neurodevelopment earlyemergence of cortical … › content › sci › 360 › 6384 ›...

NEURODEVELOPMENT

Early emergence of corticalinterneuron diversity inthe mouse embryoDa Mi,1,2* Zhen Li,3* Lynette Lim,1,2 Mingfeng Li,3 Monika Moissidis,1,2 Yifei Yang,4

Tianliuyun Gao,3 Tim Xiaoming Hu,5,6 Thomas Pratt,4 David J. Price,4

Nenad Sestan,3† Oscar Marín1,2†

GABAergic interneurons (GABA, g-aminobutyric acid) regulate neural-circuit activity in themammalian cerebral cortex. These cortical interneurons are structurally and functionallydiverse. Here, we use single-cell transcriptomics to study the origins of this diversity in themouse. We identify distinct types of progenitor cells and newborn neurons in the ganglioniceminences, the embryonic proliferative regions that give rise to cortical interneurons. Theseembryonic precursors show temporally and spatially restricted transcriptional patternsthat lead to different classes of interneurons in the adult cerebral cortex. Our findingssuggest that shortly after the interneurons become postmitotic, their diversity is alreadypatent in their diverse transcriptional programs, which subsequently guide furtherdifferentiation in the developing cortex.

The mammalian cerebral cortex containsmore than two dozenGABAergic cell types(GABA, g-aminobutyric acid) with particu-larmorphological, electrophysiological, andmolecular characteristics (1–3). Interneuron

diversity has evolved to increase the repertoire ofcortical computationalmotifs through a divisionof labor that allows individual classes of inter-neurons to control information flow in corticalcircuits (4–6). Although a picture about corticalinterneuron cell types is emerging (7, 8), themechanisms that generate interneuron diversityremain controversial. One model proposes thatinterneurons acquire the potential to differenti-ate into a distinct subtype at the level of progen-itors or shortly after becoming postmitotic, beforethey migrate; the competing model postulatesthat interneuron identity is established relativelylate in development, after they havemigrated totheir final location, through interactionswith thecortical environment (9).To study cell diversity in the germinal regions

of cortical interneurons (10), we dissected tissuefrom three regions in themouse subpallium, thedorsal and ventral medial ganglionic eminence(dMGE and vMGE, respectively) and the caudalganglionic eminence (CGE), across two stagesthat coincide with the peak of neurogenesis for

cortical interneurons [embryonic (E) days 12.5and E14.5] (11) (Fig. 1A). We prepared single-cellsuspensions and sequenced the transcriptomeof individual cells, which, following quality con-trol (fig. S1, A to E), led to a final data set of 2003cells (fig. S1F), covering on average of about 3200genes per cell. We performed regression analysison these cells to remove the influence of cell cycle–dependent genes in cell type identification (fig. S1G).We used principal components analysis (PCA)

to identify the most prominent sources of vari-ation. We found that developmental stage andanatomical source contribute to cell segregation(Fig. 1, B and C, and figs. S2 and S3). To distin-guish between dividing and postmitotic cells, weconducted random forest (RF) feature selectionand classification, starting with a list of estab-lished genes to sort cells into these categories(Fig. 1D). Subsequently, we reduced the dimen-sionality of our data using t-SNE (t-distributedstochastic neighbor embedding) to visualize thesegregation of progenitor cells from neurons(figs. S4A and S5A). These analyses revealed geneexpression patterns that distinguish progenitorcells and neurons at each developmental stage(figs. S4B and S5B).We took a semisupervised clustering approach

to explore variation across all progenitor cells andidentified progenitor clusters with characteristicregional and developmental patterns (fig. S6, Ato D). This analysis revealed a prominent tempo-ral segregation of progenitor clusters (fig. S6, Band D), which suggests that progenitor cells inthe ganglionic eminences (GE)may have a rapidturnover during embryonic development. To iden-tify distinctive features of E12.5 andE14.5 progen-itor cells, we further investigated progenitor celldiversity at each stage individually.We first usedRF feature selection and classification, startingwith a list of established genes to distinguish be-tween ventricular zone (VZ) radial glial cells and

