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Molecular design of hypothalamus development

Nature volume 582, pages 246–252 (2020)Cite this article

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Abstract

A wealth of specialized neuroendocrine command systems intercalated within the hypothalamus control the most fundamental physiological needs in vertebrates1,2. Nevertheless, we lack a developmental blueprint that integrates the molecular determinants of neuronal and glial diversity along temporal and spatial scales of hypothalamus development3. Here we combine single-cell RNA sequencing of 51,199 mouse cells of ectodermal origin, gene regulatory network (GRN) screens in conjunction with genome-wide association study-based disease phenotyping, and genetic lineage reconstruction to show that nine glial and thirty-three neuronal subtypes are generated by mid-gestation under the control of distinct GRNs. Combinatorial molecular codes that arise from neurotransmitters, neuropeptides and transcription factors are minimally required to decode the taxonomical hierarchy of hypothalamic neurons. The differentiation of γ-aminobutyric acid (GABA) and dopamine neurons, but not glutamate neurons, relies on quasi-stable intermediate states, with a pool of GABA progenitors giving rise to dopamine cells4. We found an unexpected abundance of chemotropic proliferation and guidance cues that are commonly implicated in dorsal (cortical) patterning5 in the hypothalamus. In particular, loss of SLIT–ROBO signalling impaired both the production and positioning of periventricular dopamine neurons. Overall, we identify molecular principles that shape the developmental architecture of the hypothalamus and show how neuronal heterogeneity is transformed into a multimodal neural unit to provide virtually infinite adaptive potential throughout life.

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Fig. 1: Developmental diversification of hypothalamic cell lineages.
Fig. 2: Neuronal differentiation in the hypothalamus.
Fig. 3: GRNs (regulons), including chemotropic guidance cues, in ectoderm-derived hypothalamic cells.
Fig. 4: Molecular configuration of hypothalamic dopamine systems.

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Data availability

Raw, processed and supplementary datasets have been deposited in GEO (accession number: GSE132730). GEO files include: 1) raw fastq files for every sequencing run; 2) filtered matrices for every sample in RDS file format including Seurat 3 objects with all processed cells; 3) original integrated dataset in RDS file format including Seurat 3 objects with all processed cells as well as all used commands; 4) integrated dataset used for dynamics analysis (which passed filtering of RNA velocity analysis); 5) AUCell matrices from pySCENIC pipeline; 6) full regulon hypothalamic network in GraphML file format; 7) metadata protocol describing all experimental, computational procedures and quality control. An interactive view of the integrated dataset (for processing in Pagoda2) can be accessed at https://doi.org/10.6084/m9.figshare.11867889 (~1.1 GB). All data presented (for example, imaging) will be made available by T. Harkany (tibor.harkany@ki.se or tibor.harkany@meduniwien.ac.at) upon reasonable request.

Code availability

The code used is available at https://doi.org/10.6084/m9.figshare.11867889.

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Acknowledgements

A. Reinthaler is acknowledged for her expert laboratory assistance. We thank the Biomedical Sequencing Facility at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences for assistance with next-generation sequencing, G. A. Bazykin for providing access to the 2TB RAM Makarich computational cluster for OMICS data analysis, E. Porcu for advice on GWAS analysis, M. Watanabe for antibodies, and G. Szabó, F. Erdélyi, J. Bunt, L. J. Richards and Y. Yanagawa for transgenic mice. This work was supported by the Swedish Research Council (F.L., I.A., T. Hökfelt, T. Harkany); Novo Nordisk Foundation (T. Hökfelt, T. Harkany); Bertil Hållsten Research Foundation (I.A.); Hjärnfonden (T. Harkany), European Research Council (STEMMING-FROM-NERVE, 2014-CoG-647844; I.A. and SECRET-CELLS, 2015-AdG-695136; T. Harkany), the EMBO Young Investigator Program (I.A.), Åke Wiberg Foundation (I.A.), Wallenberg Academy fellowship (F.L.), a Ming Wai Lau Center investigator grant (F.L.), the Strategic Research program for Brain Sciences (AMED, Japan; K.N.), Fonds spéciaux de recherche of the Université catholique de Louvain (F.C.), Actions de Recherche Concertées (17/22-079) of the Direction générale de l’Enseignement non obligatoire et de la Recherche scientifique–Direction de la Recherche scientifique–Communauté française de Belgique and granted by the Académie universitaire ‘Louvain’ (F.C.), 5 Top 100 Russian Academic Excellence Project at the Immanuel Kant Federal Baltic University and Russian Foundation for Basic Research (project 18-29-13055, K.P.) and intramural funds of the Medical University of Vienna (T. Harkany). M.F. is supported by a special research program of the Austrian Science Fund (FWF-F61). R.A.R. is an EMBO advanced research fellow (ALTF 493-2017). E.O.T. is supported by a scholarship from the Austrian Science Fund (FWF, DOC 33-B27). F.C. is a senior research associate of the F.R.S.-FNRS.

