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Cell-type-resolved mosaicism reveals clonal dynamics of the human forebrain


Debate remains around the anatomical origins of specific brain cell subtypes and lineage relationships within the human forebrain1,2,3,4,5,6,7. Thus, direct observation in the mature human brain is critical for a complete understanding of its structural organization and cellular origins. Here we utilize brain mosaic variation within specific cell types as distinct indicators for clonal dynamics, denoted as cell-type-specific mosaic variant barcode analysis. From four hemispheres and two different human neurotypical donors, we identified 287 and 780 mosaic variants, respectively, that were used to deconvolve clonal dynamics. Clonal spread and allele fractions within the brain reveal that local hippocampal excitatory neurons are more lineage-restricted than resident neocortical excitatory neurons or resident basal ganglia GABAergic inhibitory neurons. Furthermore, simultaneous genome transcriptome analysis at both a cell-type-specific and a single-cell level suggests a dorsal neocortical origin for a subgroup of DLX1+ inhibitory neurons that disperse radially from an origin shared with excitatory neurons. Finally, the distribution of mosaic variants across 17 locations within one parietal lobe reveals that restriction of clonal spread in the anterior–posterior axis precedes restriction in the dorsal–ventral axis for both excitatory and inhibitory neurons. Thus, cell-type-resolved somatic mosaicism can uncover lineage relationships governing the development of the human forebrain.

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Fig. 1: Comprehensive cMVBA identifies cell-type-resolved and region-specific mosaic variants.
Fig. 2: Human hippocampal lineage diverges from the cortex and basal ganglia.
Fig. 3: Clonal dynamics of cortical excitatory and inhibitory neurons.
Fig. 4: Single-nuclear MPAS incorporating single-nucleus RNA sequencing supports the existence of dorsally derived cortical inhibitory neurons in humans.
Fig. 5: Earlier establishment of the anterior–posterior axis compared with the dorsal–ventral RCS within a cortical lobe.

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Data availability

Raw WGS and MPAS and snMPAS are available through the Sequence Read Archive (accession number PRJNA799597) and NDA (accession number study 919) for ID01 and ID05. The 300× WGS panel of normal is available on the Sequence Read Archive (accession number PRJNA660493). human_g1k_v37 reference: gnomAD: Human multiple cortical areas SMART-seq dataset:

Code availability

Details and codes for the data processing and annotation are provided on GitHub (


  1. Anderson, S. A., Eisenstat, D. D., Shi, L. & Rubenstein, J. L. Interneuron migration from basal forebrain to neocortex: dependence on Dlx genes. Science 278, 474–476 (1997).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Letinic, K., Zoncu, R. & Rakic, P. Origin of GABAergic neurons in the human neocortex. Nature 417, 645–649 (2002).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Wonders, C. P. & Anderson, S. A. The origin and specification of cortical interneurons. Nat. Rev. Neurosci. 7, 687–696 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Petanjek, Z., Berger, B. & Esclapez, M. Origins of cortical GABAergic neurons in the cynomolgus monkey. Cereb. Cortex 19, 249–262 (2009).

    Article  PubMed  Google Scholar 

  5. Hansen, D. V. et al. Non-epithelial stem cells and cortical interneuron production in the human ganglionic eminences. Nat. Neurosci. 16, 1576–1587 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Andrews, M. G. et al. LIF signaling regulates outer radial glial to interneuron fate during human cortical development. Cell Stem Cell 30, 1382–1391.e5 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bulfone, A. et al. Spatially restricted expression of Dlx-1, Dlx-2 (Tes-1), Gbx-2, and Wnt-3 in the embryonic day 12.5 mouse forebrain defines potential transverse and longitudinal segmental boundaries. J. Neurosci. 13, 3155–3172 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Puelles, L. & Rubenstein, J. L. Forebrain gene expression domains and the evolving prosomeric model. Trends Neurosci. 26, 469–476 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Furuta, Y., Piston, D. W. & Hogan, B. L. Bone morphogenetic proteins (BMPs) as regulators of dorsal forebrain development. Development 124, 2203–2212 (1997).

    Article  CAS  PubMed  Google Scholar 

  11. Grove, E. A., Tole, S., Limon, J., Yip, L. & Ragsdale, C. W. The hem of the embryonic cerebral cortex is defined by the expression of multiple Wnt genes and is compromised in Gli3-deficient mice. Development 125, 2315–2325 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Monuki, E. S., Porter, F. D. & Walsh, C. A. Patterning of the dorsal telencephalon and cerebral cortex by a roof plate-Lhx2 pathway. Neuron 32, 591–604 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  14. 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 

  15. Dang, H. et al. Monoclonal antibody specific to acid phosphatase isoenzyme 4. Prostate 9, 47–55 (1986).

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Breuss, M. W. et al. Somatic mosaicism reveals clonal distributions of neocortical development. Nature 604, 689–696 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Park, S. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597, 393–397 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Rakic, P. Mode of cell migration to the superficial layers of fetal monkey neocortex. J. Comp. Neurol. 145, 61–83 (1972).

    Article  CAS  PubMed  Google Scholar 

  20. Kriegstein, A. R. & Noctor, S. C. Patterns of neuronal migration in the embryonic cortex. Trends Neurosci. 27, 392–399 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Rakic, P. Evolution of the neocortex: a perspective from developmental biology. Nat. Rev. Neurosci. 10, 724–735 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wichterle, H., Turnbull, D. H., Nery, S., Fishell, G. & Alvarez-Buylla, A. In utero fate mapping reveals distinct migratory pathways and fates of neurons born in the mammalian basal forebrain. Development 128, 3759–3771 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Ma, T. et al. Subcortical origins of human and monkey neocortical interneurons. Nat. Neurosci. 16, 1588–1597 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Arshad, A. et al. Extended production of cortical interneurons into the third trimester of human gestation. Cereb. Cortex 26, 2242–2256 (2016).

    Article  PubMed  Google Scholar 

  25. Alzu’bi, A. et al. The transcription factors COUP-TFI and COUP-TFII have distinct roles in arealisation and GABAergic interneuron specification in the early human fetal relencephalon. Cereb. Cortex 27, 4971–4987 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Alzu’bi, A. et al. Distinct cortical and sub-cortical neurogenic domains for GABAergic interneuron precursor transcription factors NKX2.1, OLIG2 and COUP-TFII in early fetal human telencephalon. Brain Struct. Funct. 222, 2309–2328 (2017).

    Article  PubMed  Google Scholar 

  27. Marks, J. R. et al. Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms. Preprint at bioRxiv (2023).

  28. Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  29. 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 

  30. Ernst, A. et al. Neurogenesis in the striatum of the adult human brain. Cell 156, 1072–1083 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ju, Y. S. et al. Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543, 714–718 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ginhoux, F. & Garel, S. The mysterious origins of microglia. Nat. Neurosci. 21, 897–899 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Gilbert, E., Shanmugam, A. & Cavalleri, G. L. Revealing the recent demographic history of Europe via haplotype sharing in the UK Biobank. Proc. Natl Acad. Sci. USA 119, e2119281119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Diaz-Papkovich, A., Anderson-Trocme, L. & Gravel, S. A review of UMAP in population genetics. J. Hum. Genet. 66, 85–91 (2021).

    Article  PubMed  Google Scholar 

  38. Xu, Q., Cobos, I., De La Cruz, E., Rubenstein, J. L. & Anderson, S. A. Origins of cortical interneuron subtypes. J. Neurosci. 24, 2612–2622 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Miyoshi, G. et al. Genetic fate mapping reveals that the caudal ganglionic eminence produces a large and diverse population of superficial cortical interneurons. J. Neurosci. 30, 1582–1594 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Oliver, G. et al. Prox 1, a prospero-related homeobox gene expressed during mouse development. Mech. Dev. 44, 3–16 (1993).

    Article  CAS  PubMed  Google Scholar 

  44. LaBonne, C. & Bronner-Fraser, M. Neural crest induction in Xenopus: evidence for a two-signal model. Development 125, 2403–2414 (1998).

    Article  CAS  PubMed  Google Scholar 

  45. Saint-Jeannet, J. P., He, X., Varmus, H. E. & Dawid, I. B. Regulation of dorsal fate in the neuraxis by Wnt-1 and Wnt-3a. Proc. Natl Acad. Sci. USA 94, 13713–13718 (1997).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Faure, S., de Santa Barbara, P., Roberts, D. J. & Whitman, M. Endogenous patterns of BMP signaling during early chick development. Dev. Biol. 244, 44–65 (2002).

    Article  CAS  PubMed  Google Scholar 

  47. Stuhlmiller, T. J. & Garcia-Castro, M. I. Current perspectives of the signaling pathways directing neural crest induction. Cell. Mol. Life Sci. 69, 3715–3737 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bingman, V. P., Salas, C. & Rodriguez, F. in Encyclopedia of Neuroscience (eds Binder, M. D., Hirokawa, N. & Windhorst, U.) 1356–1360 (Springer Berlin Heidelberg, 2009).

  49. Grillner, S., Robertson, B. & Stephenson-Jones, M. The evolutionary origin of the vertebrate basal ganglia and its role in action selection. J. Physiol. 591, 5425–5431 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Stephenson-Jones, M., Samuelsson, E., Ericsson, J., Robertson, B. & Grillner, S. Evolutionary conservation of the basal ganglia as a common vertebrate mechanism for action selection. Curr. Biol. 21, 1081–1091 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Sepulveda, W., Sepulveda, F., Schonstedt, V., Stern, J. & Diaz-Serani, R. Neuroimaging findings in fetal hemimegalencephaly: case study and review. Fetal Diagn. Ther. (2023).

  52. Chung, C. et al. shishenyxx/Human_Inhibitory_Neurons: 1.0.1. Zenodo (2024).

  53. Huang, A. Y. et al. MosaicHunter: accurate detection of postzygotic single-nucleotide mosaicism through next-generation sequencing of unpaired, trio, and paired samples. Nucleic Acids Res. 45, e76 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Breuss, M. W. et al. Autism risk in offspring can be assessed through quantification of male sperm mosaicism. Nat. Med. 26, 143–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Yang, X. et al. Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nat. Biotechnol. 41, 870–877 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Dou, Y. et al. Accurate detection of mosaic variants in sequencing data without matched controls. Nat. Biotechnol. 38, 314–319 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  61. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gonzalez-Pena, V. et al. Accurate genomic variant detection in single cells with primary template-directed amplification. Proc. Natl Acad. Sci. USA (2021).

  63. Lee, J. et al. Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures. Nucleic Acids Res. 46, W102–W108 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular Evolutionary Genetics Analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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We thank the individuals who donate their bodies and tissues for the advancement of research; T. Komiyama for feedback; and the San Diego Supercomputer Center (grant no. TG-IBN190021 to X.Y. and J.G.G.) for computational help. This work was supported by the National Institute of Mental Health (NIMH) (grants U01MH108898 and R01MH124890 to J.G.G. and R21MH134401 to X.Y. and J.C.M.S.), the Larry L. Hillblom Foundation Grant (to J.G.G.), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (grant K99HD111686 to X.Y.), a 2021 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (30598 to C.C.) and the Rady Children’s Institute for Genomic Medicine. This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a US National Institutes of Health SIG grant (no. S10OD026929 to C.C., X.Y. and J.G.G.). We are grateful to C. Fine, M. Espinoza and M. Banihassan (UCSD) for technical assistance with flow cytometry experiments, supported by the UCSD Stem Cell Program and a CIRM Major Facilities grant (FA1-00607) to the Sanford Consortium for Regenerative Medicine. This publication includes data generated at the UCSD Human Embryonic Stem Cell Core Facility, using the BD Biosciences Influx, FACS Aria Fusion and FACS Aria II Flow Cytometry Sorters. Images in Fig. 1 and Extended Data Fig. 1 were created and modified using BioRender (

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Authors and Affiliations



C.C., X.Y. and J.G.G. designed the study. C.C., X.Y., R.F.H., K.I.V., C.B., V.S., S.M., M.W.B., J.C.M.S., S.T.B., G.N. and S.F.K. organized, handled and sequenced human samples. C.C., X.Y. and K.K. performed the ResolveOME experiment. C.C. and Y.L. performed the MFNS experiment. C.C., X.Y., A.P. and R.N. performed the bioinformatics and data analyses. C.C., X.Y. and J.G.G. wrote the manuscript. All authors reviewed the manuscript. C.C. and X.Y. contributed equally to this work.

Corresponding author

Correspondence to Joseph G. Gleeson.

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K.K. is a senior scientist at Bioskryb Genomics Inc. All other authors declare no competing interests.

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Nature thanks Young Seok Ju and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Tissues collected from ID01 and ID05.

Red dots indicate approximate sites of punch biopsies. Abbreviations: PF, prefrontal cortex; F, frontal cortex; P, parietal cortex; O, occipital cortex; T, temporal cortex; I, insular cortex; Cb, Cerebellum; CC, cingulate cortex; mOC, medial occipital cortex; Cau, Caudate; Put, Putamen; Thal, Thalamus; GP, globus pallidus; EC, entorhinal cortex; HIP, hippocampus; AMG, amygdala; POA, preoptic area; Cl, Claustrum. Graphics were created with BioRender (

Extended Data Fig. 2 Bisulfite sequencing in sorted TBR1+ and DLX1+ nuclear pools correlate with excitatory and inhibitory neuron methylome signatures.

(a-c) MFNS gating strategy on 30,000 single brain nuclei using DLX1, TBR1, and COUPTFII antibodies. X-axis: 488 channel intensity for monitoring autofluorescence signals. Y axis: Fluorescence intensity from antigen-bound antibodies. (d) snRNA-seq of post-MFNS nuclei confirming enrichment of targeted nuclear types. (e) Marker expression in assigned nuclear types correlating with targeted nuclear types. (f) UMAP plot NR2F2 expression pattern (encoding COUPTFII) highlighting a subpopulation of inhibitory neurons (compare with Fig. 1c). (g-i) Reference excitatory and inhibitory neuronal methylome signatures (aggregated from an available public single-nuclei methylome dataset) compared to methylome signatures of sorted nuclei and a bulk heart sample. Normalized relative methylation levels (y-axes) and genomic positions (x-axes) of genes listed at top. (g) Methylome signature of SLC17A7 encoding VGLUT1, an excitatory neuronal marker in the brain, showing reduced methylation (i.e. representing activation) across the gene body and especially near the transcription start site (TSS, red box) in TBR1+ excitatory neuron samples. (h) Methylome signature of SLC6A1 encoding VGAT1, an inhibitory neuronal marker in the brain, showing reduced methylation across the gene body and especially near the TSS (red box). (i) Methylome signature of RBFOX3 encoding NEUN, a mature neuronal marker in the brain, showing reduced methylation at the TSS in neurons compared with bulk heart. Ref ExN, reference excitatory neurons; Ref InN, reference inhibitory neurons. (j) Heatmap and dendrograms based on cosine similarities of global methylation patterns between groups. Two different TBR1+ or DLX1+ nuclear pools were aggregated. The TBR1+ nuclear pool was clustered with Ref ExN while the DLX1+ clustered near the pool with Ref InN. The control heart bulk sample was distant from either group.

Extended Data Fig. 3 Quality controls of MPAS results.

(a-b) Violin plot distribution of log-transformed total read depths (y-axes) of individual variant positions in 321 or 147 samples from ID01 or ID05 (x-axes), respectively. The blue dashed lines indicate 1000× read depth. (c-d) Correlation between sqrt-t AF of individual variants from WGS and MPAS. Error bars of individual points: square-root-transformed lower and upper bounds for binomial distribution of individual AFs. Blue horizontal dashed lines: Lower bound for binomial distribution detection threshold. r and p-values (two-tailed) from Pearson’s Product-Moment correlation. Identity lines (red).

Extended Data Fig. 4 Basic characteristics of positively validated MVs from the cMVBA pipeline.

(a) ID01. (b) ID05. CTX, cortex; BG, Basal ganglia; THAL, thalamus; HIP, hippocampus; AMG, amygdala; CB, cerebellum; SUB, subiculum; CLA, Claustrum; POA, preoptic area; OLF, olfactory bulb. (c) Mutational signature analysis using 368 brain-specific somatic single nucleotide variants (sSNVs) from ID01 and ID05 using Mutalisk. Clonal sSNVs show clock-like signatures such as SBS 1 and 5, reflecting embryonic developmental origins. (d-e) AF distributions of organ-shared early embryonic MVs in ID01(d) and ID05 (e) reflect the asymmetric clonal division in early human embryos. Vertical dashed lines (red): expected peaks (AF = 25%) from the first symmetric cell division, absent in observed distribution, suggesting asymmetric divisions.

