Abstract
One of the hallmarks of the cerebral cortex is the extreme diversity of interneurons1,2,3. The two largest subtypes of cortical interneurons, parvalbumin- and somatostatin-positive cells, are morphologically and functionally distinct in adulthood but arise from common lineages within the medial ganglionic eminence4,5,6,7,8,9,10,11. This makes them an attractive model for studying the generation of cell diversity. Here we examine how developmental changes in transcription and chromatin structure enable these cells to acquire distinct identities in the mouse cortex. Generic interneuron features are first detected upon cell cycle exit through the opening of chromatin at distal elements. By constructing cell-type-specific gene regulatory networks, we observed that parvalbumin- and somatostatin-positive cells initiate distinct programs upon settling within the cortex. We used these networks to model the differential transcriptional requirement of a shared regulator, Mef2c, and confirmed the accuracy of our predictions through experimental loss-of-function experiments. We therefore reveal how a common molecular program diverges to enable these neuronal subtypes to acquire highly specialized properties by adulthood. Our methods provide a framework for examining the emergence of cellular diversity, as well as for quantifying and predicting the effect of candidate genes on cell-type-specific development.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data Availability
All sequencing data produced for this study are available at the Gene Expression Omnibus (GEO) at accession number GSE165233.
Code Availability
The ChromA package, used for peak calling in all scATAC–seq datasets, is available at https://github.com/marianogabitto/ChromA. The CellRanger and CellRanger ATAC pipelines, used for preprocessing of scRNA-seq and scATAC–seq libraries, respectively, are available at https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest and https://support.10xgenomics.com/single-cell-atac/software/downloads/latest. The snapATAC package, used for downstream analysis of scATAC–seq datasets, is available at https://github.com/r3fang/SnapATAC. The Seurat package, used for scRNA-seq analysis and integration of scRNA-seq and scATAC–seq datasets, is available at https://github.com/satijalab/seurat. Scripts for analysis are available at https://github.com/marianogabitto/ChromatinDynamics2020-Analysis.
References
Ascoli, G. A. et al. Petilla terminology: Nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).
Defelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013).
Kepecs, A. & Fishell, G. Interneuron cell types are fit to function. Nature 505, 318–326 (2014).
Mayer, C. et al. Clonally related forebrain interneurons disperse broadly across both functional areas and structural boundaries. Neuron 87, 989–998 (2015).
Harwell, C. C. et al. Wide dispersion and diversity of clonally related inhibitory interneurons. Neuron 87, 999–1007 (2015).
Freund, T. F. Interneuron diversity series: rhythm and mood in perisomatic inhibition. Trends Neurosci. 26, 489–495 (2003).
Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).
Mayer, C. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018).
Mi, D. et al. Early emergence of cortical interneuron diversity in the mouse embryo. Science 360, 81–85 (2018).
Wonders, C. P. et al. A spatial bias for the origins of interneuron subgroups within the medial ganglionic eminence. Dev. Biol. 314, 127–136 (2008).
Batista-Brito, R. & Fishell, G. The developmental integration of cortical interneurons into a functional network. Curr. Top. Dev. Biol. 87, 81–118 (2009).
Wang, Y. et al. Dlx5 and Dlx6 regulate the development of parvalbumin-expressing cortical interneurons. J. Neurosci. 30, 5334–5345 (2010).
Pai, E. L.-L. et al. Mafb and c-Maf have prenatal compensatory and postnatal antagonistic roles in cortical interneuron fate and function. Cell Rep. 26, 1157–1173.e5 (2019).
Pai, E. L.-L. et al. Maf and Mafb control mouse pallial interneuron fate and maturation through neuropsychiatric disease gene regulation. eLife 9, e54903 (2020).
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).
Sinnamon, J. R. et al. The accessible chromatin landscape of the murine hippocampus at single-cell resolution. Genome Res. 29, 857–869 (2019).
Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).
Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).
Miyoshi, G. & Fishell, G. GABAergic interneuron lineages selectively sort into specific cortical layers during early postnatal development. Cereb. Cortex 21, 845–852 (2011).
Batista-Brito, R. et al. The cell-intrinsic requirement of Sox6 for cortical interneuron development. Neuron 63, 466–481 (2009).
Azim, E., Jabaudon, D., Fame, R. M. & MacKlis, J. D. SOX6 controls dorsal progenitor identity and interneuron diversity during neocortical development. Nat. Neurosci. 12, 1238–1247 (2009).
Au, E. et al. A modular gain-of-function approach to generate cortical interneuron subtypes from ES cells. Neuron 80, 1145–1158 (2013).