Author information

Author notes

  1. These authors contributed equally: Roman A. Romanov, Evgenii O. Tretiakov

  2. These authors jointly supervised the work: Erik Keimpema, Tibor Harkany

Authors and Affiliations

  1. Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria

    Roman A. Romanov, Evgenii O. Tretiakov, Maria Eleni Kastriti, Maja Zupancic, Martin Häring, Solomiia Korchynska, Marco Benevento, Patrick Rebernik, Igor Adameyko, Erik Keimpema & Tibor Harkany

  2. Department of Neuroscience, Biomedicum D7, Karolinska Institutet, Solna, Sweden

    Roman A. Romanov, Francois Lallemend, Tomas Hökfelt & Tibor Harkany

  3. Department of Physiology and Pharmacology, Biomedicum D6, Karolinska Institutet, Solna, Sweden

    Maria Eleni Kastriti & Igor Adameyko

  4. Human Genomics of Infection and Immunity, School of Life Sciences, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland

    Konstantin Popadin

  5. Center for Mitochondrial Functional Genomics, Institute of Living Systems, Immanuel Kant Baltic Federal University, Kaliningrad, Russia

    Konstantin Popadin

  6. Deptartment of Obesity and Internal Inflammation, Fukushima Medical University, Fukushima City, Japan

    Katsuhiko Nishimori

  7. Laboratory of Neural Differentiation, Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium

    Frédéric Clotman

  8. Department of Cell and Developmental Biology, University College London, London, UK

    William D. Andrews & John G. Parnavelas

  9. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Matthias Farlik & Christoph Bock

  10. Department of Dermatology, Medical University of Vienna, Vienna, Austria

    Matthias Farlik

  11. Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria

    Christoph Bock

Authors
  1. Roman A. Romanov
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  2. Evgenii O. Tretiakov
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  3. Maria Eleni Kastriti
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  4. Maja Zupancic
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  5. Martin Häring
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  6. Solomiia Korchynska
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  7. Konstantin Popadin
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  8. Marco Benevento
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  9. Patrick Rebernik
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  10. Francois Lallemend
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  11. Katsuhiko Nishimori
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  12. Frédéric Clotman
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  13. William D. Andrews
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  14. John G. Parnavelas
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  15. Matthias Farlik
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  16. Christoph Bock
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  17. Igor Adameyko
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  18. Tomas Hökfelt
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  19. Erik Keimpema
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  20. Tibor Harkany
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Contributions

T. Harkany and R.A.R. conceived the project; K.N., I.A., T. Hökfelt and T. Harkany procured funding; R.A.R., I.A., M.F., C.B., T. Hökfelt, E.K. and T. Harkany designed experiments; R.A.R., E.O.T., M.E.K., M.Z., M.H., S.K., K.P., M.B., P.R. and M.F. performed experiments and analysed data; F.L., K.N., F.C., W.D.A. and J.G.P. provided unique reagents and mouse models. R.A.R. and T. Harkany wrote the manuscript with input from all co-authors.

Corresponding author

Correspondence to Tibor Harkany.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Nenad Sestan 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 Fig. 1 Marker genes to define molecular phenotypes.

a, Differential gene expression by glia (clusters 1–9) and neurons (clusters 10–45). Because of the integration of six stages, early-expressed TFs and spatially restricted genes amenable to cellular differentiation were identified. For neuronal clusters, fast neurotransmitter specificity is shown to the right. The relative diameter of the solid circles for each cluster is scaled to the fraction of cells that expresses a specific gene. Colour coding and numbering at the top correspond to those in Fig. 1a. b, Top, Differential TF expression in 45 ectoderm-derived cell groups in the hypothalamus. Bottom, subclass-specific TFs recapitulate the UMAP positions of neuronal (left) and glial (right) subtypes. c, Integrated molecular and anatomical annotation of hypothalamic clusters with their specific assignment to hypothalamic areas. ARC-Agrp, arcuate nucleus agouti-related peptide+ neurons; ARC-Sst, arcuate nucleus somatostatin+ neurons; ARC-TIDA, arcuate nucleus–tuberoinfundibular dopamine neurons; Gal, galanin; Ghrh/Vacht, growth hormone-releasing hormone/vesicular acetylcholine transporter+ neurons; LH, lateral hypothalamus; LH-Lhx9, lateral hypothalamus LIM homeobox 9+ cluster; Meis2, meis homeobox 2; MM, mammillary nucleus; MM-Lhx9, mammillary nucleus LIM homeobox 9+ neurons; Pomc, proopiomelanocortin; PH, posterior hypothalamus; PMM, premamillary nucleus; Tbr1, T-box brain transcription factor 1.

