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

Abstract

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.

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Figure 1: Molecular diversity of cells from the developing human PFC.
Figure 2: Molecular signature of neural progenitor cells of the developing human PFC.
Figure 3: Dynamics of neurogenesis in the developing human PFC.
Figure 4: Interneuron development and signalling pathways that regulate neuronal maturation.

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Acknowledgements

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

Authors and Affiliations

Authors

Contributions

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.

Corresponding authors

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

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Competing interests

The authors declare no competing financial interests.

Additional information

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 figures and tables

Extended Data Figure 1 Single-cell RNA-seq information and molecular diversity of single cells.

a, Experimental workflow for single-cell RNA-seq of human developing PFC. b, Table summarizing PFC sampling. c, t-SNE plots of cells in the PFC. n = 3 (GW10) and n = 2 (GW23) independent biological samples. No obvious differences in distribution were observed among the different batches at the same development stages. Each colour represents the gestational week, and the colour contours correspond to the cell types. Expression of known markers is shown using the same layout (grey, no expression; yellow–red, relative expression). d, Heat map shows blocks of genes enriched in each cell type. Right, specific genes related to each type are highlighted with enriched gene ontology terms. n = 2,309 cells.

Extended Data Figure 2 Molecular diversity of subgroups of cells.

a, Heat map showing the expression level and identity of genes in six major cell types in the PFC. Expression of genes known to be expressed in each cell type is shown to the right of each heat map panel. b, Expression of known markers is shown using the same layout as in Fig. 1a (grey, no expression; yellow–red, relative expression). c, Heat maps show the subclasses of OPCs, astrocytes and microglia. The genes were organized into clusters. Top chart, gestational weeks. d, Immunostaining for new markers of NPCs (SFRP1) and excitatory neurons (RBFOX1) at GW10 in the PFC. Scale bars, 100 μm (left), 100 μm (middle) and 10 μm (right). n = 3 independent replicates per gestational week.

Extended Data Figure 3 Pie chart of the distribution of the 35 subclasses of the six cell types across gestational time.

Extended Data Figure 4 Relative expression of known markers of each cell type.

a, The bifurcation of gene expression along two branches is clustered hierarchically into six modules. Gene ontology analysis of each module reflected the processes controlling neuronal and glial fate commitment. In this heat map, columns are points in pseudo-time, rows are genes and the middle (NPCs) is the beginning of pseudo-time. One lineage goes from the middle of the heat map to the right (glial cells) while the other lineage goes to the left (excitatory neurons). n = 1,540 cells. b, The markers (EOMES, HES1 for NPCs; NEUROD1, NEUROD6, SLA for excitatory neurons; GFAP, S100B for astrocytes; OLIG2, PDGFRA for OPCs) were ordered by Monocle analysis in pseudo-time as in Fig. 1c; the shadow indicates the confidence interval around the fitted curve.

Extended Data Figure 5 Immunostaining of neural progenitor cells and microglia in the developing PFC.

a, Immunostaining for known markers (PAX6 and SOX2 for NPCs, NEUROD2 for excitatory neurons) at GW8, GW12, GW16, GW19 and GW23, showing the position of NPCs and excitatory neurons. Scale bars, 100 μm (left) and 25 μm (right). n = 3 for GW 19 and GW23, n = 4 for GW8, GW12 and GW16. b, Bar charts showing the proportion of PAX6+SOX2+ cells in SOX2+ cells in the ventricular zone, iSVZ and oSVZ in the PFC at GW8, GW12, GW16, GW19 and GW23 relative to the images in a. Data are mean ± s.e.m. c, Co-staining for AIF1 and CD45 to label microglia in the PFC at GW16. d, Immunostaining for the microglia marker CD45 in the PFC at GW23. Scale bar, 100 μm. n = 3 independent replicates per gestational week.

Extended Data Figure 6 Subclasses and Monocle analysis of neural progenitor cells in the developing PFC.

a, Heat map of differentially expressed genes of subclasses in NPCs. Expression of known genes in each type is shown on the right of each heat map panel with enriched gene ontology terms. The graph on the top shows the distribution of each subclass across gestational week. n = 288 cells. b, Visualization of nine major classes of NPCs using t-SNE (colour on the left, subtype of NPCs) with known marker expression (right and bottom: grey, no expression; yellow–red, relative expression). c, Heat map of differentially expressed genes between GW8 to GW10 and GW16 to GW19 in EOMES NPCs. The graph at the top shows the distribution of each subclass across gestational weeks. d, Immunostaining for different stage markers of GW10 (HMGA2) and GW16 (HOPX) in the PFC. Scale bars, 100 μm. n = 3 independent replicates per gestational week. e, Histogram showing the ratio of EOMES+ cells to all NPCs across gestational weeks quantified by RNA-seq data.

