Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
07
12
2021
accepted:
20
03
2023
medline:
10
5
2023
pubmed:
25
4
2023
entrez:
24
04
2023
Statut:
ppublish
Résumé
The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.
Identifiants
pubmed: 37095395
doi: 10.1038/s41593-023-01312-9
pii: 10.1038/s41593-023-01312-9
pmc: PMC10166856
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
891-901Informations de copyright
© 2023. The Author(s).
Références
Guérout, N., Li, X. & Barnabé-Heider, F. Cell fate control in the developing central nervous system. Exp. Cell. Res. 321, 77–83 (2014).
doi: 10.1016/j.yexcr.2013.10.003
pubmed: 24140262
Curtis, E. et al. A first-in-human, phase I study of neural stem cell transplantation for chronic spinal cord injury. Cell Stem Cell 22, 941–950 (2018).
doi: 10.1016/j.stem.2018.05.014
pubmed: 29859175
Xu, N. et al. Transplantation of human neural precursor cells reverses syrinx growth in a rat model of post-traumatic syringomyelia. Neurotherapeutics 18, 1257–1272 (2021).
doi: 10.1007/s13311-020-00987-3
pubmed: 33469829
pmcid: 8423938
Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660 (2019).
doi: 10.1016/j.cell.2019.11.025
pubmed: 31835037
Gyllborg, D. et al. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).
doi: 10.1093/nar/gkaa792
pubmed: 32990747
pmcid: 7641728
Zhang, Q. et al. Single-cell analysis reveals dynamic changes of neural cells in developing human spinal cord. EMBO Rep. 22, e52728 (2021).
doi: 10.15252/embr.202152728
pubmed: 34605607
pmcid: 8567249
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).
Marklund, U. et al. Detailed expression analysis of regulatory genes in the early developing human neural tube. Stem Cells Dev. 23, 5–15 (2014).
doi: 10.1089/scd.2013.0309
pubmed: 24007338
Bayer, S. A. & Altman, J. (eds) In Atlas of Human Central Nervous System Development. (CRC Press, 2005).
Bayer, S. A. & Altman, J. (eds) In Atlas of Human Central Nervous System Development. Vol. 4. (CRC Press, 2006).
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).
doi: 10.1038/s42003-020-01247-y
pubmed: 33037292
pmcid: 7547664
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2019).
doi: 10.1038/s41592-019-0631-4
pubmed: 31740815
pmcid: 6949128
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).
doi: 10.1038/s41587-020-0591-3
pubmed: 32747759
Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
doi: 10.1186/s12864-018-4772-0
pubmed: 29914354
pmcid: 6007078
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
doi: 10.1038/s41586-018-0414-6
pubmed: 30089906
pmcid: 6130801
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
doi: 10.1038/nmeth.4463
pubmed: 28991892
pmcid: 5937676
Lu, D. C., Niu, T. & Alaynick, W. A. Molecular and cellular development of spinal cord locomotor circuitry. Front. Mol. Neurosci. 8, 25 (2015).
doi: 10.3389/fnmol.2015.00025
pubmed: 26136656
pmcid: 4468382
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).
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).
doi: 10.1126/science.aam8999
pubmed: 29545511
pmcid: 7643870
Marqués-Torrejón, M. et al. LRIG1 is a gatekeeper to exit from quiescence in adult neural stem cells. Nat. Commun. 12, 2594 (2021).
doi: 10.1038/s41467-021-22813-w
pubmed: 33972529
pmcid: 8110534
Barry, D. & McDermott, K. Differentiation of radial glia from radial precursor cells and transformation into astrocytes in the developing rat spinal cord. Glia 50, 187–197 (2005).
doi: 10.1002/glia.20166
pubmed: 15682427
Marques, S. et al. Transcriptional convergence of oligodendrocyte lineage progenitors during development. Dev. Cell 46, 504–517 (2018).
doi: 10.1016/j.devcel.2018.07.005
pubmed: 30078729
pmcid: 6104814
Li, X. et al. Regenerative potential of ependymal cells for spinal cord injuries over time. EBioMedicine 13, 55–65 (2016).
doi: 10.1016/j.ebiom.2016.10.035
pubmed: 27818039
pmcid: 5264475
Li, X. et al. FoxJ1 regulates spinal cord development and is required for the maintenance of spinal cord stem cell potential. Exp. Cell. Res. 368, 84–100 (2018).
doi: 10.1016/j.yexcr.2018.04.017
pubmed: 29689278
Ghazale, H. et al. RNA profiling of the human and mouse spinal cord stem cell niches reveals an embryonic-like regionalization MSX1
doi: 10.1016/j.stemcr.2019.04.001
Byer, L. et al. A systematic review and meta-analysis of outcomes in pediatric, recurrent ependymoma. J. Neurooncol. 144, 445–452 (2019).
doi: 10.1007/s11060-019-03255-3
pubmed: 31502040
Gojo, J. et al. Single-cell RNA-seq reveals cellular hierarchies and impaired developmental trajectories in pediatric ependymoma. Cancer Cell 38, 44–59 (2020).
doi: 10.1016/j.ccell.2020.06.004
pubmed: 32663469
pmcid: 7479515
Elsamadicy, A. A. et al. Comparison of epidemiology, treatments, and outcomes in pediatric versus adult ependymoma. Neurooncol. Adv. 2, vdaa019 (2020).
pubmed: 32642681
pmcid: 7212900
Milich, L. M. et al. Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. J. Exp. Med. 218, e20210040 (2021).
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).
doi: 10.1038/s41593-020-00795-0
pubmed: 33589834
pmcid: 8016743
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
doi: 10.1016/j.cell.2018.06.021
pubmed: 30096314
pmcid: 6086934
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).
doi: 10.1016/j.celrep.2018.02.003
pubmed: 29466745
pmcid: 5849084
Alkaslasi, M. R. et al. Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord. Nat. Commun. 12, 2471 (2021).
doi: 10.1038/s41467-021-22691-2
pubmed: 33931636
pmcid: 8087807
Russ, D. E. et al. A harmonized atlas of mouse spinal cord cell types and their spatial organization. Nat. Commun. 12, 5722 (2021).
doi: 10.1038/s41467-021-25125-1
pubmed: 34588430
pmcid: 8481483
Pielawski, N. et al. TissUUmaps 3: interactive visualization and quality assessment of large-scale spatial omics data. Preprint at bioRxiv https://doi.org/10.1101/2022.01.28.478131 (2022).
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).
doi: 10.1016/j.cell.2008.02.046
pubmed: 18455991
pmcid: 2394859
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
doi: 10.1016/j.cell.2021.04.048
pubmed: 34062119
pmcid: 8238499
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
doi: 10.1089/omi.2011.0118
pubmed: 22455463
pmcid: 3339379
Van den Berge, K. et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 11, 1201 (2020).
doi: 10.1038/s41467-020-14766-3
pubmed: 32139671
pmcid: 7058077
Chalfoun, J. et al. MIST: accurate and scalable microscopy image stitching tool with stage modeling and error minimization. Sci. Rep. 7, 4988 (2017).
doi: 10.1038/s41598-017-04567-y
pubmed: 28694478
pmcid: 5504007