Extended Data Fig. 2 Molecular analysis of TFs involved in neurogenesis and neuronal differentiation.

a, Comparative and time-resolved analysis of the cell bridge by MNN, CONOS and Seurat alignment. In UMAP space on separate developmental stages, MNN, CONOS and Seurat algorithms were compared for their ability to specifically resolve the transition of progenitors to immature cells (bridge). Colour codes correspond to those in Fig. 1a. b, RNA velocity at E15.5, E17.5 and P0. Colour codes are consistent with those in Fig. 1a. Note the presence of a bridge (grey background) between progenitor/glial and neuronal compartments at early developmental stages with its rupture being evident by birth. c, Gene expression in UMAP space at E15.5. Note a central role for Notch signalling in neurogenesis. d, Genetic tracing of Ascl1+ cells produced in the developing hypothalamus during the E12.5–E16.5 period. e, In situ hybridization showing the distribution of Tbr1 and Eomes. Image credit: Allen Institute (https://www.brain-map.org). f, Genetic tracing of Ascl1+ cells in the developing hypothalamus of Ascl+/− and Ascl−/− mice. Sox2 was localized by immunohistochemistry. g, Sox2, Ascl1 (Tomato) and Rbfox3 (NeuN) immunolocalization at successive developmental stages. h, Genetic tracing of Ascl+ cells postnatally (as in Fig. 1f). Scale bars, 200 μm (d), 20 μm (f–h).

Extended Data Fig. 3 Neurotransmitter and neuropeptide specificity and load in the developing hypothalamus.

a–c, Coincident profiling of fast neurotransmitters (a), neuropeptides (b) and neuropeptide receptors (c, top) in 45 cell groups of ectodermal origin. c, Bottom left, given their abundance, Ntrk2 and Adcyap1r1 were plotted separately along the developmental timeline studied with appropriate scaling. Bottom right, likewise, the distribution of both receptors per cell cluster was mapped and scaled separately. d, Coincident profiling of neuropeptides in neuronal clusters distinguished as GABA (blue) and glutamate (grey) phenotypes. e, Map of Th expression in GABA and glutamate neurons. Colour coding as in d. f, Developmental mapping of hypothalamic Oxtr expression in OxtrVenus/+ mice. Low-magnification image surveys are shown (see also Fig. 2e). Scale bars, 200 μm (f). Data shown as dot plots and scaled as previously described6,51,65.

Extended Data Fig. 4 Hierarchical relationship of GRNs (regulons).

An AUC separability plot was used to assign regulons that determine cell cluster identities identified in SCENIC23. GRNs were reconstructed individually for each cell and then assigned as ‘regulon representation’ (Logreg test) to each cell group. TFs to the left are representative for each regulon. Marked dendrogram branchpoints were estimated by both the Wilcoxon and Logreg tests (see also https://doi.org/10.6084/m9.figshare.11867889).

Extended Data Fig. 5 Relationships between regulons and disease phenotypes in humans.

a, Complete heat map of associations between regulon activity and clinical disease phenotype. Left: classifications of diseases as per phenotypic criteria of the UK biobank registry (https://www.ukbiobank.ac.uk). Top, master genes for each regulon. Genes presented in Fig. 3 are in red and highlighted in b. Colour coding from deep blue to bright yellow shows increasing correlation probability. b, Scatter plot reflecting the ratios of mutability in master genes versus all downstream target genes per regulon. Mutability and the constrains of TFs were expressed as the total number of mutations. Colours represent four quadrants that were separated on the basis of the total number of mutations per master gene (medians, y-axis) versus target genes (medians, x-axis). Horizontal line corresponds to the median of SNPs in all genes. Dot size reflects the median influence of a given regulon on its targets as per SCENIC output.

Extended Data Fig. 6 Molecular complexity and function of the Onecut3 regulon.

a, Interlinked Onecut2 and Onecut3 regulons in hypothalamic neurons. Genes that were biologically validated (see below) are shown in black. b, Co-expression of Onecut2 and Onecut3 along the rostrocaudal axis of the hypothalamus. c, Co-localization of Onecut3 and its target genes (from a). d, Overexpression of Onecut3 (OC3) and ATP-binding cassette D2 (Abcd2, to control promoter activity) in Neuro2A cells. Left, representative images by multiple fluorescence labelling-differential interference microscopy. Right, quantification of Hoechst+ and phospho-histone H3 (pHH3)+ Neuro2A cells revealed significantly reduced proliferation upon Onecut3 overexpression. No significant cell death was induced by either overexpressed plasmid or the transfection reagent alone. e, qPCR analysis of genes regulated by Onecut3: Cxxc5, Tmprss9 and Th. All data were normalized to samples transfected with Abcd2, which were taken as technical controls. Scale bars, 50 μm (d), 20 μm (b, f), 10 μm (g).