subventricular zone (SVZ) intermediate progen-itors (12, 13) (fig. S7A). We then carried out semi-supervised clustering and distinguished VZ andSVZ progenitor clusters at both developmentalstages (Fig. 2A and fig. S7B), independent of theircell cycle state (fig. S8), and found characteris-tic patterns of gene expression (fig. S9). Cross-validation using MetaNeighbor (14) confirmedcluster robustness and identity (fig. S10A).Manyprogenitor clusters found at E12.5 did not seemto have a direct transcriptional equivalent atE14.5 (fig. S10B), which reinforces the notionthat the GE contains highly dynamic pools ofprogenitor cells during development.Analysis of progenitor cell clusters confirmed

that radial glial cells and intermediate progen-itors have distinct identities across different re-gions of the subpallium, with characteristic andoften complementary expression of transcriptionfactors (e.g., Nkx2-1 and Pax6 in VZ, Lhx6 andFoxp2 in SVZ) (Fig. 2B and figs. S11 and S12). Al-though the molecular diversity of VZ cells wasmore limited than anticipated (15, 16), this anal-ysis revealed diversity among SVZ progenitors(Fig. 2B and figs. S11 to S14). Thus, based ontranscriptomic signatures, the diversification ofprogenitor cells in the GE seems to emerge pri-marily within the highly neurogenic SVZ.We next turned our attention to the neurons

that are being generated in the GE during thistemporalwindowof highprogenitor cell diversity.TheMGE and CGE generate different groups ofcortical interneurons (17–19). Most parvalbumin(PV)–expressingandsomatostatin (SST)–expressinginterneurons are born in the MGE, whereas theCGE is the origin of vasoactive intestinal peptide(VIP)–expressing interneurons andneurogliaform(NDNF+) cells (20). We took a completely unsu-pervised approach to explore the emergence ofneuronal diversity in the GE. Unbiased cluster-ing of all neurons identified 13 groups of newbornneuronswith distinctive gene expression profiles,aswell as specific temporal and regional identities(figs. S15, A toD, and S16). This analysis revealedthat regional identity segregates more clearlyamong E14.5 neuronal clusters (fig. S15C), whichsuggests that neurons become more transcrip-tionally heterogeneous over time. Similar resultswere obtainedwhenneuronal clusterswere iden-tified for both stages separately (fig. S17).GO (Gene Ontology) enrichment analysis re-

vealed different states ofmaturation across neu-ronal clusters (fig. S15E), which suggests thatsome aspects of this diversity might be linkedto the differentiation of newborn neurons andnot cell identity. Analysis of the expression ofregion- and cell type–specific genes revealedthe emerging signature of the main groups ofcortical interneurons (fig. S15F). For example,clusters primarily populated by MGE-derivedcells can be further segregated into those withfeatures of SST+ interneurons (N3 and N4) andthose without (N1, N2, and N9), which presum-ably include neurons that will differentiate intoPV+ interneurons. The profile of emerging CGE-specific interneuron classes, such as those char-acterized by the expression ofMeis2 (21), is also

RESEARCH

Mi et al., Science 360, 81–85 (2018) 6 April 2018 1 of 5

1Centre for Developmental Neurobiology, Institute ofPsychiatry, Psychology, and Neuroscience, King’s CollegeLondon, London SE1 1UL, UK. 2Medical Research CouncilCentre for Neurodevelopmental Disorders, King’s CollegeLondon, London SE1 1UL, UK. 3Department of Neuroscienceand Kavli Institute for Neuroscience, Yale School of Medicine,New Haven, CT 06510, USA. 4Biomedical Sciences,University of Edinburgh, Edinburgh EH8 9XD, UK.5Department of Biomedical Informatics, Harvard MedicalSchool, Boston, MA 02446, USA. 6Department of Physiology,Anatomy, and Genetics, University of Oxford, Oxford, UK.*These authors contributed equally to this work.†Corresponding author. Email: [email protected] (O.M.);[email protected] (N.S.)

