Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Longitudinal scRNA-seq analysis in mouse and human informs optimization of rapid mouse astrocyte differentiation protocols

Nature Neuroscience volume 26, pages 1726–1738 (2023)Cite this article

Subjects

An Author Correction to this article was published on 23 November 2023

This article has been updated

Abstract

Macroglia (astrocytes and oligodendrocytes) are required for normal development and function of the central nervous system, yet many questions remain about their emergence during the development of the brain and spinal cord. Here we used single-cell/single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) to analyze over 298,000 cells and nuclei during macroglia differentiation from mouse embryonic and human-induced pluripotent stem cells. We computationally identify candidate genes involved in the fate specification of glia in both species and report heterogeneous expression of astrocyte surface markers across differentiating cells. We then used our transcriptomic data to optimize a previous mouse astrocyte differentiation protocol, decreasing the overall protocol length and complexity. Finally, we used multi-omic, dual single-nuclei (sn)RNA-seq/snATAC-seq analysis to uncover potential genomic regulatory sites mediating glial differentiation. These datasets will enable future optimization of glial differentiation protocols and provide insight into human glial differentiation.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: scRNA-seq of human glial differentiation uncovers lineage-specific precursors and transient states.
Fig. 2: scRNA-seq of mouse glial differentiation uncovers lineage-specific precursors and transient states.
Fig. 3: Analysis of subsets of early mouse astrocyte differentiation.
Fig. 4: Comparison of putative cell surface marker gene expression patterns in differentiated and primary cells.
Fig. 5: Multi-omic analysis of two different astrocyte differentiation time series.
Fig. 6: Data integration and functional analyses of differentiated immature astrocytes.

Similar content being viewed by others

Data availability

All raw sequencing data generated in this study are available at the GEO archive under accession GSE211140. Gene expression and pseudobulk data for all timepoints from both species are directly searchable via a custom web app, https://liddelowlab.shinyapps.io/frazel_astro_diff_final/. Previously published datasets are available from the NCBI GEO under accession GSE152223, and from the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession PRJNA637987. Source data are provided with this paper.

Change history

References

  1. Hasel, P., Rose, I. V. L., Sadick, J. S., Kim, R. D. & Liddelow, S. A. Neuroinflammatory astrocyte subtypes in the mouse brain. Nat. Neurosci. 24, 1475–1487 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Guttenplan, K. A. et al. Neurotoxic reactive astrocytes induce cell death via saturated lipids. Nature 599, 102–107 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Stumpf, P. S. et al. Stem cell differentiation as a non-Markov stochastic process. Cell Syst. 5, 268–282 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature 596, 92–96 (2021).

    Article  PubMed  Google Scholar 

  6. Eze, U. C., Bhaduri, A., Haeussler, M., Nowakowski, T. J. & Kriegstein, A. R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia. Nat. Neurosci. 24, 584–594 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kang, P. et al. Sox9 and NFIA coordinate a transcriptional regulatory cascade during the initiation of gliogenesis. Neuron 74, 79–94 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Matuzelski, E. et al. Transcriptional regulation of Nfix by NFIB drives astrocytic maturation within the developing spinal cord. Dev. Biol. 432, 286–297 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Sun, W. et al. SOX9 is an astrocyte-specific nuclear marker in the adult brain outside the neurogenic regions. J. Neurosci. 37, 4493–4507 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Fabra-Beser, J. et al. Differential expression levels of Sox9 in early neocortical radial glial cells regulate the decision between stem cell maintenance and differentiation. J. Neurosci. 41, 6969–6986 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hasel, P. et al. Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism. Nat. Commun. 8, 15132 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sloan, S. A. & Barres, B. A. Mechanisms of astrocyte development and their contributions to neurodevelopmental disorders. Curr. Opin. Neurobiol. 27, 75–81 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gallo, V. & Deneen, B. Glial development: the crossroads of regeneration and repair in the CNS. Neuron 83, 283–308 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lozzi, B., Huang, T. W., Sardar, D., Huang, A. Y. S. & Deneen, B. Regionally distinct astrocytes display unique transcription factor profiles in the adult brain. Front. Neurosci. 14, 61 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Welle, A. et al. Epigenetic control of region-specific transcriptional programs in mouse cerebellar and cortical astrocytes. Glia 69, 2160–2177 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Ohlig, S. et al. Molecular diversity of diencephalic astrocytes reveals adult astrogenesis regulated by Smad4. EMBO J. 40, e107532 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bayraktar, O. A. et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 23, 500–509 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rubenstein, J., Rakic, P., Chen, B. & Kwan, K. Y. (eds.) Patterning and Cell Type Specification in the Developing CNS and PNS 2nd edn (Elsevier Inc., 2020).