Extended Data Fig. 7 Experimental validation of ventricle-restricted genes identified by scRNA-seq.

a, Left, expressional dynamics of ventricle-associated marker genes: Slc1a3, Rax and Dll3 on UMAP embedding (top) and trend lines (bottom). Right, validation by in situ hybridization. b, In situ hybridization for the co-existence of Slit2 and Rax in ventricular progenitors and consequential medial-to-lateral Slit1–Dll1–Dll3 patterns during neuronal differentiation and migration by E15.5 (left, top right). Left-to-right orientation corresponds to medial-to-lateral hypothalamic positions. Bottom right, localization of Slit1 and Slit2 mRNAs in the VMH at E18.5. Scale bars, 200 μm (a), 20 μm (b).

Extended Data Fig. 8 Physiological and morphological subtypes of hypothalamic dopamine neurons.

a, Action potential waveforms of dopamine neurons within the A12–A14 groups. Note the diversification of A14 dopamine cells into subgroups A–D with clearly different action potential signatures. Morphological reconstruction of biocytin-filled neurons is shown with each group. b, Distribution of tdTomato+ neurons in the hypothalamus of Slc6a3-Ires-cre::Ai14 mice. Scale bars, 50 μm (b), 20 μm (a).

Extended Data Fig. 9 Transcriptional and physiological features of dopamine neurons in the developing hypothalamus.

a, Ascl1-creERT2/+::Ai14 (control) versus Ascl1-creERT2/ERT2::Ai14 mice (a knock-in mouse line with Cre disrupting the Ascl1 gene, referred to as Ascl1 ko), injected with tamoxifen at E11.5 and analysed at E13.5. Note the accumulation of tdTomato+ cells in the KO relative to controls. b, Genetic tracing in Ascl1-creERT2::Ai14 reporter mice identified Ascl1+/Th+ neurons within the preoptic and periventricular nuclei. Meanwhile, Ascl1−/Th+ neurons populated the Arc and zona incerta (ZI) by E18.5. c, Isl1 and Meis2 transcriptional trends of differentiation for trajectories in Th+ groups (clusters 1–9). Amplitudes are shown in log10 scale. Line shading corresponds to mean ± s.e.m. d, Genetic lineage tracing using Isl1-cre::Ai14 mice. e, In situ hybridization for Gad1 and Th revealed anti-parallel expressional load for these genes as a factor of medial-to-lateral positioning. Scatter plots show the number of fluorescent puncta per cell (threshold >2). f, In situ hybridization for Meis2, Th and Ddc in the hypothalami of E18.5 and P2 mice. Scale bars, 120 μm (a, f (left)), 12 μm (b, d–f (right)).

Extended Data Fig. 10 GABA origin of hypothalamic dopamine neurons.

a, Immunohistochemical analysis of TH and ONECUT3 protein expression in the hypothalamus of (BAC)GAD65–eGFP and GAD67–GFP mice at the developmental time-points indicated. Note a gradual GABA-to-dopamine transition as a factor of advancing age with ONECUT3 expression preceding that of TH. Dashed rectangles denote the positions of high-resolution insets. b, Expression patterns of regulon-forming TFs that directly drive Th transcription in the developing hypothalamus. Meis2, Pbx3 and Dlx1 were visualized on UMAP embedding for neuronal lineages. c, Histochemical localization of the migratory route of prospective PeVN dopamine neurons (cluster 9) through the coincident localization of TH and ONECUT3 during embryonic development. Dashed lines denote the ventricular surface. d, Localization of Onecut2 and Pmfbp1a target genes within the Onecut3 regulon to PeVN dopamine neurons by a combination of immunohistochemistry and in situ hybridization. e, Sst expression in PeVN dopamine neurons. f, Post hoc reconstruction of A14 Onecut3+ dopamine neurons after patch-clamp recordings. Scale bars, 200 μm (a, overviews), 50 μm (a, insets), 20 μm (f), 12 μm (c–e).

Supplementary information

Supplementary Information

This file contains Supplementary Sections 1-3. Section 1 contains integrated data analysis - quality control, data filtration, integration and clustering, and includes Supplementary Figures S1-S11. Section 2 includes identification of dopamine cell groups and reconstruction of their developmental trajectories, including Supplementary Figures S12 and S13. Section 3 contains additional remarks to the source data on Figshare.

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Romanov, R.A., Tretiakov, E.O., Kastriti, M.E. et al. Molecular design of hypothalamus development. Nature 582, 246–252 (2020). https://doi.org/10.1038/s41586-020-2266-0

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