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Single-cell transcriptomic landscape of the developing human spinal cord

Nature Neuroscience volume 26, pages 902–914 (2023)Cite this article

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Abstract

Understanding spinal cord assembly is essential to elucidate how motor behavior is controlled and how disorders arise. The human spinal cord is exquisitely organized, and this complex organization contributes to the diversity and intricacy of motor behavior and sensory processing. But how this complexity arises at the cellular level in the human spinal cord remains unknown. Here we transcriptomically profiled the midgestation human spinal cord with single-cell resolution and discovered remarkable heterogeneity across and within cell types. Glia displayed diversity related to positional identity along the dorso-ventral and rostro-caudal axes, while astrocytes with specialized transcriptional programs mapped into white and gray matter subtypes. Motor neurons clustered at this stage into groups suggestive of alpha and gamma neurons. We also integrated our data with multiple existing datasets of the developing human spinal cord spanning 22 weeks of gestation to investigate the cell diversity over time. Together with mapping of disease-related genes, this transcriptomic mapping of the developing human spinal cord opens new avenues for interrogating the cellular basis of motor control in humans and guides human stem cell-based models of disease.

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Fig. 1: A single-cell atlas of the human spinal cord at midgestation.
Fig. 2: OPC/Oligo lineage in the spinal cord.
Fig. 3: Astrocytes in the spinal cord show diversity along several axes.
Fig. 4: Midline glia in the developing human spinal cord.
Fig. 5: Neuron and MN diversity in the spinal cord.
Fig. 6: Mapping of disease gene sets on a human developing spinal cord atlas.
Fig. 7: Integration of developing human spinal cord datasets.

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Data availability

Data generated for the analyses presented in this work are available under GEO accession number GSE188516. A website associated with the manuscript, including an interactive data browser, is available at https://devspinalcord.su.domains/.

Code availability

All code used for data analysis is available as part of the packages mentioned above.

References

  1. Jessell, T. M. Neuronal specification in the spinal cord: inductive signals and transcriptional codes. Nat. Rev. Genet. 1, 20–29 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Haim, L. B. & Rowitch, D. H. Functional diversity of astrocytes in neural circuit regulation. Nat. Rev. Neurosci. 18, 31–41 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Monani, U. R. Spinal muscular atrophy: a deficiency in a ubiquitous protein; a motor neuron-specific disease. Neuron 48, 885–896 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Swash, M., Leader, M., Brown, A. & Swettenham, K. W. Focal loss of anterior horn cells in the cervical cord in motor neuron disease. Brain 109, 939–952 (1986).

    Article  PubMed  Google Scholar 

  5. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Rayon, T., Maizels, R. J., Barrington, C. & Briscoe, J. Single-cell transcriptome profiling of the human developing spinal cord reveals a conserved genetic programme with human-specific features. Development 148, dev199711 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sousa, A. M. M., Meyer, K. A., Santpere, G., Gulden, F. O. & Sestan, N. Evolution of the human nervous system function, structure, and development. Cell 170, 226–247 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gu, Z. et al. Control of species-dependent cortico-motoneuronal connections underlying manual dexterity. Science 357, 400–404 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Oberheim, N. A. et al. Uniquely hominid features of adult human astrocytes. J. Neurosci. 29, 3276–3287 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl Acad. Sci. USA 106, 5743–5748 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ohmura, Y. & Kuniyoshi, Y. A translational model to determine rodent’s age from human foetal age. Sci. Rep. 7, 17248 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tadros, M. A., Lim, R., Hughes, D. I., Brichta, A. M. & Callister, R. J. Electrical maturation of spinal neurons in the human fetus: comparison of ventral and dorsal horn. J. Neurophysiol. 114, 2661–2671 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Molofsky, A. V. et al. Astrocyte-encoded positional cues maintain sensorimotor circuit integrity. Nature 509, 189–194 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tsai, H.-H. et al. Regional astrocyte allocation regulates CNS synaptogenesis and repair. Science 337, 358–362 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hochstim, C., Deneen, B., Lukaszewicz, A., Zhou, Q. & Anderson, D. J. Identification of positionally distinct astrocyte subtypes whose identities are specified by a homeodomain code. Cell 133, 510–522 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Khakh, B. S. & Deneen, B. The emerging nature of astrocyte diversity. Annu. Rev. Neurosci. 42, 187–207 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Allen, N. J. Astrocyte regulation of synaptic behavior. Annu. Rev. Cell Dev. Biol. 30, 439–463 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Allen, N. J. & Barres, B. A. Glia—more than just brain glue. Nature 457, 675–677 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Allen, N. J. & Eroglu, C. Cell biology of astrocyte–synapse interactions. Neuron 96, 697–708 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chung, W.-S. et al. Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways. Nature 504, 394–400 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Man, H.-Y. GluA2-lacking, calcium-permeable AMPA receptors—inducers of plasticity? Curr. Opin. Neurobiol. 21, 291–298 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Droste, D. et al. Ca2+-permeable AMPA receptors in mouse olfactory bulb astrocytes. Sci. Rep. 7, 44817 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lavialle, M. et al. Structural plasticity of perisynaptic astrocyte processes involves ezrin and metabotropic glutamate receptors. Proc. Natl Acad. Sci. USA 108, 12915–12919 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Srinivasan, J., Schachner, M. & Catterall, W. A. Interaction of voltage-gated sodium channels with the extracellular matrix molecules tenascin-C and tenascin-R. Proc. Natl Acad. Sci. USA 95, 15753–15757 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Betters, E., Liu, Y., Kjaeldgaard, A., Sundström, E. & García-Castro, M. I. Analysis of early human neural crest development. Dev. Biol. 344, 578–592 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ghazale, H. et al. RNA profiling of the human and mouse spinal cord stem cell niches reveals an embryonic-like regionalization with MSX1+ roof-plate-derived cells. Stem Cell Rep. 12, 1159–1177 (2019).