  20. Barbar, L. et al. CD49f is a novel marker of functional and reactive human iPSC-derived astrocytes. Neuron 107, 436–453 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sardar, D. et al. Mapping astrocyte transcriptional signatures in response to neuroactive compounds. Int. J. Mol. Sci. 22, 3975 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Thompson, R. E. et al. Different mixed astrocyte populations derived from embryonic stem cells have variable neuronal growth support capacities. Stem. Cells Dev. 26, 1597–1611 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lattke, M. et al. Extensive transcriptional and chromatin changes underlie astrocyte maturation in vivo and in culture. Nat. Commun. 12, 4335 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tiwari, N. et al. Stage-specific transcription factors drive astrogliogenesis by remodeling gene regulatory landscapes. Cell Stem Cell 23, 557–571 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Paull, D. et al. Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells. Nat. Methods 12, 885–892 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Crowell, H. L. et al. Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE 9, e98679 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  30. 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 

  31. Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gratton, M.-O. et al. Hes6 promotes cortical neurogenesis and inhibits Hes1 transcription repression activity by multiple mechanisms. Mol. Cell. Biol. 23, 6922–6935 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Park, N. I. et al. ASCL1 reorganizes chromatin to direct neuronal fate and suppress tumorigenicity of glioblastoma stem cells. Cell Stem Cell 21, 209–224 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Ge, W. P., Miyawaki, A., Gage, F. H., Jan, Y. N. & Jan, L. Y. Local generation of glia is a major astrocyte source in postnatal cortex. Nature 484, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bronstein, R., Kyle, J., Abraham, A. B. & Tsirka, S. E. Neurogenic to gliogenic fate transition perturbed by loss of HMGB2. Front. Mol. Neurosci. 10, 153 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fu, Y. et al. Heterogeneity of glial progenitor cells during the neurogenesis-to-gliogenesis switch in the developing human cerebral cortex. Cell Rep. 34, 108788 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Guerra San Juan, I. et al. Loss of mouse Stmn2 function causes motor neuropathy. Neuron 110, 1671–1688 (2022).

