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Developmental diversification of cortical inhibitory interneurons

Nature volume 555, pages 457–462 (2018)Cite this article

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Abstract

Diverse subsets of cortical interneurons have vital roles in higher-order brain functions. To investigate how this diversity is generated, here we used single-cell RNA sequencing to profile the transcriptomes of mouse cells collected along a developmental time course. Heterogeneity within mitotic progenitors in the ganglionic eminences is driven by a highly conserved maturation trajectory, alongside eminence-specific transcription factor expression that seeds the emergence of later diversity. Upon becoming postmitotic, progenitors diverge and differentiate into transcriptionally distinct states, including an interneuron precursor state. By integrating datasets across developmental time points, we identified shared sources of transcriptomic heterogeneity between adult interneurons and their precursors, and uncovered the embryonic emergence of cardinal interneuron subtypes. Our analysis revealed that the transcription factor Mef2c, which is linked to various neuropsychiatric and neurodevelopmental disorders, delineates early precursors of parvalbumin-expressing neurons, and is essential for their development. These findings shed new light on the molecular diversification of early inhibitory precursors, and identify gene modules that may influence the specification of human interneuron subtypes.

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Figure 1: Transcriptional landscape of single cells in the ganglionic eminences.
Figure 2: A common developmental program of gene expression functions in the mitotic progenitors of all three ganglionic eminences.
Figure 3: Postmitotic cells from all eminences pass through distinct precursor states.
Figure 4: Integrating developmental scRNA-seq datasets to link embryonic heterogeneity with adult interneuron subtypes.

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Acknowledgements

We thank members of the Fishell and Satija laboratories, and C. Desplan, for feedback and discussion; L. Harshman, B. Bracken and W. Stephenson for assistance with scRNA-seq experiments; and N. Habib for assistance with published datasets. This work was supported by National Institutes of Health (NIH) grants R01 NS074972 (G.F.), R01 NS081297 (G.F.), MH071679-12 (G.F. and R.S.), NIH DP2-HG-009623 (R.S.), European Molecular Biology Organization ALTF 1295-2012 (C.M.), Deutsche Forschungsgemeinschaft Postdoctoral Fellow (C.H.), NIH F30MH114462 (R.C.B.), T32GM007308 (R.C.B.), NIH F31NS103398 (K.A.), and National Science Foundation DGE1342536 (A.B.). G.F. is also supported by a grant from the Simons Foundation (274578).

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Author notes

  1. Christian Mayer, Christoph Hafemeister and Rachel C. Bandler: These authors contributed equally to this work.

Authors and Affiliations

  1. NYU Neuroscience Institute, Langone Medical Center, New York, 10016, New York, USA

    Christian Mayer, Rachel C. Bandler, Robert Machold, Renata Batista Brito, Xavier Jaglin, Kathryn Allaway & Gord Fishell

  2. New York Genome Center, New York, 10013, New York, USA

    Christian Mayer, Christoph Hafemeister, Andrew Butler & Rahul Satija

  3. Department of Neurobiology, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Christian Mayer, Xavier Jaglin, Kathryn Allaway & Gord Fishell

  4. Broad Institute, Stanley Center for Psychiatric Research, Cambridge, 02142, Massachusetts, USA

    Christian Mayer & Gord Fishell

  5. Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, 10461, New York, USA

    Renata Batista Brito

  6. Center for Genomics and Systems Biology, New York University, New York, 10012, New York, USA

    Andrew Butler & Rahul Satija

  7. Center for Genomics and Systems Biology, New York University, PO Box, Saadiyat Island, 129188, Abu Dhabi, United Arab Emirates

    Gord Fishell

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Contributions

C.M., C.H., R.C.B., G.F. and R.S. conceived the research. C.M. and R.C.B. led experimental work, assisted by R.M., R.B.B., X.J., K.A. and supervised by G.F. C.H. led computational analysis, assisted by C.M. and A.B., and supervised by R.S. All authors participated in interpretation and writing the manuscript.

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Correspondence to Gord Fishell or Rahul Satija.