    Article  CAS  Google Scholar 

  32. Cañizares, M. A. et al. Multiple steps characterise ventricular layer attrition to form the ependymal cell lining of the adult mouse spinal cord central canal. J. Anat. 236, 334–350 (2020).

    Article  PubMed  Google Scholar 

  33. Sagner, A. & Briscoe, J. Establishing neuronal diversity in the spinal cord: a time and a place. Development 146, dev182154 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Weidenheim, K. M., Epshteyn, I., Rashbaum, W. K. & Lyman, W. D. Patterns of glial development in the human foetal spinal cord during the late first and second trimester. J. Neurocytol. 23, 343–353 (1994).

    Article  CAS  PubMed  Google Scholar 

  35. Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069.e23 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Girskis, K. M. et al. Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions. Neuron 109, 3239–3251.e7 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shinozuka, T. & Takada, S. Morphological and functional changes of roof plate cells in spinal cord development. J. Dev. Biol. 9, 30 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lemke, G. Glial control of neuronal development. Annu. Rev. Neurosci. 24, 87–105 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Chédotal, A. Roles of axon guidance molecules in neuronal wiring in the developing spinal cord. Nat. Rev. Neurosci. 20, 380–396 (2019).

    Article  PubMed  Google Scholar 

  41. Masahira, N. et al. Olig2-positive progenitors in the embryonic spinal cord give rise not only to motoneurons and oligodendrocytes, but also to a subset of astrocytes and ependymal cells. Dev. Biol. 293, 358–369 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. Wang, H. et al. Region-specific distribution of Olig2-expressing astrocytes in adult mouse brain and spinal cord. Mol. Brain 14, 36 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vue, T. Y., Kim, E. J., Parras, C. M., Guillemot, F. & Johnson, J. E. Ascl1 controls the number and distribution of astrocytes and oligodendrocytes in the gray matter and white matter of the spinal cord. Development 141, 3721–3731 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Marshall, C. A. G., Novitch, B. G. & Goldman, J. E. Olig2 directs astrocyte and oligodendrocyte formation in postnatal subventricular zone cells. J. Neurosci. 25, 7289–7298 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Llorens-Bobadilla, E. et al. A latent lineage potential in resident neural stem cells enables spinal cord repair. Science 370, eabb8795 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Russ, D. E. et al. A harmonized atlas of mouse spinal cord cell types and their spatial organization. Nat. Commun. 12, 5722 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sathyamurthy, A. et al. Massively parallel single nucleus transcriptional profiling defines spinal cord neurons and their activity during behavior. Cell Rep. 22, 2216–2225 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Osseward, P. J. et al. Conserved genetic signatures parcellate cardinal spinal neuron classes into local and projection subsets. Science 372, 385–393 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Häring, M. et al. Neuronal atlas of the dorsal horn defines its architecture and links sensory input to transcriptional cell types. Nat. Neurosci. 21, 869–880 (2018).