Castro, D. M., de Veaux, N. R., Miraldi, E. R. & Bonneau, R. Multi-study inference of regulatory networks for more accurate models of gene regulation. PLoS Comput. Biol. 15, 1–22 (2019).
Yadav, T., Quivy, J.-P. & Almouzni, G. Chromatin plasticity: a versatile landscape that underlies cell fate and identity. Science 361, 1332–1336 (2018).
Ladstätter, S. & Tachibana, K. Genomic insights into chromatin reprogramming to totipotency in embryos. J. Cell Biol. 218, 70–82 (2019).
Harrington, A. J. et al. MEF2C regulates cortical inhibitory and excitatory synapses and behaviors relevant to neurodevelopmental disorders. eLife 5, e20059 (2016).
Cosgrove, D. et al. Genes influenced by MEF2C contribute to neurodevelopmental disease via gene expression changes that affect multiple types of cortical excitatory neurons. Hum. Mol. Genet. 30, 961–970 (2021).
Monory, K. et al. The endocannabinoid system controls key epileptogenic circuits in the hippocampus. Neuron 51, 455–466 (2006).
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).
Mo, A. et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron 86, 1369–1384 (2015).
Vong, L. H., Ragusa, M. J. & Schwarz, J. J. Generation of conditional Mef2cloxP/loxP mice for temporal- and tissue-specific analyses. Genesis 43, 43–48 (2005).
Fogarty, M. et al. Spatial genetic patterning of the embryonic neuroepithelium generates GABAergic interneuron diversity in the adult cortex. J. Neurosci. 27, 10935–10946 (2007).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Gabitto, M. I. et al. Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling. Nat. Commun. 11, 1–10 (2020).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Stroud, H. et al. An activity-mediated transition in transcription in early postnatal neurons. Neuron 107, 874–890.e8 (2020).
Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442.e17 (2018).
Zhu, Q., Liu, N., Orkin, S. H. & Yuan, G.-C. CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis. Genome Biol. 20, 192 (2019).
Pliner, H. et al. Chromatin accessibility dynamics of myogenesis at single cell resolution. Preprint at https://doi.org/10.1101/155473 (2017).
Jackson, C. & Skok Gibbs, C. Inferelator example data and scripts. https://doi.org/10.5281/zenodo.3355524 (2019).
Acknowledgements
We thank J. Dimidschstein, E. Favuzzi, D. Ginty, E. Macosko and E. Miraldi for feedback and discussion; N. Yusuf and M. Fernandez-Otero for technical assistance; R. Batista-Brito for providing mice for the Mef2c loss-of-function experiments; and C. Davis for sharing the CUT&RUN protocol and reagents. G.F. and K.C.A. acknowledge support from the following sources: National Institutes of Health (NIH) 1UG3MH120096-01 (G.F.), NIH 5R01MH071679-15 (G.F.) and NIH F31NS103398 (K.C.A.). R.B. and M.I.G. acknowledge support from the following sources: NIH R01DK103358, Simons Foundation, NSF-IOS 1546218, NIH R35GM122515, NSF-CBET 1728858 and NIH R01AI130945.
Author information
Authors and Affiliations
Contributions
K.C.A., M.I.G. and O.W. contributed equally to this work and are featured in alphabetical order in the author list. K.C.A., M.I.G., O.W., R.B. and G.F. conceived the original project. K.C.A. led experimental work, with assistance from O.W., C.-Y.W., R.C.B. and S.J.W., and supervision from G.F. M.I.G. led computational analysis, with assistance from G.S. and K.C.A., and supervision from R.B. K.C.A., M.I.G., G.S., R.B. and G.F. interpreted the data and wrote the manuscript, with input from O.W. and C.-Y.W.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
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 Fig. 1 Quality control of embryonic scRNA-seq samples and mitotic versus postmitotic discrimination.