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delineated at this stage. This analysis also re-vealed that the CGE gives rise to neurons withmolecular profile of SST+ interneurons (N13),which reinforces the view that this anatomicalregion contains a molecularly heterogeneouspool of progenitor cells (15).The adultmouse cerebral cortex containsmore

than 20 distinct classes of interneuronswith char-acteristic transcriptional profiles (7, 8).We askedwhether any of these classes of interneuronswouldbe identifiable shortly after becoming postmitoticin the GE. To this end, we used a publicly availa-ble single-cell RNA sequencing (RNA-seq) dataset of 761 adult GABAergic interneurons fromthe adult mouse visual cortex (8) and identifiedhighly variable genes in both adult and embry-onic data sets. We employed the resulting dataset to identify the features that best representeach of the 23 interneuron cell types found inthe adult mouse cortex (8). We then carried outRF feature selection and classification based

on those features to assign the identity of adultinterneurons into distinct cell types. We wereunable to identify all cell types originally de-scribed in the adult data set (8), which suggestsa difference in transcriptomic and cell-type di-versity between embryo and adult.We thenusedthe identified adult interneuron cell types toannotate the embryonic data set using the RFclassificationworkflow and found six prospectiveinterneuron subtypes among embryonic neurons(Fig. 3A and fig. S18). Cross–data set validationbetween the emerging embryonic subtypes andadult interneurons confirmed the robustness ofthese annotations (fig. S19).We used a second, independent approach to

assign embryonic neurons to adult interneuronsubtypes. In brief, we conducted canonical cor-relation analysis (CCA) to identify the sourcesof variation that are shared between embryonicand adult neurons. To this end, we first reducedthe dimensionality of both data sets onto the

same two-dimensional space using t-SNE, whichallowed the identification of 11 clusters of adultinterneurons based on the expression of variablegenes shared between both data sets (Fig. 3B).These groups correspond to anatomically andelectrophysiologically defined classes of corti-cal interneurons, including several types of PV+

basket cells, SST+Martinotti and non-Martinotticells, VIP+ basket and bipolar interneurons, andneurogliaform cells (8, 22). We then assignedprospective identities to embryonic neuronsbased on transcriptional similarity with adultinterneurons. This analysis provided evidencefor early cell type differentiation: All 11 classesof cortical interneurons were identified amongembryonic neurons (Fig. 3C), which exhibit char-acteristic patterns of gene expression (Fig. 3Dand fig. S20) and robustness in cross-validationanalyses (fig. S21). Comparison between the twoindependent approaches identified eight con-served interneuron subtypes among the assigned


Fig. 1. Major sources of transcriptionalheterogeneity among single cells frommouse MGE and CGE. (A) Schematicillustrating sample collection, sequencing,and single-cell RNA-seq analysis workflow.Single cells from E12.5 and E14.5 dMGE,vMGE, and CGE were isolated andsubjected to cDNA synthesis using aFluidigm C1 system and RNA-seq. (B andC) Visualization of stage and region oforigin variation in single cells using PCA.(D) RF classification of cells into progenitoror neuronal identity. The heat mapillustrates expression of genes selectedby RF analysis that best representprogenitor or neuronal identity. Coloredbars above the heat map indicate cellidentity, stage, and region of origin.

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embryonic neurons (Fig. 3E). Analysis of the con-tribution of E12.5 and E14.5 neurons to theseidentities revealed timing biases for the genera-tion ormaturation of some interneuron subtypes(Fig. 3F and fig. S22). Altogether, these resultsstrongly suggested that interneurons exhibit agreat diversity of transcriptional signatures short-ly after becoming postmitotic in the GE.We hypothesized that the patterns of gene

expression identified in progenitor cells and

newborn neurons delineate specific lineages ofcortical interneurons. To investigate this idea,we limited our analysis to embryonic neuronsthat were assigned to the same subtype iden-tity by both RF and CCA methods, which wenamed “consensus” neurons and which belongto three interneuron subtypes: PV1, SST1, andSST2. We conducted MetaNeighbor analysis toidentify possible links between progenitor cellclusters and consensus neurons. This analysis