    Article  PubMed  Google Scholar 

  52. Fang, R. et al. Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH. Science 377, 56–62 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Blum, J. A. et al. Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons. Nat. Neurosci. 24, 572–583 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Price, S. R., Garcia, N. V. D. M., Ranscht, B. & Jessell, T. M. Regulation of motor neuron pool sorting by differential expression of type ii cadherins. Cell 109, 205–216 (2002).

    Article  CAS  PubMed  Google Scholar 

  55. Dewitz, C., Duan, X. & Zampieri, N. Organization of motor pools depends on the combined function of N-cadherin and type II cadherins. Development 146, dev180422 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. van der Knaap, M. S. & Bugiani, M. Leukodystrophies: a proposed classification system based on pathological changes and pathogenetic mechanisms. Acta Neuropathol. 134, 351–382 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ilieva, H., Polymenidou, M. & Cleveland, D. W. Non-cell autonomous toxicity in neurodegenerative disorders: ALS and beyond. J. Cell Biol. 187, 761–772 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang, Q. et al. Single-cell analysis reveals dynamic changes of neural cells in developing human spinal cord. EMBO Rep. 22, e52728 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ashrafi, S. et al. Wnt7A identifies embryonic γ-motor neurons and reveals early postnatal dependence of γ-motor neurons on a muscle spindle-derived signal. J. Neurosci. 32, 8725–8731 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Delile, J. et al. Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord. Development 146, dev173807 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Winnubst, J. & Arber, S. A census of cell types in the brain’s motor cortex. Nature 598, 33–34 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Sloan, S. A., Andersen, J., Pașca, A. M., Birey, F. & Pașca, S. P. Generation and assembly of human brain region-specific three-dimensional cultures. Nat. Protoc. 13, 2062–2085 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Trevino, A. E. et al. Chromatin accessibility dynamics in a model of human forebrain development. Science 367, eaay1645 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Matson, K. J. E. et al. Isolation of adult spinal cord nuclei for massively parallel single-nucleus RNA sequencing. J. Vis. Exp. https://doi.org/10.3791/58413 (2018).

  65. Matevossian, A. & Akbarian, S. Neuronal nuclei isolation from human postmortem brain tissue. J. Vis. Exp. https://doi.org/10.3791/914 (2008).

  66. Charzewska, A. et al. Hypomyelinating leukodystrophies—a molecular insight into the white matter pathology. Clin. Genet 90, 293–304 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Abrams, C. K. & Scherer, S. S. Gap junctions in inherited human disorders of the central nervous system. Biochim. Biophys. Acta 1818, 2030–2047 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Brenner, M. et al. Mutations in GFAP, encoding glial fibrillary acidic protein, are associated with Alexander disease. Nat. Genet. 27, 117–120 (2001).

    Article  CAS  PubMed  Google Scholar 

  69. Zardadi, S. et al. Four mutations in MITF, SOX10 and PAX3 genes were identified as genetic causes of waardenburg syndrome in four unrelated Iranian patients: case report. BMC Pediatr. 21, 70 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Depienne, C. et al. Brain white matter oedema due to ClC-2 chloride channel deficiency: an observational analytical study. Lancet Neurol. 12, 659–668 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Kenna, K. P. et al. NEK1 variants confer susceptibility to amyotrophic lateral sclerosis. Nat. Genet. 48, 1037–1042 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Cirulli, E. T. et al. Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Science 347, 1436–1441 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Farhan, S. M. K. et al. Exome sequencing in amyotrophic lateral sclerosis implicates a novel gene, DNAJC7, encoding a heat-shock protein. Nat. Neurosci. 22, 1966–1974 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Smith, B. N. et al. Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS. Neuron 84, 324–331 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Smith, B. N. et al. Mutations in the vesicular trafficking protein annexin A11 are associated with amyotrophic lateral sclerosis. Sci. Transl. Med. 9, eaad9157 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Nicolas, A. et al. Genome-wide analyses identify KIF5A as a novel ALS gene. Neuron 97, 1268–1283.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of S. P. Pașca, A. M. Pașca and W. G. Greenleaf laboratories for support, discussion and advice, especially S. Kanton, Y. Miura, L. Li, A. Trevino, K. W. Kelley and F. Birey. This work was supported by the S. Coates and V. Coates Foundation (S.P.P.), the Stanford Brain Organogenesis Program and the Big Idea Grant in the Stanford Wu Tsai Neurosciences Institute (S.P.P.), Bio-X (S.P.P.), the Kwan Fund (S.P.P), the Senkut Research Funds (S.P.P.), the Chan Zuckerberg Initiative Ben Barres Investigator Award (S.P.P), the Stanford University Department of Neurosurgery (J.A.K.) and the Stanford Wu Tsai Neurosciences Institute (J.A.K.). S.P.P. is a New York Stem Cell Foundation Robertson Stem Cell investigator and a Chan Zuckerberg Biohub investigator. W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative. Fellowship support was provided by the Idun Berry Postdoctoral Fellowship (J.A.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. Jimena Andersen