Extended Data Figure 7 Expression of markers of IPCs in the developing PFC.

a, Heat map of differentially expressed genes in EOMES+ NPCs at GW10 and GW16. Expression of known genes at each stage is shown to the right of each heat map panel with enriched gene ontology terms. n = 288 cells. b, Heat map of expression of cell-cycle genes in all NPCs and neurons. The pie chart at the bottom indicates the ratios of cell types at each cell-cycle stage. c, Gene expression of novel markers of IPCs across developmental time corresponding to human cortical neurogenesis. NPCs: GW8, 3 cells; GW9, 50 cells; GW10, 84 cells; GW12, 12 cells; GW13, 7 cells; GW16, 123 cells; GW19, 5 cells; GW23, 2 cells; GW26, 4 cells. d, The expression of novel markers of IPCs is shown using the same layout as Extended Data Fig. 2b (grey, no expression; yellow–red, relative expression). n = 288 NPCs.

Extended Data Figure 8 Subclasses of excitatory neurons in the developing PFC.

a, Heat map of differentially expressed genes in different subtypes of excitatory neurons. The bar chart at the top shows the gestational week. b, Principal component analysis and t-SNE were used to sort excitatory neurons into subgroups. Each dot represents a single cell. The colour shows the gestational week. c, GSEA shows genes related to key functions of excitatory neurons in the PFC across multiple developmental time points corresponding to human cortical neurogenesis. n = 1,057 excitatory neurons.

Extended Data Figure 9 Whole-cell patch-clamp recordings in the developing PFC.

a, Immunostaining after whole-cell patch-clamp recordings of the PFC at GW23. Scale bar, 100 μm. n = 3 independent replicates. b, Whole-cell patch-clamp recordings of the PFC at GW23. c, Quantification of Na+ current (top) and K+ current (bottom) of neurons located in upper layer, deep layer or subplate of GW23 PFC. n = 31 neurons from three independent replicates. Data are mean ± s.e.m. All P > 0.05, two-way ANOVA. d, Whole-cell patch-clamp recordings of the PFC neurons at GW26. e, Immunostaining after whole-cell patch-clamp recordings of the PFC neurons at GW26. Scale bar, 100 μm. f, Three-dimensional reconstructions of neurons are shown. Scale bar, 30 μm. Nine representative images are shown from n = 26 neurons from three independent replicates.

Extended Data Figure 10 Subclasses of interneurons and signal pathways regulating neurogenesis.

a, Heat map of illustrated subclasses of interneurons. The bar chart on the top shows the gestational week. b, The expression of known markers of interneurons mapped to the Monocle analysis (Fig. 4a) shows the pseudo-time course of interneurons during development (grey, no expression; yellow–red, relative expression). c, In situ hybridization of TTF1 shows the postion of interneuron progenitor cells in PFC at GW10. Scale bar, 100 μm. n = 3 independent replicates. d, Pattern of marker gene expression in interneuron progenitor cells mapped onto the cell cycle plot for all interneurons. Interneurons with high expression of TTF1, LHX6, DLX1 and DLX2 exhibit low expression of cell cycle genes. e, Quantification of specific markers for interneuron progenitor cells and interneurons of PFC at GW7 by reverse transcription–PCR analysis. n = 3 independent replicates. f, In situ hybridization of SST in PFC at GW10. Scale bar, 100 μm. g, Immunostaining of SST in PFC at GW10. Scale bar, 100 μm. n = 3 independent replicates. h, GSEA enrichment plot of the KEGG neurotrophin signalling pathway. n = 663 excitatory neurons; n = 485 interneurons. i, Mean expression of Notch signalling pathway genes in NPCs, excitatory neurons and interneurons at different stages of development. The bar chart at the top represents the ratio of expressed genes to all genes in the Notch pathway.

Supplementary information

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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. (XLSX 417 kb)

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Zhong, S., Zhang, S., Fan, X. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018). https://doi.org/10.1038/nature25980

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