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

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Extended data figures and tables

Extended Data Figure 1 Ordering cells along a maturation trajectory.

a, Diffusion map analysis of eminence datasets suggests a pan-eminence developmental continuum. Each eminence was analysed independently, revealing nearly identical patterns. Cells are coloured according to the expression of canonical regulators. b, Using principal component analysis to reconstruct developmental maturation returns nearly identical results to the diffusion map analysis in Fig. 1. Principal component analysis was calculated for all eminences independently, and cells are coloured by their expression of canonical markers. c, Eigenvalues for the two dimensionality reduction methods. We observe a substantial eigenvalue drop-off after the initial components, demonstrating that the majority of the variance is captured in the first few dimensions. d, Single-cell heat map showing scaled expression levels of top genes that were correlated with cell cycle score. Cells on the x axis are sorted by cell cycle score. Negative scores correspond to cells in S phase, positive scores correspond to cells in G2/M phase. e, Scatter plot illustrating the relationship between maturation score and cell cycle score for all cells in the dataset. Each dot corresponds to a single cell. Early progenitors span a wide range of cell cycle states, whereas late cells do not express G2/M or S-phase specific genes and express postmitotic genes. f, Expression of canonical marker genes as a function of ‘pseudotime’, as calculated with Monocle213. Monocle2 pseudotime was strongly correlated with our maturation trajectory (both Pearson and Spearman R = 0.94). g, h, Diffusion map (g) and maturation trajectory (h) analysis of 1,099 single cells obtained from FlashTag animals, and sequenced using a custom version of the Smart-seq2 protocol (Supplementary Methods). Cells are coloured by their expression of canonical markers, which exhibit dynamics that are concurrent with the maturation trajectory learned from the Drop-seq data. i, j, Relationship between the maturation trajectory and cell cycle scores derived from the FlashTag datasets replicates our observations from Drop-seq. Therefore, our FlashTag maturation trajectory serves as complementary validation of our Drop-seq maturation trajectory, and exhibits strong association with biological time.

Extended Data Figure 2 Enrichment of differentially expressed genes in the MGE, CGE and LGE.

a, Schematic of embryonic brain sections at E13.5/E14.5. One sagittal section shows the MGE and LGE next to one another (right), whereas the other shows the CGE (left). b, ISH images from the Allen Brain Institute Developing Mouse Brain Atlas at E13.5 for genes that our analysis identified as being differentially expressed between the eminences. For each gene, ISH images are shown for the MGE, CGE and LGE. Image credit: Allen Institute. c, Temporal dynamics for differentially expressed genes in early mitotic cells. Curves represent local averaging of single-cell expression, as a function of progression along the maturation trajectory, for each eminence independently. Grey area indicates 95% confidence interval. Genes are selected from the differentially expressed genes in early mitotic cells (Fig. 2a). d, Gene expression dynamics in mitotic cells, based on local averaging of single-cell data, plotted along maturation score for selected developmentally regulated genes.

Extended Data Figure 3 Enrichment of dynamically expressed genes in the ventricular zone, subventricular zone and mantle zone.

a, Schematic of an embryonic brain section at E13.5/E14.5. The locations of the ventricular zone (VZ) and mantle zone (MZ) are indicated. b, Sagittal ISH images from the Allen Brain Institute Developing Mouse Brain Atlas at E13.5. Genes are ordered from lowest to highest maturation score rank. The trend overall shows that genes with peak expression at low maturation score tend to have higher expression in the ventricular zone, and as maturation score rank increases the expression pattern shifts to the subventricular zone and then to the mantle zone. Image credit, Allen Institute.

Extended Data Figure 4 Fate divergence occurs as cells become postmitotic.