    Present address: Department of Human Genetics, Emory University, Atlanta, GA, USA

  2. These authors contributed equally: Jimena Andersen, Nicholas Thom.

Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Jimena Andersen, Nicholas Thom, Xiaoyu Chen, Massimo Mario Onesto, Neal D. Amin, Se-Jin Yoon & Sergiu P. Pașca

  2. Stanford Brain Organogenesis, Wu Tsai Neurosciences Institute, Stanford, CA, USA

    Jimena Andersen, Nicholas Thom, Xiaoyu Chen, Massimo Mario Onesto, Neal D. Amin, Se-Jin Yoon & Sergiu P. Pașca

  3. Department of Neurosurgery, Stanford University, Stanford, CA, USA

    Jennifer L. Shadrach & Julia A. Kaltschmidt

  4. Neurosciences Graduate Program, Stanford University, Stanford, CA, USA

    Massimo Mario Onesto

  5. Department of Human Genetics, Emory University, Atlanta, GA, USA

    Li Li

  6. Department of Genetics, Stanford University, Stanford, CA, USA

    William J. Greenleaf & Fabian Müller

  7. Department of Applied Physics, Stanford University, Stanford, CA, USA

    William J. Greenleaf

  8. Center for Bioinformatics, Saarland University, Saarbrücken, Germany

    Fabian Müller

  9. Department of Pediatrics, Division of Neonatology, Stanford University, Stanford, CA, USA

    Anca M. Pașca

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Contributions

J.A., N.T. and S.P.P. conceived the project and designed experiments. N.T. performed data analysis with guidance from F.M. and W.J.G. J.A. guided the biological interpretation of the analysis and performed immunohistochemistry validations. F.M. performed the RNA velocity analysis. J.L.S and J.A.K. performed and interpreted RNAScope validations. A.M.P., N.D.A., L.L. and J.A. processed samples. X.C. performed the nuclei NeuN-sort experiment. M.M.O performed immunohistochemistry validations and quantification. S.-J.Y. performed Thy1 immunopanning. J.A. performed Chromium 10x capture. J.A., N.T. and S.P.P. wrote the manuscript with input from all authors. J.A. and S.P.P. supervised the work.

Corresponding author

Correspondence to Sergiu P. Pașca.

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Competing interests

W.J.G. was a consultant for 10x Genomics. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Sample overview and quality control metrics.

a. Table showing the number of single cells and single nuclei per sample and collection method. Numbers correspond to total numbers of cells or nuclei recovered after quality control (QC) and filtering. b. UMAPs showing single cells (left) and single nuclei (right) before integration, colored by cell type. c. Single cell and single nucleus UMAPs split by collection method. d. Picture of GW18 spinal cord sample before and after dividing it into rostro-caudal regions: cervical (C), thoracic (T1, T2) and lumbar (L). e. Single cell UMAPs showing GW18 cells split by region. f. Heatmap showing HOX gene expression as the sum of gene counts normalized to total sample counts in all samples. g. Scatter plot showing mitochondrial (MT) percent versus nFeature with points colored by cell type. The red dash line shows a less conservative hypothetical cutoff. h. Bar plot showing the proportion of cells/nuclei with MT percent above or below the hypothetical 15% cutoff per cell type. i. Violin plots showing total counts (nCount), ratio of spliced to unspliced counts, total number of features (nFeature), percent of mitochondrial (MT) counts, and percent of ribosomal (ribo) counts separated by cell type in both single cell and nucleus samples. j. Single cell and single nucleus UMAPs split by sample age. k. Heatmap showing the correlation of the normalized average gene expression between sample ages in single cell samples separated by cell type represented as R2 values. l. Heatmap showing the correlation of the normalized average gene expression between ages in single nuclei samples separated by cell type represented as R2 values. m. G2/M and S cell cycle scores in the integrated single cell and single nuclei UMAP.