    Article  CAS  PubMed  Google Scholar 

  39. Fragkouli, A. et al. Neuronal ELAVL proteins utilize AUF-1 as a co-partner to induce neuron-specific alternative splicing of APP. Sci. Rep. 7, 44507 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kim, J. et al. Ttyh1 regulates embryonic neural stem cell properties by enhancing the Notch signaling pathway. EMBO Rep. 19, e45472 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Jung, E. et al. Tweety-homolog 1 drives brain colonization of gliomas. J. Neurosci. 37, 6837–6850 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Liu, J., Wu, X. & Lu, Q. Molecular divergence of mammalian astrocyte progenitor cells at early gliogenesis. Development 149, dev199985 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gao, S., Dai, Y. & Rehman, J. A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes. Genome Res. 31, 1296–1311 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yang, R. et al. POU2F2 regulates glycolytic reprogramming and glioblastoma progression via PDPK1-dependent activation of PI3K/AKT/mTOR pathway. Cell Death Dis. 12, 433 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. O’Sullivan, M. L. et al. Astrocytes follow ganglion cell axons to establish an angiogenic template during retinal development. Glia 65, 1697–1716 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Samyesudhas, S. J., Roy, L. & Cowden Dahl, K. D. Differential expression of ARID3B in normal adult tissue and carcinomas. Gene 543, 174–180 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Lanjakornsiripan, D. et al. Layer-specific morphological and molecular differences in neocortical astrocytes and their dependence on neuronal layers. Nat. Commun. 9, 1623 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Chamling, X. et al. Single-cell transcriptomic reveals molecular diversity and developmental heterogeneity of human stem cell-derived oligodendrocyte lineage cells. Nat. Commun. 12, 652 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Aydin, B. et al. Proneural factors Ascl1 and Neurog2 contribute to neuronal subtype identities by establishing distinct chromatin landscapes. Nat. Neurosci. 22, 897–908 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Janesick, A., Wu, S. C. & Blumberg, B. Retinoic acid signaling and neuronal differentiation. Cell. Mol. Life Sci. 72, 1559–1576 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Zamanian, J. L. et al. Genomic analysis of reactive astrogliosis. J. Neurosci. 32, 6391–6410 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bergen, V., Soldatov, R. A., Kharchenko, P. V. & Theis, F. J. RNA velocity—current challenges and future perspectives. Mol. Syst. Biol. 17, e10282 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sakers, K. et al. Loss of Quaking RNA binding protein disrupts the expression of genes associated with astrocyte maturation in mouse brain. Nat. Commun. 12, 1537 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Elsafadi, M. et al. Transgelin is a TGFβ-inducible gene that regulates osteoblastic and adipogenic differentiation of human skeletal stem cells through actin cytoskeleston organization. Cell Death Dis. 7, e2321 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yu, H. et al. Transgelin is a direct target of TGF‐β/Smad3‐dependent epithelial cell migration in lung fibrosis. FASEB J. 22, 1778–1789 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Cole, A. E., Murray, S. S. & Xiao, J. Bone morphogenetic protein 4 signalling in neural stem and progenitor cells during development and after injury. Stem Cells Int. 2016, 9260592 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Labib, D. et al. Proteomic alterations and novel markers of neurotoxic reactive astrocytes in human induced pluripotent stem cell models. Front. Mol. Neurosci. 15, 870085 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kantzer, C. G. et al. ACSA-2 and GLAST classify subpopulations of multipotent and glial-restricted cerebellar precursors. J. Neurosci. Res. 99, 2228–2249 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. 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 

  61. Yanagisawa, H., Schluterman, M. K. & Brekken, R. A. Fibulin-5, an integrin-binding matricellular protein: its function in development and disease. J. Cell Commun. Signal. 3, 337–347 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bayraktar, O. A., Fuentealba, L. C., Alvarez-Buylla, A. & Rowitch, D. H. Astrocyte development and heterogeneity. Cold Spring Harb. Perspect. Biol. 7, a020362 (2015).

    Article  PubMed Central  Google Scholar 

  63. Isbel, L., Grand, R. S. & Schübeler, D. Generating specificity in genome regulation through transcription factor sensitivity to chromatin. Nat. Rev. Genet. 23, 728–740 (2022).

    Article  CAS  PubMed  Google Scholar 

  64. Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimers Dis. 64, S161–S189 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Barbar, L., Rusielewicz, T., Zimmer, M., Kalpana, K. & Fossati, V. Isolation of human CD49f+ astrocytes and in vitro iPSC-based beurotoxicity assays. STAR Protoc. 1, 100172 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lagomarsino, V. N. et al. Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. Neuron 109, 3402–3420 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Pinglay, S. et al. Synthetic regulatory reconstitution reveals principles of mammalian Hox cluster regulation. Science (1979) 377, eabk2820 (2022).