revealed putative SST+ and PV+ progenitor cellclusters at E12.5 and E14.5 (Fig. 4A and fig. S23).We then carried out differential gene expressionbetween E12.5 progenitor clusters P5 and P7(Fig. 4B), which exhibited the highest associa-tion with PV1 and SST1, respectively (Fig. 4A).We found early PV (Ccnd2 and St18) and SST(Epha5, Cdk14, and Maf) markers in these pro-genitor pools (Fig. 4, C and D), which are sub-sequently maintained in specific subtypes of


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cells to each progenitor cluster. (B) Violin plots depict the expressionof marker genes that distinguish VZ/SVZ identities and patterninginformation in progenitor clusters.



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newborn interneurons (Fig. 3, A and D). To val-idate these observations, we investigated thefunction of Maf in the delineation of MGE in-terneuron lineages. We infected progenitor cellsin Nkx2-1-Cre embryos with conditional retro-viruses expressing Cre-dependent control orMafvectors during the period of SST+ interneuronproduction (E12.5) (23) and explored the identityof labeled interneurons in the cortex of youngadult mice (Fig. 4E and fig. S24, A and B). Wefound that widespread expression of Maf inMGE progenitors increases the relative propor-

tion of SST+ interneurons at the expense of PV+

cells (Fig. 4F and fig. S24C). Conversely, condi-tional loss of Maf from MGE progenitor cellsdecreases the density of cortical SST+ inter-neurons (Fig. 4, G and H). We also observed thatoverexpression of Maf at the peak of PV neuro-genesis (E14.5) (23) represses PV+ interneuronfates (fig. S24, D and E). Altogether, these resultsindicated that Maf regulates the potential of in-terneurons to acquire SST+ interneuron identity.Our study reveals that GABAergic interneurons

have a propensity toward a defined fate long

before they occupy their final position in thecerebral cortex during early postnatal develop-ment. This suggests that interneuron diversitydoes not emerge in response to activity-dependentmechanisms in the cortex (1, 9) but rather is es-tablished early, before these cells reach the cortex,by specific transcriptional programs that thenunfold over the course of several weeks. Activity-dependent mechanisms undoubtedly influencedevelopment, maturation, and plasticity of cor-tical interneurons (24–26), but most aspects thatare directly linked to the functional diversity of


Fig. 3. Emergence of cortical interneuron diversity in the ganglioniceminences. (A) Heat map showing average expression of differentiallyexpressed (DE) genes among six classes of interneurons identified by RFclassification of embryonic neurons. (B) Integration of embryonicneurons and adult cortical interneurons in t-SNE space followingcanonical correlation analysis (CCA). (C) Embryonic neurons assignedto specific interneuron lineages by k-nearest neighbor (KNN) analysis aredepicted in the same t-SNE space. Unassigned embryonic neurons areomitted. (D) Heat map illustrating the expression of DE genes among

the 11 classes of interneurons identified by CCA. (E) Heat map ofmean AUROC (area under the receiver operating characteristic curve)scores for assigned interneuron cell types using the two independentapproaches (RF and CCA). AUROC scores of eight conserved interneuronsubtypes: PV1RF-PV1CCA = 0.95; PV1RF-PV3CCA = 0.90; PV1RF-PV4CCA =0.90; SST1RF-SST1CCA = 1; SST2RF-SST2CCA = 0.95; VIP2RF-VIP2CCA =0.90; VIP2RF-VIP3CCA = 0.85; NDNF1RF-NDNF1CCA = 0.7. (F) Histogramillustrating the relative contribution of E12.5 and E14.5 neurons toconserved interneuron subtypes.