Extended Data Fig. 2 Comparison of single cell and single nucleus transcriptional data.

a. Heatmap showing the correlation of the normalized average gene expression between single cell and single nucleus samples separated by cell type represented as R2 values. b. Heatmaps showing the top 5 gene ontology (GO) terms by cell type of genes enriched in either single cell samples (top) or single nucleus samples (bottom) per cell type, colored by significance level. Values shown were calculated using an over-representation analysis with a hypergeometric test. Exact p-values are shown in Supplementary Table 4. c. Heatmap showing gene expression of selected genes within GO terms that are differentially expressed in single cell and single nucleus samples as the sum of gene counts normalized to total counts per cell type.

Extended Data Fig. 3 Quality control and cell types in the OPC/Oligo lineage.

a. Bar plot showing the percent of single cells and single nuclei in the OPC/Oligo subcluster. b. UMAP of OPC/Oligo subcluster split to show single cell and single nucleus samples separately. c. Root probability in UMAP space as computed by scVelo. d. Bubble plot showing Gene Ontology (GO) enrichment analysis of genes represented in the 10 clusters of velocity variable genes in Fig. 2d. Enrichment was computed using the ‘TopGO’ R package with a Fisher’s Exact test.

Extended Data Fig. 4 Cell types and quality control in the astroglia subcluster.

a. Bar plot showing the percent of single cells and single nuclei in the astroglia subcluster. b. UMAP of astroglia subcluster split to show single cell and single nucleus samples separately. c. UMAP plots showing expression of astroglia genes. d. Highlight of cycling astroglia, highlighting the cells selected for subclustering. e. UMAP plots showing gene expression of markers associated with dorsal and ventral, and fibrous and protoplasmic identities within the cycling astroglia subcluster. f. Reactive astrocyte signatures shown as sum expression of reactive astrocyte genes from20 in single cell and single nucleus samples separately. g. UMAP plots showing gene expression of V1 astrocyte-associated markers. h. MA plot showing differential expression between V1 fibrous astrocytes and the rest of fibrous astrocytes. V1 fibrous astrocytes were selected based on their expression of RELN and absence of SLIT1 (RELN counts > 1, SLIT1 counts = 0). Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. i. MA plot showing differential expression between V1 protoplasmic astrocytes and the rest of protoplasmic astrocytes. V1 protoplasmic astrocytes were selected based on their expression of RELN and absence of SLIT1 (RELN counts > 1, SLIT1 counts = 0). Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. j. UMAP plot showing expression of NKX6-1 in the astroglia subcluster. k. Representative immunohistochemistry images of SOX9 astrocytes expressing either the dorsal marker PAX3 or the ventral marker NKX6-1 in a coronal GW19 spinal cord cryosection. Top and bottom images correspond to different cryosections. Immunohistochemistry replicated 6 and 4 times in 2 independent samples. l. Dot plot showing the expression of transcription factors associated with patterning in the spinal cord. The size of the dots represents the percent of cells expressing each gene while the color depicts the scaled average expression per cell type. Scale bars: 100 μm (insets in k), 200 μm (k).

Extended Data Fig. 5 Fibrous and protoplasmic astrocytes in the spinal cord.