    CAS  Google Scholar 

  68. Brosh, R. et al. A versatile platform for locus-scale genome rewriting and verification. Proc. Natl Acad. Sci. USA 118, e2023952118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Tinevez, J. Y. et al. TrackMate: an open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017).

    Article  CAS  PubMed  Google Scholar 

  70. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The computational requirements for this work were supported in part by the NYU Langone High Performance Computing (HPC) Core’s resources and personnel. Funding for this work was provided by NIH/NEI (R01EY033353), the Cure Alzheimer’s Fund, Anderson Neurodegeneration Consortium, Anonymous Donors, the Blas Frangione Foundation, The Alzheimer’s Association (to S.A.L.), the Dark Matter Project funded by an NIH Center for Excellence in Genome Science (grants 5RM1HG009491, 3RM1HG009491-03S1 and 3RM1HG009491-03S2 to J.D.B.), National Institute of Neurological Disorders and Stroke (NINDS; grant 1R21NS111186 to V.F.) and NINDS T32 (NYU/Dasen) 5T32NS086750 (to P.W.F.). This investigation was supported (in part) by a grant from the National Multiple Sclerosis Society to S.A.L. S.A.L. also acknowledges the generous support of the Parekh Center for Interdisciplinary Neurology at NYU Grossman School of Medicine, and of P. Slavick. We thank the study participants in ROSMAP and David Bennett and Rush University for the use of ROSMAP iPSC lines. ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917. U01AG46152, U01AG61356. ROSMAP resources can be requested at https://www.radc.rush.edu.

Author information

Authors and Affiliations

  1. Neuroscience Institute, NYU Grossman School of Medicine, New York City, NY, USA

    Paul W. Frazel, Theodore Fisher, Anne Marchildon & Shane A. Liddelow

  2. The New York Stem Cell Foundation Research Institute, New York City, NY, USA

    David Labib, Nicolette Pirjanian & Valentina Fossati

  3. Institute for Systems Genetics, NYU Grossman School of Medicine, New York City, NY, USA

    Ran Brosh & Jef D. Boeke

  4. Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York City, NY, USA

    Jef D. Boeke

  5. Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA

    Jef D. Boeke

  6. Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

  7. Department of Ophthalmology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

  8. Parekh Center for Interdisciplinary Neurology, NYU Grossman School of Medicine, New York City, NY, USA

    Shane A. Liddelow

Authors
  1. Paul W. Frazel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Labib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Theodore Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ran Brosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicolette Pirjanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anne Marchildon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jef D. Boeke
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Valentina Fossati
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shane A. Liddelow
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.W.F., V.F. and S.A.L. planned the experiments. P.W.F., D.L., T.F., A.M., N.P. and R.B. completed the experiments. P.W.F. and T.F. helped with data analysis. J.D.B., V.F. and S.A.L. obtained funding for this study. P.W.F. and S.A.L. wrote the manuscript. All authors contributed to discussions and critical review of the manuscript.

Corresponding authors

Correspondence to Paul W. Frazel or Shane A. Liddelow.

Ethics declarations

Competing interests

S.A.L. is an academic founder and sits on the SAB of AstronauTx Ltd. and is a SAB member of the BioAccess Fund. J.D.B. is a founder and director of CDI Labs Inc., a founder of and consultant to Neochromosome Inc., a founder member of SAB, a consultant to ReOpen Diagnostics LLC and serves or served on the SAB of the following: Logomix, Inc., Sangamo Inc., Modern Meadow Inc., Rome Therapeutics Inc., Sample6 Inc., Tessera Therapeutics Inc. and the Wyss Institute. The remaining authors declare no competing interests. V.F. is listed as inventor on issued and pending patent claims covering glial cell generation methods.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Differentiation overview and scRNAseq analysis metrics.

a) Detailed human differentiation overview, with phase contrast micrographs of cells at the timepoints analyzed via scRNAseq. Representative images from 3 independent differentiations. b) Table of quality control processing statistics for the data produced in this study and for the reanalyzed data from20. c) Number of genes identified for each cell and number of unique molecular identifier data for each cell. d) Harmony integration of two independently harvested and processed samples from the same human timepoint (30day) shows no evidence of batch effect. SB = SB431542, LDN = LDN193189; SAG = smoothened agonist; RA = retinoic acid; AA = ascorbic acid.