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cortical interneurons seem to be intrinsicallydetermined (14, 27).Our analysis identifies earlymarkers formany

different classes of cortical interneurons, whosefunctional validation may eventually illuminatethemechanisms regulating the differentiation ofGABAergic interneurons into specific subtypes

and, through comparative analyses, inform theuse of stem cell biology for the generation ofdistinct classes of human cortical interneurons(28, 29). Thus, core aspects of interneuron iden-tity are drafted early in development, formingthe foundation on which later interactions withother neurons must function.

REFERENCES AND NOTES

1. A. Kepecs, G. Fishell, Nature 505, 318–326 (2014).2. T. Klausberger, P. Somogyi, Science 321, 53–57 (2008).3. X. Jiang et al., Science 350, aac9462 (2015).4. W. Muñoz, R. Tremblay, D. Levenstein, B. Rudy, Science 355,

954–959 (2017).5. H. Hu, J. Gan, P. Jonas, Science 345, 1255263 (2014).6. H. J. Pi et al., Nature 503, 521–524 (2013).7. A. Zeisel et al., Science 347, 1138–1142 (2015).8. B. Tasic et al., Nat. Neurosci. 19, 335–346 (2016).9. B. Wamsley, G. Fishell, Nat. Rev. Neurosci. 18, 299–309 (2017).10. S. A. Anderson, D. D. Eisenstat, L. Shi, J. L. R. Rubenstein,

Science 278, 474–476 (1997).11. G. Miyoshi, S. J. Butt, H. Takebayashi, G. Fishell, J. Neurosci.

27, 7786–7798 (2007).12. I. H. Smart, J. Anat. 121, 71–84 (1976).13. M. Götz, W. B. Huttner, Nat. Rev. Mol. Cell Biol. 6, 777–788

(2005).14. A. Paul et al., Cell 171, 522–539.e20 (2017).15. N. Flames et al., J. Neurosci. 27, 9682–9695 (2007).16. S. N. Silberberg et al., Neuron 92, 59–74 (2016).17. L. Sussel, O. Marín, S. Kimura, J. L. Rubenstein, Development

126, 3359–3370 (1999).18. S. Nery, G. Fishell, J. G. Corbin, Nat. Neurosci. 5, 1279–1287

(2002).19. H. Wichterle, J. M. Garcia-Verdugo, D. G. Herrera,

A. Alvarez-Buylla, Nat. Neurosci. 2, 461–466 (1999).20. D. M. Gelman, O. Marín, Eur. J. Neurosci. 31, 2136–2141

(2010).21. S. Frazer et al., Nat. Commun. 8, 14219 (2017).22. R. Tremblay, S. Lee, B. Rudy, Neuron 91, 260–292 (2016).23. C. P. Wonders, S. A. Anderson, Nat. Rev. Neurosci. 7, 687–696

(2006).24. D. Bortone, F. Polleux, Neuron 62, 53–71 (2009).25. N. V. De Marco García, T. Karayannis, G. Fishell, Nature 472,

351–355 (2011).26. N. Dehorter et al., Science 349, 1216–1220 (2015).27. G. Bartolini, G. Ciceri, O. Marín, Neuron 79, 849–864 (2013).28. J. L. Close et al., Neuron 93, 1035–1048.e5 (2017).29. A. M. Maroof et al., Cell Stem Cell 12, 559–572 (2013).