a. MA plot showing differential expression between protoplasmic and fibrous astrocytes. Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. b. MA plot showing differential expression between dorsal and ventral protoplasmic astrocytes. Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. c. MA plot showing differential expression between dorsal and ventral fibrous astrocytes. Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. d. Representative immunohistochemistry images of the fibrous astrocyte marker CRYAB and colocalization with astrocyte markers AQP4 and GFAP in a coronal GW19 spinal cord cryosection. Immunohistochemistry replicated 3 times in 1 independent sample. e. Representative immunohistochemistry images of the protoplasmic astrocyte markers GLUL and SLC1A2 and colocalization with the astrocyte marker GFAP in a coronal GW19 spinal cord cryosection. Immunohistochemistry combination replicated 1 time in 1 independent sample, and independently 4, 4 and 16 times, respectively, in 1-2 independent samples. f. Representative immunohistochemistry images of AQP4 and SMI-312 showing the location of astrocytes and axons in the GW19 spinal cord. g. Representative immunohistochemistry images of the fibrous astrocyte marker CRYAB and the myelinating oligodendrocyte marker MBP showing the location of white matter in the GW19 spinal cord. h. UMAP plot showing expression of CD38 in the astroglia subcluster. i. Dot plot showing the expression of selected genes associated with astrocyte functions in fibrous and protoplasmic subtypes. The size of the dots represents the percent of cells expressing each gene while the color depicts the scaled average expression per subtype. j. Heatmap showing the sum of specificity scores for all interactions between fibrous and protoplasmic astrocytes and OPC/Oligo cell types as computed by NATMI (see Methods). k. Network plot showing interactions between sender and receiver cell types for ligand and receptor pairs. Only fibrous astrocytes and late-stage OPC/Oligo cell types are shown. The size of the dots represents the percent of cells expressing each gene, while the color of the dots depicts their average expression. The color of the line represents the specificity of each interaction as computed by NATMI. Scale bars: 50 μm (insets in d, e), 200 μm (d, e, f, g).

Extended Data Fig. 6 Ventricular zone (VZ) cells in the human spinal cord at midgestation.

a. UMAP plot showing expression of ZIC1 in the VZ subcluster. b. UMAP plots showing expression of SOX9 and PAX7 in the VZ subcluster. c. Representative immunohistochemistry image showing ZIC1 in the VZ of GW19 spinal cord. Immunohistochemistry replicated 4 times in 2 independent samples. d. Representative immunohistochemistry image showing SOX9, FOXA2 and PAX7 in the VZ of GW19 spinal cord. Arrow shows dissociation of ventral cells from the ventricular wall. Immunohistochemistry replicated 2 times in 2 independent samples. Individual markers were replicated 16, 4 and 4 times. e. UMAP plot showing expression of RFX4 in the VZ subcluster. f. Representative immunohistochemistry image showing RFX4 in the VZ of GW19 spinal cord. Immunohistochemistry replicated 4 times in 2 independent samples. g. UMAP plot showing expression of NKX6-1 in the VZ subcluster. h. Representative immunohistochemistry image showing SOX9, FOXA2 and NKX6-1 in the VZ of GW19 spinal cord. Immunohistochemistry replicated 4 times in 2 independent samples. i. UMAP plot showing expression of OLIG2 in the VZ subcluster. j. Representative immunohistochemistry image showing SOX9 and OLIG2 in the VZ of GW19 spinal cord. Immunohistochemistry replicated 7 times in 2 independent samples. k. Bubble plot showing expression of ligands and receptors in PPP1R17+ cells of the roof plate (RP) and floor plate (FP). The first 3 genes were differentially expressed in both FP and RP cells relative to midplate cells. The next 20 were differentially expressed in FP relative to RP cells, and the final 20 were differentially expressed in RP relative to FP cells. Genes were identified as ligands or receptors based on the NATMI database. l. Representative immunohistochemistry image showing sites of axon crossing (SMI-312) along the dorsal and ventral midline expressing PPP1R17. Immunohistochemistry replicated 4 times in 2 independent samples. m. Representative immunohistochemistry image showing substance P+ axons at the midline in GW19 spinal cord. Immunohistochemistry replicated 4 times in 2 independent samples. n. Subcluster of ventricular zone (VZ) cells from Delile et al.60. o. Feature plot showing expression of PPP1R17 in Delile et al.60. p. Bubbleplot showing expression of floor plate markers (Shh, Foxa2), roof plate markers (Gdf10, Gdf7) and Ppp1r17 in Delile et al.60. q. Subcluster of ependymal cells from Zeisel et al.50. r. Feature plot showing expression of Ppp1r17 in Zeisel et al.50 ependymal cells. s. Bubbleplot showing expression of floor plate markers (Shh, Foxa2), roof plate markers (Gdf10, Gdf7) and Ppp1r17 in Zeisel et al.50. t. Representative immunohistochemistry image showing Ppp1r17 in mouse embryonic spinal cord. Immunohistochemistry replicated in n = 3. Scale bars: 20 μm (inset in l), 50 μm (f, l, t, inset in m), 100 μm (c, d, h, j), 200 μm (m).