Extended Data Fig. 2 Survey of differentiation cell type heterogeneity across 9 iPSC lines.

a) UMAP of 128,839 nuclei from 9 different iPSC lines (see Methods for line information). b) Clustering of nuclei from a based on gene expression. c) Cell type proportion for each cluster identified in b) for each of the 9 iPSC lines tested suggest broad representation of each cell type in each iPSC line. d) Dot plot for the top 2 genes enriched in astrocytes from 9 different iPSC lines; differential expression analysis performed using the Wilcoxon ranked-sum test via Seurat. e) Normalized expression of STMN2 and TTYH1 shows similar expression of both genes across all 9 cell lines. f) One well of differentiated cells from each of the 9 lines were immunostained in parallel for cell type marker genes: GFAP (astrocytes; green), MAP2 (neurons; red), and SOX10 (oligodendrocytes; yellow) plus Hoescht 33342 nuclear stain (blue). Representative image from 2 independent wells.

Extended Data Fig. 3 Cell type feature plots.

a) Feature maps for neuron cell-type marker genes (SNAP25, STMN2, SYT1). b) Feature maps for astrocyte cell-type marker genes (CLU, ID3, SPARC). c) Feature maps for oligodendrocyte cell-type marker genes (MBP, MOG, OLIG1). d) Dot plot of top enriched genes for each cluster as determined by Scanpy (clusters correspond to those numbered in Fig. 1d). e) Number of cells per cell-type at each time point.

Extended Data Fig. 4 Reclustering and analysis of transient states in human glial differentiation.

a) FLE reduction of the same data from Fig. 1, reclustered at higher resolution (1.2). Marker genes for each of the clusters of interest are listed in the table to the right (see Results for further discussion), and the clusters are labeled with arrows on the FLE plot. b) Feature maps for mitotic scores from Scanpy70 (‘Scanpy growth rates’) and from scVelo30 (‘S phase score’ and ‘G2M phase score’). c) Feature maps for genes enriched in shared neuron/astrocyte precursors (NHLH1, DLL3, EGFR). d) Feature maps for genes enriched in astrocyte/oligodendrocyte precursors (OLIG2, ASCL1).

Extended Data Fig. 5 Inferred transcription factor (TF) activity in differentiating human glia detected using BITFAM.

a) Heatmap of BITFAM detected activities for each cluster from Fig. 1c (all human data timepoints). Clusters are numbered and labeled on the x-axis, and detected TFs are on the y-axis. Pink arrowheads: TFs with gene expression plotted below, green arrowheads: TFs with gene expression plotted in earlier supplemental figures. b) gene expression plots for genes highlighted with purple arrowheads in the BITFAM heatmap from a.

Extended Data Fig. 6 Differentiation overview and scRNAseq analysis metrics.

a) Mouse differentiation overview, with comparison across protocols. Quality control and pre/post filtering numbers for each mouse timepoint from the various protocols in table. b–d) Micrographs of cells during the embryoid body (EB), adherent differentiation (AD), and astrocyte growth factor stages. e, f) GFAP staining after protocol including EB stage. As expected based on published results22, (e) minimal GFAP staining is visible following treatment with FGF and BMP4 but (f) robust staining is visible after CNTF treatment. All images are representative from 3 independent differentiations g) Harmony integration of two independently harvested and processed samples from the same mouse timepoint (2 day EB) shows no evidence of batch effect. h) Number of genes identified for each cell and i) number of unique molecular identifier data for each cell from each timepoint.