a, Supervised analysis: PCA of full dataset, run using only branch-dependent genes. Cells are grouped based on the maturation-trajectory bin: the first five bins represent mitotic progenitors, the last four bins represent postmitotic cells which are coloured by branch ID. Mitotic cells fall within a homogeneous point cloud, with low variance on principal components 1 and 2, showing no evidence of fate bifurcation. b, To test whether our inability to detect fate bifurcations earlier in development was due to the lower sequencing depth of Drop-seq, we sequenced 400 Dlx6a-cre;RCEloxP negative ganglionic eminence cells (thereby enriching for mitotic progenitors), using a modified Smart-seq2 protocol. Diffusion map analysis of these cells returned only two significant principal components, with no evidence of further structure. These components reflect our previously defined maturation trajectory, with DMC1 separating mitotic cells (left). c, Rare mitotic cells expressing canonical branch markers do not segregate on the diffusion plot. d–f, Branching analysis on mitotic progenitors. We repeated the branch analysis, previously computed on postmitotic cells (Fig. 3a), on mitotic progenitors from all three ganglionic eminences. Although we did observe computational evidence of branching, this does not represent fate bifurcations as we observed in postmitotic cells. Instead, cells from different branches could largely be separated into ‘early’, ‘intermediate’ and ‘late’ regions of mitotic pseudotime, with one branch being largely defined by the expression of pro-neural cell cycle regulators (for example, Ascl1). As these genes peak at intermediate stages, our branching patterns could reflect either the aberrant assignment of intermediate cells to a new branch, or reflect the potential of multiple modes of cell division (namely, direct versus indirect neurogenesis) occurring in the ventricular zone and subventricular zone. g, Genetic fate-mapping using Lhx8-cre/cerulean demonstrates that MGE branch three precursors give rise to the entire breadth of cholinergic projection (globus pallidus and nucleus basalis) and interneuron (striatum) populations. The cumulative longitudinal use of a constitutive Cre driver also results in extensive labelling of cortical interneurons owing to transient expression within this population. Scale bar, 500 μm. Ctx, cortex; Str, striatum; LS, lateral septum; MS, medial septum; NP, nucleus basalis; GP, globus pallidus. h, Our Lhx6–GFP-negative dataset contains both mitotic and postmitotic cells from the CGE and diffusion map analysis shows our previously defined maturation trajectory. i, j, To isolate postmitotic cells, we calculated a maturation trajectory (i), and used the cell cycle scores to identify the transition point between mitotic and postmitotic cells (j) as with the eminence datasets in Fig. 1. k, To avoid the possibility of FACS false-negative MGE cells contaminating our Lhx6–GFP-negative dataset, we clustered the postmitotic cells from this dataset, and filtered out three rare clusters where Lhx6 mRNA expression was detected in more than 20% of cells (Supplementary Methods). l, m, We mapped postmitotic cells from the Lhx6–GFP-positive (l) and Lhx6–GFP-negative (m) datasets to the branches determined from the Drop-seq dataset (Supplementary Methods). Heat maps show scaled single-cell expression markers associated with each branch. n, Analogous to Fig. 3e, but also including the Lhx6–GFP-positive and Lhx6–GFP-negative datasets generated using 10x Genomics, as a validation of the original Drop-seq datasets that were performed on wild-type mice.

Extended Data Figure 5 Filtering of E18.5 and P10 10x datasets and mapping of E18.5 cortex and subcortex neurons to E13.5/E14.5 branches.

a, c, t-SNE visualization of Dlx6a-cre;RCEloxP positive E18.5 cortical cells (a) and Dlx6a-cre;RCEloxP positive P10 cortical cells (c). Although the Dlx6a-cre should mark only GABAergic eminence-derived cells, we identified rare populations that did not express canonical interneuron (IN) markers, probably representing false positives from FACS. b, d, Gene expression in these populations (E18.5 cells b, P10 cells d; heat map shows average expression in group) identifies rare contaminating populations of microglia (micro), astrocytes (astro), oligodendrocyte precursor cells (OPCs) and oligodendrocytes (oligo); smooth muscle cells (SMC), stem cells (SC), projection neurons (PN). For all downstream analyses, we considered only cells in the interneuron cluster. e, t-SNE visualization of 8,382 Dlx6a-cre;RCEloxP positive E18.5 cortical cells (same dataset as in Extended Data Fig. 5a, but after removing contaminating populations). Each E18.5 cell was mapped to one of six precursor states (branch 1, 2, and 3 for Lhx6–GFP-positive and Lhx6–GFP-negative datasets), using a correlation-based distance metric (Supplementary Methods). This enabled us to assign a putative eminence and branch of origin for each of the E18.5 cortical cells. f, As expected, the vast majority of Dlx6a-cre;RCEloxP positive E18.5 cortical cells map to the interneuron precursor state, and are split between MGE and CGE-derived precursors. By contrast, Dlx6a-cre;RCEloxP positive E18.5 cells from the subcortex primarily map to branches 2 and 3, consistent with our interpretation of these branches as precursor states for projection neurons; CX, cortex; SC, subcortex. g, h, The minority of Dlx6a-positive cortical cells mapping to precursor states 2 and 3 primarily co-express Meis2 (g) and Gad1 (h), probably representing a CGE-derived GABAergic population. These cells have been recently described as being present in the cortical white matter and probably represent projection neuron precursors21. i, j, Heat maps showing single-cell expression markers for the three different mapped branches of Dlx6a-cre;RCEloxP positive E18.5 cells from the cortex (i) and the subcortex (j).

Extended Data Figure 6 Clustering of adult visual cortical neurons into 14 major non-overlapping inhibitory interneuron subtypes.

a, Initial t-SNE visualization and graph-based clustering of 8,329 single cells individually isolated from P56 mouse visual cortex and sequenced with the Smart-Seq2 protocol. Data was downloaded from the publicly available resource hosted by the Allen Brain Atlas22 (Allen Cell Types Database, http://celltypes.brain-map.org/download (2015)). b, Of all cells, 3,432 GABAergic interneurons were easily identified by the expression of Gad1 (left) and the absence of Slc17a7 (right), and were selected for downstream analysis. c, t-SNE visualization and graph-based clustering of the 3,432 GABAergic cells reveals 14 clusters. d, e, The clusters revealed in c could be broadly grouped into cardinal types based on the expression of canonical markers. f, Single-cell heat map showing scaled expression values for the best transcriptomic markers in each cluster.