Extended Data Fig. 7 mGPCs are split into the astrocyte and oligodendrocyte lineages in the spinal cord.

a. Highlight of multipotent glial progenitor cells (mGPC) in main UMAP. b. UMAP plots showing expression of EGFR, OLIG2 and ASCL1 in the main UMAP. c. UMAP plots showing expression of astroglia and OPC/Oligo genes in mGPCs within the main UMAP. d. Representative immunohistochemistry images showing EGFR+/OLIG2+ mGPCs that colocalize with the astroglia-specific marker SOX9. Immunohistochemistry replicated 4 times in 2 independent samples. e. Representative immunohistochemistry images showing EGFR+/OLIG2+ mGPCs that colocalize with the oligodendrocyte lineage marker NKX2-2. Immunohistochemistry replicated 4 times in 2 independent samples. Scale bars: 50 μm (insets in d, e), 200 μm (d, e).

Extended Data Fig. 8 Diversity of neurons in the spinal cord.

a. Violin plots showing nCount (left) and nFeature (right) in the neuron subcluster separated by single cell, single nucleus and NeuN-sorted nucleus samples. b. Bar plot showing the percent of single cells and single nuclei in the neuron subcluster. c. UMAP of the neuron subcluster to show single cell, single nucleus and NeuN-sorted nuclei samples separately. d. UMAP of the neuron subcluster colored by the scaled prediction score as computed by Seurat for label transfer annotations from Russ et al.46. e. Representative immunohistochemistry images of neuronal marker SSTR2 in a GW19 spinal cord cryosection. Immunohistochemistry replicated 5 times in 2 independent samples. f. Representative immunohistochemistry images of neuronal marker RORB in a GW19 spinal cord cryosection. Immunohistochemistry replicated 3 times in 2 independent samples. g. Label transfer showing neuronal annotations from Zeisel et al.50 in the neuron subcluster. h. Label transfer showing neuronal annotations from Rosenberg et al.47 in the neuron subcluster. i. Label transfer showing neuronal annotations from Sathyamurthy et al.48 in the neuron subcluster. j. Label transfer showing annotations from Häring et al.51 in the neuron subcluster. k. Label transfer showing annotations from Osseward et al.49 based on cardinal spinal cord neuronal classes in the neuron subcluster. l. Label transfer showing annotations from Osseward et al.49 based on K-means division groups in the neuron subcluster. m. Label transfer showing annotations from Russ et al.46 based on Putative lineage groups in the neuron subcluster. Scale bars: 50 μm (insets in e, f), 100 μm (e, f).

Extended Data Fig. 9 Neuronal diversity and cell types and quality control metrics in the motor neuron subcluster.

a. Representative immunohistochemistry images of CHAT motor neurons in a GW19 spinal cord cryosection. Immunohistochemistry replicated 9 times in 2 independent samples. b. UMAP of the neuron subcluster, colored by motor neuron subtype. c. Violin plots of nCount (left), nFeature (middle) and percent of mitochondrial genes (MT) separated by motor neuron subtype. d. Bar plot showing the percent of single cells, single nuclei and NeuN-sorted nuclei per motor neuron subtype. e. UMAP of the motor neuron subcluster split to show single cell, single nucleus and NeuN-sorted nucleus samples separately. f. UMAP plot showing the scaled prediction scores for the label transfer of Blum et al.53 motor neuron annotations onto the motor neuron subcluster (this study). g. Venn diagrams showing the number of common markers between Blum et al.53 and this study for alpha motor neurons (left) and gamma motor neurons (right). h. Feature plots showing expression of CHODL (left) and NDNF (middle) in the motor neuron subcluster. i. Feature plots showing expression of CHODL (left), NDNF (middle) and CHODL and NDNF combined (right) in the neuron subcluster. j. Representative in situ hybridization of CHAT, CHODL and NDNF in coronal cervical spinal cord cryosections at GW19. Insets show CHAT+ motor neurons that express either the alpha marker CHODL or the gamma marker NDNF. Immunohistochemistry replicated in n = 2 biological replicates. k. Representative in situ hybridization of Chat, Chodl and Ndnf in coronal mouse spinal cord cryosections at P0. Inset shows Chat+ motor neurons that express the alpha marker Chodl but not the gamma marker Ndnf. Immunohistochemistry replicated in n = 4 biological replicates. l. Representative in situ hybridization of Chat, Chodl and Ndnf in coronal mouse spinal cord cryosections at P25. Insets show Chat+ motor neurons that express both the alpha marker Chodl and the gamma marker Ndnf. Immunohistochemistry replicated in n = 3 biological replicates. Scale bars: 10 μm (insets in j), 20 μm (insets in k, l), 50 μm (insets in a), 100 μm (a, j, k, l).