Extended Data Fig. 7 Cell type feature plots.

a) Feature maps for neuron cell-type marker genes (Dcx, Tubb3, Syt1). b) Feature maps for astrocyte cell-type marker genes (Vim, Sparcl1, Clu). c) Feature maps for putative astrocyte precursor genes (Nhlh1, Nhlh2, Hmga2). d) Scanpy growth were calculated for each cell.

Extended Data Fig. 8 Multiomics quality control and genomic accessibility analyses.

a) Violin plots of genes and counts per nucleus (top row), ATAC counts pre/post filter (middle row), and nucleosome signal and transcription start site enrichment scores (bottom row) for each timepoint. b) Table of each sample included in dataset analyzed for Fig. 5. Nuclei were filtered based on the following cutoffs (see Methods): nCount_ATAC < 120000, nCount_ATAC > 500, nCount_RNA < 50000, nCount_RNA > 500, TSS.enrichment > 1. c) Pseudotime calculated based on the stochastic model of RNA velocity. d) Confidence in RNA velocity calculated for each cell based on local coherence of velocity vectors. e) Annotated coverage plot for Fabp7 locus. See Fig. 5f legend for detailed description of plot. f, g) Transcription factor motifs detected as enriched in peaks that were differentially accessible in BMP4/FGF1 (f) or CNTF (g) conditions. h) Approach for peak deletion for multiomic validation. i) Agarose gel demonstrating successful deletion of peak #2 and replacement with single-stranded oligo-donor nucleotide (ssODN) template. j) No differences in S100a6 gene expression between the unedited (Ai17iCre) and the edited, peak #2 deleted (∆p2) mESC lines as measured by quantitative PCR. N = 6 biologically independent wells from 2 separate differentiations, two-sided t-test with no multiple comparisons, data are plotted as mean ± s.e.m.

Source data

Extended Data Fig. 9 Analysis of gene expression final timepoint from each growth factor time series.

a) (Left) Dimensional reduction of gene expression data from final timepoints. (Right) Top 10 genes positively enriched in cells belonging to one growth factor versus the other, as calculated in Scanpy. b) Louvain clustering of data from (a) identifies multiple clusters of cells based on gene expression that are either shared by both growth factor conditions (clusters 0,4), unique to BMP4/FGF1 condition (clusters 3,6), or unique to the CNTF condition (clusters 1,2). Top 10 marker genes for the above clusters are listed to the right. c–e) Validation of gene expression results with S100A6 immunostaining in cells from the final CNTF timepoint. Representative images from 3 independent differentiations.

Extended Data Fig. 10 Primary atlas integration, marker genes, and functional testing quality controls.

a) Cell class annotations were plotted from cell atlas metadata, and plotted for all 93,894 cells from the atlas. b) Cells were initially clustered at low resolution (0.1); reprint of Fig. 6c for ease of comparison. c) Cells were clustered at higher resolution (0.3). d) Top marker genes for select clusters (dashed lines) from Extended Data Fig. 10b,c that contain both primary cells and differentiated cells. Marker genes for subclusters emerging from either Cluster 3 (left) or Cluster 4 (right) as part of re-clustering at higher resolution. e, f) Pie chart of percentage of cells from the BMP or CNTF 9 day timepoints (BMP: 7445 total, CNTF: 8361 total) that belong to each cluster from (b). g) Feature plots for major marker genes in panel (d). h) Top: Still image from Supplementary Video 1 with example of scratch quantification. Bottom: Graph of scratch closure over time from one well. i) Graphical summary of findings from the human datasets (top) and mouse datasets (bottom).

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Video 1

Scratch repair time-lapse video.

Source data

Source Data Extended Data Fig. 8

Unprocessed genotyping gel.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Frazel, P.W., Labib, D., Fisher, T. et al. Longitudinal scRNA-seq analysis in mouse and human informs optimization of rapid mouse astrocyte differentiation protocols. Nat Neurosci 26, 1726–1738 (2023). https://doi.org/10.1038/s41593-023-01424-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-023-01424-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing