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Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system

Abstract

Single-cell and single-nucleus genomic approaches can provide unbiased and multimodal insights. Here, we discuss what constitutes a molecular cell atlas and how to leverage single-cell omics data to generate hypotheses and gain insights into cell transitions in development and disease of the nervous system. We share points of reflection on what to consider during study design and implementation as well as limitations and pitfalls.

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Fig. 1: Schematics for building a multimodal molecular atlas.
Fig. 2: Methods for establishing temporal relationships between cells.

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References

  1. Bonev, B. et al. Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01806-0 (2024).

  2. Colonna, M. et al. Implementation and validation of single-cell genomics experiments in neuroscience. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01814-0 (2024).

  3. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

  5. Ecker, J. R. et al. The BRAIN Initiative Cell Census Consortium: lessons learned toward generating a comprehensive brain cell atlas. Neuron 96, 542–557 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Langlieb, J. et al. The molecular cytoarchitecture of the adult mouse brain. Nature 624, 333–342 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  Google Scholar 

  12. Domcke, S. & Shendure, J. A reference cell tree will serve science better than a reference cell atlas. Cell 186, 1103–1114 (2023).

    Article  CAS  PubMed  Google Scholar 

  13. Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183, 935–953 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Gazestani, V. et al. Early Alzheimer’s disease pathology in human cortex involves transient cell states. Cell 186, 4438–4453 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kaya, T. et al. CD8+ T cells induce interferon-responsive oligodendrocytes and microglia in white matter aging. Nat. Neurosci. 25, 1446–1457 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen, X. et al. Microglia-mediated T cell infiltration drives neurodegeneration in tauopathy. Nature 615, 668–677 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nagy, C. et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nat. Neurosci. 23, 771–781 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. 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 

  25. MacLean, A. L., Hong, T. & Nie, Q. Exploring intermediate cell states through the lens of single cells. Curr. Opin. Syst. Biol. 9, 32–41 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sadick, J. S. et al. Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in Alzheimer’s disease. Neuron 110, 1788–1805 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cain, A. et al. Multicellular communities are perturbed in the aging human brain and Alzheimer’s disease. Nat. Neurosci. 26, 1267–1280 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kirschenbaum, D. et al. Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma. Cell 187, 149–165 (2024).

    Article  CAS  PubMed  Google Scholar 

  29. Habib, N. et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat. Neurosci. 23, 701–706 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sun, W. et al. Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory. Nature 627, 374–381 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Olah, M. et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat. Commun. 11, 6129 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kamath, T. et al. Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease. Nat. Neurosci. 25, 588–595 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Malaiya, S. et al. Single-nucleus RNA-seq reveals dysregulation of striatal cell identity due to Huntington’s disease mutations. J. Neurosci. 41, 5534–5552 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Stanley, G., Gokce, O., Malenka, R. C., Sudhof, T. C. & Quake, S. R. Continuous and discrete neuron types of the adult murine striatum. Neuron 105, 688–699 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16, 1126–1137 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hashemiaghdam, A. & Mroczek, M. Microglia heterogeneity and neurodegeneration: the emerging paradigm of the role of immunity in Alzheimer’s disease. J. Neuroimmunol. 341, 577185 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Safaiyan, S. et al. White matter aging drives microglial diversity. Neuron 109, 1100–1117 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Krienen, F. M. et al. A marmoset brain cell census reveals regional specialization of cellular identities. Sci. Adv. 9, eadk3986 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bhaduri, A. et al. An atlas of cortical arealization identifies dynamic molecular signatures. Nature 598, 200–204 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Quesnel-Vallieres, M., Weatheritt, R. J., Cordes, S. P. & Blencowe, B. J. Autism spectrum disorder: insights into convergent mechanisms from transcriptomics. Nat. Rev. Genet. 20, 51–63 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Yang, Q. et al. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief. Bioinform. 21, 1058–1068 (2020).

    Article  CAS  Google Scholar 

  46. Lobentanzer, S., Hanin, G., Klein, J. & Soreq, H. Integrative transcriptomics reveals sexually dimorphic control of the cholinergic/neurokine interface in schizophrenia and bipolar disorder. Cell Rep. 29, 764–777 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ardesch, D. J., Libedinsky, I., Scholtens, L. H., Wei, Y. & van den Heuvel, M. P. Convergence of brain transcriptomic and neuroimaging patterns in schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 8, 630–639 (2023).

    Google Scholar 

  48. Ziffra, R. S. et al. Single-cell epigenomics reveals mechanisms of human cortical development. Nature 598, 205–213 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  50. Jin, X. et al. In vivo Perturb-seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Fleck, J. S. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 365–372 (2023).

    Article  CAS  PubMed  Google Scholar 

  52. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wheeler, M. A. et al. Droplet-based forward genetic screening of astrocyte–microglia cross-talk. Science 379, 1023–1030 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Soldatov, R. et al. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364, eaas9536 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Kastriti, M. E. et al. Schwann cell precursors represent a neural crest-like state with biased multipotency. EMBO J. 41, e108780 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Faure, L., Soldatov, R., Kharchenko, P. V. & Adameyko, I. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data. Bioinformatics 39, btac746 (2023).

    Article  CAS  PubMed  Google Scholar 

  57. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 367, eaaw3381 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Erickson, A. G., Kameneva, P. & Adameyko, I. The transcriptional portraits of the neural crest at the individual cell level. Semin. Cell Dev. Biol. 138, 68–80 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Jin, Z. et al. Single-cell gene fusion detection by scFusion. Nat. Commun. 13, 1084 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gao, R. et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat. Biotechnol. 39, 599–608 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Drokhlyansky, E. et al. The human and mouse enteric nervous system at single-cell resolution. Cell 182, 1606–1622 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Marsh, S. E. et al. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nat. Neurosci. 25, 306–316 (2022).

    Article  CAS  PubMed  Google Scholar 

  67. 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 

  68. Yang, A. C. et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature 603, 885–892 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329 (2017).

    Article  CAS  PubMed  Google Scholar 

  70. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    Article  PubMed  Google Scholar 

  71. Liu, L. et al. Dissociation of microdissected mouse brain tissue for artifact free single-cell RNA sequencing. STAR Protoc. 2, 100590 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Niu, M. et al. Droplet-based transcriptome profiling of individual synapses. Nat. Biotechnol. 41, 1332–1344 (2023).

    Article  CAS  PubMed  Google Scholar 

  73. Perez, J. D. et al. Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons. eLife 10, e63092 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 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 

  76. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).

    Article  CAS  PubMed  Google Scholar 

  78. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. VanHorn, S. & Morris, S. A. Next-generation lineage tracing and fate mapping to interrogate development. Dev. Cell 56, 7–21 (2021).

    Article  CAS  PubMed  Google Scholar 

  80. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  82. Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Bandler, R. C. et al. Single-cell delineation of lineage and genetic identity in the mouse brain. Nature 601, 404–409 (2022).

    Article  CAS  PubMed  Google Scholar 

  84. Delgado, R. N. et al. Individual human cortical progenitors can produce excitatory and inhibitory neurons. Nature 601, 397–403 (2022).

    Article  CAS  PubMed  Google Scholar 

  85. Martik, M. L., Lyons, D. C. & McClay, D. R. Developmental gene regulatory networks in sea urchins and what we can learn from them. F1000Res. 5, F1000 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Durruthy-Durruthy, R. & Heller, S. Applications for single cell trajectory analysis in inner ear development and regeneration. Cell Tissue Res. 361, 49–57 (2015).

    Article  CAS  PubMed  Google Scholar 

  88. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 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 

  90. Qiu, X. et al. Mapping transcriptomic vector fields of single cells. Cell 185, 690–711 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. 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 

  93. Lynch, A. W. et al. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nat. Methods 19, 1097–1108 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Bravo Gonzalez-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat. Methods 20, 1355–1367 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Price, J., Turner, D. & Cepko, C. Lineage analysis in the vertebrate nervous system by retrovirus-mediated gene transfer. Proc. Natl Acad. Sci. USA 84, 156–160 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Walsh, C. & Cepko, C. L. Widespread dispersion of neuronal clones across functional regions of the cerebral cortex. Science 255, 434–440 (1992).

    Article  CAS  PubMed  Google Scholar 

  98. Noctor, S. C., Flint, A. C., Weissman, T. A., Dammerman, R. S. & Kriegstein, A. R. Neurons derived from radial glial cells establish radial units in neocortex. Nature 409, 714–720 (2001).

    Article  CAS  PubMed  Google Scholar 

  99. Yu, Y. C. et al. Preferential electrical coupling regulates neocortical lineage-dependent microcircuit assembly. Nature 486, 113–117 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Li, Y. et al. Clonally related visual cortical neurons show similar stimulus feature selectivity. Nature 486, 118–121 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Ciceri, G. et al. Lineage-specific laminar organization of cortical GABAergic interneurons. Nat. Neurosci. 16, 1199–1210 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, eaat9804 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Golden, J. A., Fields-Berry, S. C. & Cepko, C. L. Construction and characterization of a highly complex retroviral library for lineage analysis. Proc. Natl Acad. Sci. USA 92, 5704–5708 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Harwell, C. C. et al. Wide dispersion and diversity of clonally related inhibitory interneurons. Neuron 87, 999–1007 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Mayer, C. et al. Clonally related forebrain interneurons disperse broadly across both functional areas and structural boundaries. Neuron 87, 989–998 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    Article  CAS  PubMed  Google Scholar 

  108. Leeper, K. et al. Lineage barcoding in mice with homing CRISPR. Nat. Protoc. 16, 2088–2108 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25, 285–294 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Forrow, A. & Schiebinger, G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat. Commun. 12, 4940 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Lange, M. et al. Mapping lineage-traced cells across time points with moslin. Genome Biol. 25, 277 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Esk, C. et al. A human tissue screen identifies a regulator of ER secretion as a brain-size determinant. Science 370, 935–941 (2020).

    Article  CAS  PubMed  Google Scholar 

  114. He, Z. et al. Lineage recording in human cerebral organoids. Nat. Methods 19, 90–99 (2022).

    Article  PubMed  Google Scholar 

  115. Dvoretskova, E. et al. Spatial enhancer activation influences inhibitory neuron identity during mouse embryonic development. Nat. Neurosci. 27, 862–872 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Fasching, L. et al. Early developmental asymmetries in cell lineage trees in living individuals. Science 371, 1245–1248 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Bae, T. et al. Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Science 359, 550–555 (2018).

    Article  CAS  PubMed  Google Scholar 

  119. Bizzotto, S. et al. Landmarks of human embryonic development inscribed in somatic mutations. Science 371, 1249–1253 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Coorens, T. H. H. et al. Extensive phylogenies of human development inferred from somatic mutations. Nature 597, 387–392 (2021).

    Article  CAS  PubMed  Google Scholar 

  121. Rodin, R. E. et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 24, 176–185 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Erwin, J. A. et al. L1-associated genomic regions are deleted in somatic cells of the healthy human brain. Nat. Neurosci. 19, 1583–1591 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Evrony, G. D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Evrony, G. D. et al. Cell lineage analysis in human brain using endogenous retroelements. Neuron 85, 49–59 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Baldassari, S. et al. Dissecting the genetic basis of focal cortical dysplasia: a large cohort study. Acta Neuropathol. 138, 885–900 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Chung, C. et al. Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development. Nat. Genet. 55, 209–220 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Kim, J. H. et al. Ultra-low level somatic mutations and structural variations in focal cortical dysplasia type II. Ann. Neurol. 93, 1082–1093 (2023).

    Article  CAS  PubMed  Google Scholar 

  128. Khoshkhoo, S. et al. Contribution of somatic Ras/Raf/mitogen-activated protein kinase variants in the hippocampus in drug-resistant mesial temporal lobe epilepsy. JAMA Neurol. 80, 578–587 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  129. D’Gama, A. M. et al. Targeted DNA sequencing from autism spectrum disorder brains implicates multiple genetic mechanisms. Neuron 88, 910–917 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Freed, D. & Pevsner, J. The contribution of mosaic variants to autism spectrum disorder. PLoS Genet. 12, e1006245 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Krupp, D. R. et al. Exonic mosaic mutations contribute risk for autism spectrum disorder. Am. J. Hum. Genet. 101, 369–390 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Lim, E. T. et al. Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat. Neurosci. 20, 1217–1224 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Lareau, C. A. et al. Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility. Nat. Protoc. 18, 1416–1440 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Miller, T. E. et al. Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat. Biotechnol. 40, 1030–1034 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    Article  CAS  PubMed  Google Scholar 

  137. Chen, W. et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733–740 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Smith, K. S. et al. Unified rhombic lip origins of group 3 and group 4 medulloblastoma. Nature 609, 1012–1020 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Bhaduri, A. et al. Outer radial glia-like cancer stem cells contribute to heterogeneity of glioblastoma. Cell Stem Cell 26, 48–63 (2020).

    CAS  Google Scholar 

  140. Kanton, S. et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature 574, 418–422 (2019).

    Article  CAS  PubMed  Google Scholar 

  141. Davis, A., Gao, R. & Navin, N. Tumor evolution: linear, branching, neutral or punctuated? Biochim. Biophys. Acta Rev. Cancer 1867, 151–161 (2017).

    Article  CAS  PubMed  Google Scholar 

  142. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Jindal, K. et al. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat. Biotechnol. 42, 946–959 (2024).

    Article  CAS  PubMed  Google Scholar 

  144. Fullgrabe, A. et al. Guidelines for reporting single-cell RNA-seq experiments. Nat. Biotechnol. 38, 1384–1386 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Qiu, C. et al. A single-cell time-lapse of mouse prenatal development from gastrula to birth. Nature 626, 1084–1093 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Arneson, D. et al. Single cell molecular alterations reveal target cells and pathways of concussive brain injury. Nat. Commun. 9, 3894 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Grubman, A. et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat. Neurosci. 22, 2087–2097 (2019).

    Article  CAS  PubMed  Google Scholar 

  149. Lau, S. F., Cao, H., Fu, A. K. Y. & Ip, N. Y. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 117, 25800–25809 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Miller, M. B. et al. Somatic genomic changes in single Alzheimer’s disease neurons. Nature 604, 714–722 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Townsend, S. E. et al. Single-nuclei transcriptomics enable detection of somatic variants in patient brain tissue. Sci. Rep. 13, 527 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Zhou, Y. et al. Molecular landscapes of human hippocampal immature neurons across lifespan. Nature 607, 527–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. BRAIN Initiative Cell Census Network (BICCN), A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).

  154. Pilz, G. A. et al. Live imaging of neurogenesis in the adult mouse hippocampus. Science 359, 658–662 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Paolicelli, R. C. et al. Microglia states and nomenclature: a field at its crossroads. Neuron 110, 3458–3483 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Chhatbar, C. et al. Type I interferon receptor signaling of neurons and astrocytes regulates microglia activation during viral encephalitis. Cell Rep. 25, 118–129 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    Article  CAS  PubMed  Google Scholar 

  158. Quinn, J. J. et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 371, eabc1944 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Simeonov, K. P. et al. Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states. Cancer Cell 39, 1150–1162 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Aivazidis, A. et al. Model-based inference of RNA velocity modules improves cell fate prediction. Preprint at bioRxiv https://doi.org/10.1101/2023.08.03.551650 (2023).

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Acknowledgements

We thank R. Awatramani, H. S. Bateup, B. Bonev, C. R. Cadwell, E. Caglayan, G. Castelo-Branco, F. Chen, J. L. Chen, S. Codeluppi, R. Corces, J. Fan, J. Gillis, G. Green, M. Heiman, K. Harris, F. Inoue, M. Kampmann, M. Kellis, F. Krienen, M. Monje, M. R. O’Dea, R. Patani, A. Pollen, A. Levine, M. Lotfollahi, C. Luo, E. Macosko, S. Marsh, K. R. Maynard, M. Nitzan, F. J. Quintana, V. Ramani, R. Satijia, M. Scavuzzo, L. Schirmer, M. Schmitz, Y. Shen, S. Sloan, N. Sun, P. Tesar, F. Theis, J. Tollkuhn, M. A. Tosches, M. E. Urbanek, X. Wang, J. D. Welch, J. Werner, H. Zeng, B. Tasic and T. Kaya for insightful feedback on this work and J. Kuhl for the illustrations in Fig. 2. G.K. is a Jon Heighten Scholar in Autism Research and holds the Townsend Distinguished Chair in Research on Autism Spectrum Disorders at UT Southwestern. G.K. was partially supported by the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition Scholar Award (220020467), the Simons Foundation for Autism Research Award (947591), and NIH grants (NS126143, HG011641, MH126481, MH134809, NS115821). D.G. was partially supported by NIH funding (R01AG078713). S.A.L. is supported by the NIH (R01EY033353), the Carol and Gene Ludwig Family Foundation, the Collaborative Center for XDP through Massachusetts General Hospital and the Cure Alzheimer’s Fund. Q.M. is a professor and the Chief of the Bioinformatics and Computational Biology Section in the Department of Biomedical Informatics at Ohio State University. This work was partially supported by awards R01GM131399, R21HG012482 and U54AG075931 from the National Institutes of Health. This work was also supported by the Pelotonia Institute of Immuno-Oncology. I.A. is a professor and the Department Chair at the Department of Neuroimmunology at the Center for Brain Research at the Medical University of Vienna, Austria and is a group leader at the Karolinska Institutet in Stockholm, Sweden. I.A. was supported by the Swedish Research Council, the Knut and Alice Wallenberg Foundation, ERC Synergy Grant KILL-OR-DIFFERENTIATE (856529, ERC-2019-SyG), the Austrian Science Fund (FWF) and EMBO Young Investigator Grants, an Alex’s Lemonade Stand Foundation Crazy 8 Initiative Award, the Paradifference Foundation and Cancerfonden. Q.L. is an assistant professor of Neuroscience and Genetics, and J.Y. is a postdoctoral scholar at Washington University in St. Louis School of Medicine. This work was partially supported by the Whitehall Foundation (2021-08-003), a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation and NIH grant R01AG078512 to Q.L. and the German Research Foundation ImmunoSensation (EXC2151-390873048 to O.G.). This work was partially supported by awards R00NS111731 to A.B. and T32GM008243 and a CIRM-BSCRC Postdoctoral Fellowship to P.N., the Esther A. and Joseph Klingenstein Fund (to T.J.N.), the Shurl and Kay Curci Foundation (T.J.N.) and the Sontag Foundation (to T.J.N.) and a gift from the William K. Bowes Jr Foundation. T.J.N. is a New York Stem Cell Foundation Robertson Neuroscience Investigator. C.N.K. is supported by the UCSF Discovery Fellowship. This work was supported by the NIH Director’s New Innovator Award 1DP2 NS127705-01 to M.G.F. T.B. thanks the founder of the Allen Institute, Paul G. Allen, for his vision, encouragement and support. The work was supported by grants from an NDCN Chan Zuckerberg Initiative grant (to O.G.); C.A.W. was supported by the NINDS (NS03245) and the Allen Discovery Center program, a Paul G. Allen Frontiers Group-advised program of the Paul G. Allen Family Foundation. C.A.W. is an investigator at the Howard Hughes Medical Institute.

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Correspondence to Omer Ali Bayraktar, Ozgun Gokce, Naomi Habib, Genevieve Konopka, Shane A. Liddelow or Tomasz J. Nowakowski.

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Adameyko, I., Bakken, T., Bhaduri, A. et al. Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system. Nat Neurosci 27, 2278–2291 (2024). https://doi.org/10.1038/s41593-024-01827-9

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