 300–312 (2014)

  15. 15.

    et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016)

  16. 16.

    et al. A complement–microglial axis drives synapse loss during virus-induced memory impairment. Nature 534, 538–543 (2016)

  17. 17.

    et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015)

  18. 18.

    , & Conical expansion of the outer subventricular zone and the role of neocortical folding in evolution and development. Front. Hum. Neurosci. 7, 424 (2013)

  19. 19.

    , , & Cortical neurons arise in symmetric and asymmetric division zones and migrate through specific phases. Nat. Neurosci. 7, 136–144 (2004)

  20. 20.

    , & Proliferation control in neural stem and progenitor cells. Nat. Rev. Neurosci. 16, 647–659 (2015)

  21. 21.

    et al. Intermediate neuronal progenitors (basal progenitors) produce pyramidal-projection neurons for all layers of cerebral cortex. Cereb. Cortex 19, 2439–2450 (2009)

  22. 22.

    , & Contribution of intermediate progenitor cells to cortical histogenesis. Arch Neurol. 64, 639–642 (2007)

  23. 23.

    et al. Cortical and clonal contribution of Tbr2 expressing progenitors in the developing mouse brain. Cereb. Cortex 25, 3290–3302 (2015)

  24. 24.

    , & Development and evolution of the human neocortex. Cell 146, 18–36 (2011)

  25. 25.

    , & Cortical interneuron specification: the juncture of genes, time and geometry. Curr. Opin. Neurobiol. 42, 17–24 (2017)

  26. 26.

    , & Rin GTPase couples nerve growth factor signaling to p38 and b-Raf/ERK pathways to promote neuronal differentiation. J. Biol. Chem. 280, 37599–37609 (2005)

  27. 27.

    & Autism spectrum disorders and schizophrenia spectrum disorders: excitation/inhibition imbalance and developmental trajectories. Front. Psychiatry 8, 69 (2017)

  28. 28.

    , , & Prefrontal cortex and social cognition in mouse and man. Front. Psychol. 6, 1805 (2015)

  29. 29.

    et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014)

  30. 30.

    et al. The tanscriptome and DNA methylome landscapes of human primordial germ cells. Cell 161, 1437–1452 (2015)

  31. 31.

    et al. Single-cell RNA-seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20, 891–892 (2017)

  32. 32.

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

  33. 33.

    et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 37, D590–D598 (2006)

  34. 34.

    , & HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

  35. 35.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  36. 36.

    , & Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009)

  37. 37.

    , & Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009)

  38. 38.

    et al. Meta- and orthogonal integration of influenza ‘omics’ data defines a role for UBR4 in virus budding. Cell Host Microbe 18, 723–735 (2015)

  39. 39.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

  40. 40.

    et al. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27, 29–34 (1999)

  41. 41.

    The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000)

  42. 42.

    et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011)

  43. 43.

    et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016)

  44. 44.

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

  45. 45.

    , & Step-by-step in situ hybridization method for localizing gene expression changes in the brain. Methods Mol. Biol. 670, 207–230 (2011)

Download references


We thank members of the Wang and Tang laboratories for discussions. This work was supported by National Basic Research Program of China (2014CB964600), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16020601), the National Natural Science Foundation of China (NSFC) (91732301, 31371100, 31771140), National Key Research and Development Program of China (2017YFA0103303, 2017YFA0102601), Shanghai Brain-Intelligence Project from STCSM (16JC1420500), Newton Advanced Fellowship (NA140246) to X.W. and Youth Innovation Promotion Association CAS to Q.W.

Author information

Author notes

    • Suijuan Zhong
    • , Shu Zhang
    • , Xiaoying Fan
    • , Qian Wu
    •  & Liying Yan

    These authors contributed equally to this work.


  1. State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China

    • Suijuan Zhong
    • , Qian Wu
    • , Long Li
    • , Le Sun
    • , Na Pan
    •  & Xiaoqun Wang
  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    • Suijuan Zhong
    • , Qian Wu
    • , Long Li
    •  & Xiaoqun Wang
  3. Beijing Advanced Innovation Center for Genomics, College of Life Sciences, Department of Obstetrics and Gynecology, Third Hospital, Peking University, Beijing, 100871, China

    • Shu Zhang
    • , Xiaoying Fan
    • , Liying Yan
    • , Ji Dong
    • , Fuchou Tang
    •  & Jie Qiao
  4. Obstetrics and Gynecology, Medical Center of Severe Cardiovascular of Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China

    • Haofeng Zhang
    • , Xiaohui Xu
    •  & Jun Zhang
  5. Biomedical Institute for Pioneering Investigation via Convergence and Center for Reproductive Medicine, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, 100871, China

    • Fuchou Tang
    •  & Jie Qiao
  6. Peking–Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China

    • Fuchou Tang
    •  & Jie Qiao
  7. Beijing Institute for Brain Disorders, Beijing, 100069, China.

    • Xiaoqun Wang


  1. Search for Suijuan Zhong in:

  2. Search for Shu Zhang in:

  3. Search for Xiaoying Fan in:

  4. Search for Qian Wu in:

  5. Search for Liying Yan in:

  6. Search for Ji Dong in:

  7. Search for Haofeng Zhang in:

  8. Search for Long Li in:

  9. Search for Le Sun in:

  10. Search for Na Pan in:

  11. Search for Xiaohui Xu in:

  12. Search for Fuchou Tang in:

  13. Search for Jun Zhang in:

  14. Search for Jie Qiao in:

  15. Search for Xiaoqun Wang in:


Q.W., X.F., J.Q., F.T. and X.W. conceived the project, designed the experiments and wrote the manuscript. S.Zho. and X.F. performed RNA-seq. S.Zha. and J.D. analysed the data. J.Z., L.L., L.S., H.Z., L.Y. and X.X. prepared the samples. S.Zho. and Q.W. performed immunofluorescence, in situ hybridization and imaging. L.S. and N.P. performed the electrophysiology experiments. All authors edited and proofread the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Fuchou Tang or Jun Zhang or Jie Qiao or Xiaoqun Wang.

Reviewer Information Nature thanks H. Song and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table

    This file contains supplementary table 1 - gene list of different cells types and comparison. The spreadsheets include marker genes of all 6 major cell types, specific genes of the sub-clusters within each cell type, excitatory neuron markers of different weeks and gestational stages.

About this article

Publication history







By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.