a, Number of cells in Dlx6a− and Dlx6a+ scRNA-seq datasets collected from E13 MGE in Dlx6a-Cre;Ai9 mice and multiome dataset in E13 MGE wild type mice. b, Mean reads per cell in Dlx6a−, Dlx6a+ and multiome scRNA-seq datasets. c, Median genes detected per cell in Dlx6a−, Dlx6a+ and multiome scRNA-seq datasets. d, Fraction of cells scored to be in G2/M or S phase of the cycle cycle at each maturation score. Blue line indicates cells from the Dlx6− dataset, red line indicates cells from the Dlx6+ dataset. e, Diffusion map of E13 Dlx6a− and Dlx6a+ MGE scRNA-seq data color-coded by assignment to a mitotic (red) or postmitotic (blue) state. f, Percentage of cycling cells as a function of the position along the maturation trajectory color-coded by assignment to a mitotic (red) or postmitotic (blue) state in E13 Dlx6a− and Dlx6a+ MGE scRNA-seq datasets. g, Diffusion map of E13 multiome MGE scRNA-seq data color-coded by assignment to a mitotic (red) or postmitotic (blue) state. h, Percentage of cycling cells as a function of the position along the maturation trajectory color-coded by assignment to a mitotic (red) or postmitotic (blue) state in E13 multiome MGE scRNA-seq dataset. i, Line plots indicating the promoter accessibility (blue) and gene expression (red) for six developmentally-regulated genes.
Extended Data Fig. 2 Developmental characterization of embryonic E13 MGE cells surveyed using scRNA-seq, scATAC-seq, and multiomic methods.
a, Analysis of Dlx6a−, Dlx6a+ and multiome scRNA-seq datasets collected from E13 MGE in Dlx6a−Cre;Ai9 mice and multiome dataset in E13 MGE wild type mice. b, Analysis of Dlx6a−, Dlx6a+ and multiome scATAC-seq datasets collected from E13 MGE in Dlx6a−Cre;Ai9 mice and multiome dataset in E13 MGE wild type mice. i, Dlx6+/− FAC-sorted and multiome cells. ii, Unbiased cluster annotation. iii, Dlx6+ and Dlx6− annotation. iv, Cell cycling phase annotation. v, Pseudotime annotation. vi, Mitotic and postmitotic cell annotation. In ii-vi, Annotations are performed on scRNA-seq datasets and transferred to scATAC-seq datasets through the multiome dataset. scRNA- and scATAC-seq low dimensional representation reflects UMAP embedding. vii, Average gene expression and promoter accessibility for unbiased clusters.
Extended Data Fig. 3 Orthogonal maturation trajectory methods reveal branching fates in postmitotic MGE cells at E13.
a, Palantir Analysis of Analysis of Dlx6a−, Dlx6a+ and multiome scRNA-seq postmitotic cells. b, UMAP Analysis of Analysis of Dlx6a−, Dlx6a+ and multiome scRNA-seq postmitotic cells. c, Diffusion Maps Analysis of Analysis of Dlx6a−, Dlx6a+ and multiome scRNA-seq postmitotic cells. For a, b, c:i, Unbiased clustering annotation. ii, Branch trajectories color-coded by slingshot pseudotime. iii, Gene expression for mitotic marker (Fabp7) and branch-specific marker (Zic1, Meis2, Maf). iv, For Palantir, cells are color coded by Palatir Pseudotime or Differentiation Potential. d, Confusion matrix reveals agreement of branch labels between different trajectory methods. e, TFs with binding motif highly enriched in a branch specific manner. f, Average gene expression across branches for TFs in e.
Extended Data Fig. 4 Chromatin accessibility precedes gene expression in branch 1- specific genes.
a, Heat map depicting gene expression, Promoter and distal elements accessibility throughout branch 1 pseudotime for loci +/− 500kb around six branch 1 - specific genes. Distal elements are selected based on the relevance for classifying branch 1 cells. For each gene, row 1 shows gene expression, row 2 promoter accessibility, row 3 aggregated accessibility and the remaining rows are distal branch 1 classifying elements. Each trace has been smoothed using the lowess function in R. b, Gene expression, Promoter and Aggregated accessibility throughout branch 1 pseudotime for 10 branch 1 - specific genes. c, Only postmitotic cells are classified as branch neurons by supervised classification methods. c−i) Classification of E13 cells into mitotic or postmitotic cells based on cell cycle RNA score. c−ii) Classification of E13 cells into mitotic or branch 1,2,3 lineages based on chromatin accessibility.
Extended Data Fig. 5 Analysis of MGE-derived cortical interneuron scRNA-seq datasets from E18 through P28.
a−d, UMAP showing individual scRNA-seq datasets from Dlx6a+ labeled cortical neurons, subsetted for PV+ and SST+ interneurons, collected at E18 (a), P2 (b), P10 (c), and P28 (d). Left UMAP in each panel is color-coded by cluster identity (individually determined for each dataset). Right UMAP in each panel is color-coded by cardinal class. Bottom right UMAP in (c) and (d) is color-coded by cortical region of origin (anterior lateral motor cortex - ALM, primary visual cortex - V1). In (d), the right-most boxes show each cluster linked to its subtype identity, determined by expression of marker genes. e, Integration of E18, P2, P10, and P28 scRNA-seq datasets using Seurat CCA. Left UMAP is color-coded by timepoint, right is color-coded by cardinal class. f, Prediction score indicating the confidence of label transfer between each timepoint. Cluster labels were transferred from P2 to E18 (top), from P10 to P2 (middle) and from P28 and P10 (bottom). Label transfer prediction scores were lowest between P10 and P2 timepoints.
Extended Data Fig. 6 Analysis of MGE-derived interneuron scATAC-seq datasets from E18 through P28.
a−d, UMAP showing individual scATAC-seq datasets from Dlx6a+ labeled cortical neurons, subsetted for PV+ and SST+ interneurons, collected at E18 (a), P2 (b), P10 (c), and P28 (d). Left UMAP in each panel is color-coded by cluster identity (individually determined for each dataset). Top right of each panel shows gene body accessibility of Gad2 (interneuron marker gene), Lhx6 (MGE-derived interneuron marker gene), Tac1 or PV (PV cIN marker gene), and Sst (SST cIN marker gene). Bottom right UMAP in each panel is color-coded by cardinal class, determined by accessibility of cardinal-class specific loci (identified at P28) and confirmed by marker gene accessibility. See methods for more information on cardinal class assignment in ATAC-seq data. Additional bottom right UMAP in (c) and (d) is color-coded by cortical region of origin (anterior lateral motor cortex - ALM, primary visual cortex - V1). e, Integration of E18, P2, P10, and P28 scATAC-seq datasets using Seurat CCA. Left UMAP is color-coded by timepoint, right is color-coded by cardinal class. f, Prediction score indicating the confidence of label transfer between each timepoint. Cluster labels were transferred from P2 to E18 (top), from P10 to P2 (middle) and from P28 and P10 (bottom). Label transfer prediction scores progressively increased over developmental time.
Extended Data Fig. 7 Integration of MGE-derived interneuron scRNA-and scATAC-seq datasets.
a−d, UMAPs showing scRNA- and scATAC-seq datasets integrated using Seurat CCA at E18 (a), P2 (b), P10 (c), and P28 (d). Top panel shows UMAP color-coded by RNA-seq cluster identity - i.e., the labels used for label transfer and calculation of prediction scores in Figure 2a–d. Bottom panel UMAP is color-coded by cardinal class identity. e−h, UMAPs showing scRNA- and scATAC-seq datasets integrated using CONOS E18 (e), P2 (f), P10 (g), and P28 (h). Top panel shows UMAP color-coded by dataset of origin (RNA or ATAC). Bottom panel UMAP is color-coded by cardinal class identity. Cells from RNA and ATAC datasets integrated relatively well at later developmental timepoints, but were mostly segregated at earlier timepoints (E18, P2).
Extended Data Fig. 8 Distally located loci open in specific cardinal classes earlier in development than proximal elements.
a, Average signal (top) and signal at each locus (bottom) for peaks specifically called for PV cells at P28 that are within promoters or gene bodies (i.e., proximal elements). Promoter regions were defined as TSS+2 kb upstream. Signal within PV and SST cells at P28, P10, P2, and E18 are shown over a window 5 kb up- and down-stream of peak center (compare to Figure 2f, which zooms in to a 1 kb +/− window). b, Average signal (top) and signal at each locus (bottom) for peaks specifically called for PV cells at P28 that are distally located at each timepoint. c, Average signal (top) and signal at each locus (bottom) for peaks specifically called for SST cells at P28 that are within promoters or gene bodies (proximal) at each timepoint. d, Average signal (top) and signal at each locus (bottom) for peaks specifically called for SST cells at P28 that are distally located at each timepoint. e, Top five enriched motifs for P28 PV-specific proximal peaks. f, Top five enriched motifs for P28 PV-specific distal peaks. g, Top five enriched motifs for P28 SST-specific proximal peaks. h, Top five enriched motifs for P28 SST-specific distal peaks.
Extended Data Fig. 9 MEF2C CUT&RUN in PV and SST interneurons at P28.
a, Number of peaks called in each CUT&RUN replicate from PV (blue) and SST (red) cells, and the number of peaks present in both replicates from each cell type (intersect). b, Percentage of peaks containing a canonical Mef2c motif in each replicate. c, Significance (-log p-value) of enrichment of Mef2c motif in each replicate. d, The distribution of CUT&RUN peaks that were also present in P28 ATAC peak sets containing MEF2C motifs. For this analysis, the CUT&RUN replicate intersect peak sets of PV and SST were subsetted for those peaks that were also present in the ATAC peaks called for each cell type at P28 and found to contain a canonical MEF2C motif. Of those, they were categorized for those that were present in both PV and SST ATAC peak sets or unique to one cell type. e, Compares peaks identified in CUT&RUN (orange) or jointly in CUT & RUN and ATAC-seq analyses (green). ATAC-seq peaks used in this analysis were those identified in each cell type at P28 regardless of presence or absence of a canonical Mef2c motif. Compare with Fig. 2j which performs the same analysis but only includes ATAC peaks with a Mef2c motif. f, g, HOMER results for de novo motif discovery in replicate intersect peak sets for PV and SST cINs. h, Genomic location of each CUT&RUN peak in replicate intersect peak sets for PV and SST cINs. i, Integrative Genomics Viewer (IGV) snapshots showing bigwig files for each CUT&RUN replicate and associated peaks called in both replicates (intersect) for PV and SST cINs. Genomic loci shows are examples of genes with nearby peaks for commonly expressed genes (Rbfox1, Grin2a) and PV-enriched genes (Erbb4, Pthlh, Plxcd3). j and k, Gene Ontology (GO) term analysis. Each CUT&peak was associated with the nearest gene TSS. These lists of genes for PV and SST cells were then used as input for GO term analysis, revealing an enrichment of genes associated with synapse development.
Extended Data Fig. 10 Single-cell RNA- and ATAC-seq analysis of Mef2c cKO interneurons compared to wild-type (WT) cells at P2.
a, UMAP of P2 WT snRNA-seq and P2 Mef2c cKO snRNA-seq data integrated using Seurat and color-coded by cluster identity. WT dataset here was prepared using single nuclei to match cKO rather than the whole cell dataset (see Fig. 4). b, UMAP in (a) segregated according to timepoint and color-coded by cell type. c, Cluster composition delineated by (i) marker gene expression (ii) cell type (iii) cell number. Compare a–c here with Fig. 4 a–c — this figure contains single nucleus data for both WT and cKO while Fig 4 WT data is whole cell. d, UMAP of Mef2c cKO and WT cells color-coded by cluster. e, UMAP of Mef2c cKO and WT cells color-coded by genotype. f, UMAP of Mef2c cKO and WT cells color-coded by cardinal class identity. Mef2c cKO identity was determined by the accessibility of marker genes (see d) and assignment of clusters to the appropriate cardinal class. g, Gene body accessibility of SST and PV cIN marker genes (SST: Sst, Grin3a, Elfn1, Cacng3, Grm1, Satb1, Tmem91. PV: Tac1, Erbb4). h, Pie chart representation of scATAC-seq data showing the total number of peaks in E18 WT, P2 WT, and P2 Mef2c cKO, subdivided into peaks that are PV or SST cell specific or shared across both cell types. Compare to Figure 4d, which shows similar pie charts but only for peaks with Mef2c motifs.
Supplementary information
Supplementary Information
This file contains a full guide for Supplementary Tables 1–4 (Supplementary Tables supplied as separate Excel files).
Supplementary Table 1
Quality control metrics and statistics for scATAC- and scRNA-seq collected for this study – see Supplementary Information document for full description.
Supplementary Table 2
Genomic coordinates of distal elements associated branch 1 genes in E13 postmitotic precursors – see Supplementary Information document for full description.
Supplementary Table 3
Motif enrichment analysis of scATAC-seq data from PV and SST cells across development – see Supplementary Information document for full description.
Supplementary Table 4
Predicted direct and second-order Mef2c targets and DE genes from Mef2c LOF experiments – see Supplementary Information document for full description.
Rights and permissions
About this article
Cite this article
Allaway, K.C., Gabitto, M.I., Wapinski, O. et al. Genetic and epigenetic coordination of cortical interneuron development. Nature 597, 693–697 (2021). https://doi.org/10.1038/s41586-021-03933-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-021-03933-1
This article is cited by
-
PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Genome Biology (2024)
-
Human neuronal maturation comes of age: cellular mechanisms and species differences
Nature Reviews Neuroscience (2024)
-
A Review of the Application of Spatial Transcriptomics in Neuroscience
Interdisciplinary Sciences: Computational Life Sciences (2024)
-
Range of chromatin accessibility configurations are permissive of GABAergic fate acquisition in developing mouse brain
BMC Genomics (2023)
-
In the developing cerebral cortex: axonogenesis, synapse formation, and synaptic plasticity are regulated by SATB2 target genes
Pediatric Research (2023)
Comments
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.