ACKNOWLEDGMENTS

We thank S. E. Bae for technical assistance and M. Fernández forgeneral lab support. We are grateful to M. Zhong at Yale StemCell Center Genomics and Bioinformatics Core for conducting RNAsequencing; N. Flames and B. Rico for critical reading of themanuscript; S. Anderson, C. Birchmeier, F. Cage, and G. Fishell forreagents and mouse strains; and members of the Marín, Rico,and Sestan laboratories for stimulating discussions and ideas.Funding: This work was supported by grants from the WellcomeTrust (103714MA) to O.M., the National Institutes for Health(NS095654 and MH106934) to N.S., and the Medical ResearchCouncil (N012291) and the Biotechnology and Biological SciencesResearch Council (N006542) to D.J.P., Y.Y., and T.P. L.L. wassupported by an EMBO postdoctoral fellowship. O.M. is a WellcomeTrust Investigator. Author contributions: D.M. and O.M. designedthe study. Single-cell experiments were performed by D.M., Z.L.,and T.G. Functional validation experiments were performedby L.L. and M.M. Data management and read processing wasperformed by M.L. Data analysis was performed by D.M., Z.L.,L.L., M.M., Y.Y., and T.X.H. The study was supervised by D.M.,Z.L., T.P., D.J.P., N.S., and O.M. The manuscript was prepared byD.M., Z.L., and O.M., with feedback from all authors. Competinginterests: None declared. Data and materials availability:Sequencing data have been deposited at the National Centerfor Biotechnology Information BioProjects Gene ExpressionOmnibus and are accessible through GEO Series accession numberGSE109796. c-Maf flox mice are available from C. Birchmeierunder a material agreement with the Max-Delbrück-Centrum fürMolekulare Medizin (Berlin). The supplementary materials containadditional data. All data needed to evaluate the conclusions in thepaper are present in the paper or the supplementary materials.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/360/6384/81/suppl/DC1Materials and MethodsFigs. S1 to S24Tables S1 to S4References (30–44)

6 December 2017; accepted 14 February 2018Published online 22 February 201810.1126/science.aar6821


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Fig. 4. Maf regulates SST+ interneuron fate. (A) AUROC values for putative lineages linking E12.5progenitor clusters (P) with specific interneuron subtypes (AUROC scores above 0.9). (B) t-SNEplot illustrating progenitor cell clusters at E12.5.Two SVZ clusters, P5 and P7, are highlighted by color.(C) Violin plots for selected DE genes between P5 and P7 clusters. (D) RNAscope labeling ofMGE SVZ progenitor cells with a Maf probe. (E) Coronal sections through the somatosensorycortex of P21 mice after viral infection with Gfp or Maf-P2A-Gfp retroviruses in the MGE at E12.5.(F) Quantification of the proportion of PV–/SST–, PV+, and SST+ interneurons; n = 5; c2 test, ***P <0.001. Post-hoc analysis was performed with binomial pairwise comparison with adjusted P valueby Bonferroni correction; PV–/SST– versus PV+, ***P < 0.001; PV+ versus SST+, **P < 0.01.(G) Coronal sections through the somatosensory cortex of P21 control and conditional Maf mutants.(H) Quantification of the density of GFP+/SST+ and GFP+/PV+ interneurons; n = 4, one-way analysisof variance with Tukey correction, *P < 0.05. Scale bars, 15 mm (D) and 100 mm [(E) and (G)].



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Early emergence of cortical interneuron diversity in the mouse embryo

David J. Price, Nenad Sestan and Oscar MarínDa Mi, Zhen Li, Lynette Lim, Mingfeng Li, Monika Moissidis, Yifei Yang, Tianliuyun Gao, Tim Xiaoming Hu, Thomas Pratt,

originally published online February 22, 2018DOI: 10.1126/science.aar6821 (6384), 81-85.360Science

, this issue p. 81Sciencemigrate and reach their final sites of differentiation and circuit integration.interneurons. Thus, the fate of embryonic interneurons can be read in their transcriptomes well before the neurons could be identified soon after their last mitosis by transcriptomic similarity with known classes of adult corticaldividing the immature embryonic interneurons themselves into classes. Nearly a dozen classes of embryonic neurons mouse. Transcriptomes of embryonic interneurons showed similarities to adult classes of differentiated interneurons, thusused single-cell transcriptomics to investigate when these subtypes emerge during interneuron development in the

et al.The adult brain contains dozens of different types of interneurons that control and refine neuronal circuits. Mi Embryonic hints of adult diversity

ARTICLE TOOLS http://science.sciencemag.org/content/360/6384/81

MATERIALSSUPPLEMENTARY http://science.sciencemag.org/content/suppl/2018/02/21/science.aar6821.DC1

REFERENCES

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Science. No claim to original U.S. Government WorksCopyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of

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