Extended Data Figure 7 Emergence of transcriptomically defined subtypes across development.

Differentially expressed genes between MGE and CGE derived subsets (left), that are conserved in both developmental and P56 cells. Each conserved gene is placed on the heat map when it is first observed to be differentially expressed during development, and the number of conserved differentially expressed genes grows over time. The same analysis is shown for Pvalb and Sst subsets (middle), and for Vip and Id2 subsets (right). This figure is identical to Fig. 4e, but with all gene names displayed.

Extended Data Figure 8 The integrated analysis agrees with an independent t-SNE analysis of each time point.

a, t-SNE visualizations of interneuron precursors from E13.5, E18.5 and P10, calculated independently for each time point. Cells are coloured as in Fig. 4b–d, based on their mapping to P56 datasets in integrated analysis. However, since the t-SNE was performed separately for each time point, we can assess how the integrated analysis agrees with an independent analysis of each time point. In each case, we can see that the cardinal type separation that we observe via integrated analysis (Fig. 4b–d) is consistent with an independent analysis of each dataset. Integrated analysis with the P56 dataset results in clearer separation, and enables us to map developmental precursors to adult subtypes. b, Expression of Gad1 and Meis2 in single-cell datasets. Cells expressing both genes are probably projection neuron precursors that have recently been described in the CGE21, but whose progeny is not captured in the mouse visual cortex dataset. Therefore, these cells are correctly mapped as unassigned.

Extended Data Figure 9 Transcriptional segregation into cortical interneuron subtypes at different developmental stages.

a, t-SNE visualization of all P10 cells mapping to a P56 subtype (as in right column of Fig. 4c, but cells are coloured by subtype instead of cardinal type). b, t-SNE visualization as in a, but zoomed in on each cardinal type independently. c, Single-cell heat maps showing the best transcriptomic markers marking each subtype, for the Sst (left), Vip (middle) and Id2 (right) cardinal types, within P10 cells. We did not observe any statistically significant markers subdividing Pvalb subtypes. d, t-SNE visualization of all E18.5 cells mapping to a P56 subtype (as in right column of Fig. 4c, but cells are coloured by subtype instead of cardinal type). e, t-SNE visualization as in d, but zoomed in on each cardinal type independently. f, Single-cell heat maps showing the best transcriptomic markers marking each subtype, for the Sst (left), Vip (middle) and Id2 (right) cardinal types, within E18.5 cells. We did not observe any statistically significant markers subdividing Pvalb subtypes.

Extended Data Figure 10 A subset of embryonic markers of cardinal type specification in mouse are conserved in adult human neurons.

a, Quantification of Pvalb-positive cortical interneurons across the different cortical layers of the control and Mef2c cortical knockout (Dlx6a-cre;Mef2cloxP/loxPRCE) animals. Mef2c cortical knockout results in a reduction in Pvalb density in all cortical layers except for layer 1. Error bars reflect s.e.m.; unpaired t-test; *P < 0.05, **P < 0.01, ***P < 0.001; n = 4 brains each for cortical knockout and control, 3–4 sections per brain. b–d, Scatter plot comparing average expression of 3,035 GABAergic single nuclei from post-mortem human neurons, after segregation into Pvalb and Sst (b), Vip and Id2 (c) and MGE and CGE inferred origins (d). Each dot represents the expression of a gene in human cells. Markers of transcriptomic cardinal types from our E13.5 and E18.5 datasets (from Fig. 4e) are shown in red or blue dots. Mouse embryonic markers that also differ by 1.5-fold in human have gene names annotated on the plot.

Supplementary information

Life Sciences Reporting Summary (PDF 72 kb)

Supplementary Table 1

This file contains a summary of scRNA-seq experiments. (XLSX 9 kb)

Supplementary Table 2

This file contains differentially expressed TFs across eminences within mitotic cells. (XLSX 11 kb)

Supplementary Table 3

This file contains temporally regulated genes in mitotic GE cells and and their MT bin of maximal expression. (XLSX 55 kb)

Supplementary Table 4

This file contains differentially expressed genes between postmitotic branches. (XLSX 105 kb)

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Mayer, C., Hafemeister, C., Bandler, R. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018). https://doi.org/10.1038/nature25999

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