Extended Data Fig. 10 Developing human spinal cord integrated dataset.

a. UMAP showing a subcluster of ventricular zone (VZ) cells in the integrated dataset colored by cell type. b. Feature plot showing expression of PPP1R17 in the integrated dataset. c. Bubble plot showing expression of floor plate markers (SHH, FOXA2), roof plate markers (GDF10, GDF7) and PPP1R17 in the integrated dataset. d. Feature plots showing expression of PPP1R17 in the integrated dataset split by age. e. Representative immunohistochemistry images showing the midline glia marker PPP1R17 and the axonal marker SMI-312 in spinal cord cryosections at GW11. Immunohistochemistry replicated 2 times in 1 independent sample. f. Representative immunohistochemistry image showing EGFR, the astroglia marker SOX9 and the OPC/Oligo lineage marker NKX2-2 in spinal cord cryosections at GW11. Immunohistochemistry replicated 2 times in 1 independent sample. g. Bar plot showing the percent of astroglia clusters in cervical (C), thoracic (T1, T2) and lumbar (L) regions of the spinal cord from this study. h. Bar plot showing the percent of fibrous and protoplasmic astrocyte clusters in C, T and L regions of the spinal cord from this study and Zhang et al.58. i. Bar plot showing fibrous and protoplasmic astrocyte clusters separated by rostro-caudal region in this study only (GW18). j. MA plot showing differential expression between the V. proto2 astrocyte cluster and the rest of the ventral protoplasmic clusters (V. proto 1 and 3) from this study (GW18). Red dots indicate genes in the top one percent of differentially expressed genes by log fold change. k. Heatmaps showing the alpha/gamma/visceral or PGC mean module score per Seurat cluster in the integrated cholinergic neuron dataset for Blum et al.53 (top) and Russ et al.46 (bottom). Module scores were calculated using the ‘AddModuleScore’ function in Seurat with ≤50 genes with P-value < 0.01 and Log2FC > 1 from either alpha, gamma or visceral/PGC clusters from each study. l. UMAP showing the cholinergic neuron subcluster in the integrated dataset showing cell type annotations and Seurat cluster numbers (c#). m. Bar plot showing the percent of cholinergic cells per Seurat cluster in each age group. n. UMAP plots showing motor neurons in Delile et al.60, colored by their assigned clades (left) and sample age (right). o. UMAP plot showing the scaled prediction scores for the label transfer of this study’s motor neuron type annotations onto Delile et al.60 motor neurons. p. Label transfer showing predicted IDs from motor neuron clusters in this study onto Delile et al.60 motor neurons. q. Bar plot showing the percent of predicted identities based on annotations from this study in Delile et al.60 MN clades. r. UMAP plots showing the motor neuron subcluster in Rayon et al.6, colored by Seurat clusters (left) and sample age (right). Approximately, Carnegie stage (CS) 12 corresponds to GW4; CS14 to GW5; CS17 to GW6; and CS19 to GW7. s. UMAP plot showing the scaled prediction scores for the label transfer of this study’s motor neuron type annotations onto Rayon et al.6 motor neurons. t. Label transfer showing predicted IDs from motor neuron clusters in this study onto Rayon et al.6 motor neurons. u. Bar plot showing the percent of predicted identities based on annotations from this study in Rayon et al.6 motor neuron clusters. Scale bars: 50 μm (e, insets in f),100 μm (f).

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Andersen, J., Thom, N., Shadrach, J.L. et al. Single-cell transcriptomic landscape of the developing human spinal cord. Nat Neurosci 26, 902–914 (2023). https://doi.org/10.1038/s41593-023-01311-w

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