Extended Data Fig. 5 UMAP relationships between samples from the brain based on AFs of validated MVs.

Clustering by the same hemisphere validates lateralization of brain-derived cell clones except for the independent origin of microglia (marked by PU.1, arrow). (a-c) UMAP clustering in ID01 samples labeled by (a) cell type, (b) gross region, or (c) subregion, respectively. Clustered samples tend to show similar AF patterns. (d-f) UMAP clustering in ID05 samples labeled by (d) cell type, (e) region, or (f) subregion, respectively. Although PU.1 cells were not sorted in ID05, other findings are similar between ID01 and ID05.

Extended Data Fig. 6 Evidence for HIP lineage restriction occurring prior to CTX or BG in ID01 sorted nuclear pools.

(a) Heatmap with 17 sorted nuclear samples based on sqrt-t AFs of 121 informative MVs from ID01, similar to Fig. 2c, showing greater HIP lineage separation compared with CTX or BG (purple compared with green or yellow). (b) Contour plot (at center) with 121 informative MVs derived from (a) and two kernel density estimation plots (at periphery). Axes show the absolute normalized difference value for each MV between the average AF of CTX and BG (CTX-BG) or CTX and HIP regions (CTX-HIP). Solid line: identity. Red dot: averaged x and y values of individual data points. sqrt-t AF, square-root transformed allele fraction; CTX, cortex; BG, basal ganglia; HIP, hippocampus; Cau, caudate; DG, dentate gyrus; HIP and Hip, hippocampal tissue; I, insular cortex; O, occipital cortex; P, parietal cortex; PF, prefrontal cortex; Put, putamen; T, temporal cortex; GP, globus pallidus.

Extended Data Fig. 7 Confidence for dendrograms with sorted nuclei from cortical areas.

(a) Bootstrapping results of ID01. (b) Bootstrapping results of ID05. The percentage of 10,000 replicates showing relationships between sqrt-t AFs for TBR1+ and DLX1+ nuclei in the same geographic region were more similar than TBR1+ nuclei from two different geographic regions. COUPTFII+ nuclei clustered among themselves, outside of the DLX1 and TBR1 clusters. Approximated unbiased p-value > 95% (red): the hypothesis “the cluster does not exist” rejected with a significance level (<5%). (c-d) Heatmaps and hierarchical clustering results after computational deconvolution of DLX1+ nuclei (grey) from Fig. 3b (c) and Fig. 3c (d). (e-f) Heatmaps and hierarchical clustering results after the simulated TBR1+ nuclei contamination for COUPTFII+ nuclear pools (black) from Fig. 3b (e) and Fig. 3c (f). (g) The estimated proportion of dorsally derived cortical inhibitory neurons within deconvolved DLX1+ nuclei of each lobe. The least square method is used (Methods). 11, 13 cortical lobes for ID01 and ID05, respectively. Median, thick horizontal line at the center; 95% confidence intervals, the notch of the box plot; 75 and 25% quantiles of data, upper and lower bounds of the box; Whiskers, maxima and minima excluding outliers.

Extended Data Fig. 8 Quality controls of the ResolveOME dataset in ID05.

(a) A UMAP plot of snRNA-seq using 225 NEUN + nuclei and 121 aggregated reference cell types. F, frontal; T, temporal; HIP, hippocampus; REF, reference dataset. (b) UMAP labeled by cell types. Note that UMAP clusters separate by cell type (ExN, InN or Other) more than by location. (c) Relative expression of cell type markers within clusters, confirming cell identity. (d) Hierarchical clustering based on sqrt-t AFs of 34 informative MVs shared in 5 to 29 cells in single-nuclear data. F- NEUN, sorted frontal NEUN+ nuclei pool; F-sc, pseudo-bulk snMPAS data from a frontal lobe punch; T-sc, snMPAS data from a frontal (F) lobe punch. (e) Correlation between sqrt-t AFs of MVs between F- NEUN and F-sc. (f) Correlation between sqrt-t AFs of MVs between F- NEUN and T-sc. In e and f, linear regression with upper and lower 95% prediction intervals displayed by blue solid lines and gray surrounding area; sqrt-t (AF), sqrt-t AF. Pearson’s Product-Moment correlation with r and p-values (two-tailed) in e and f. (g) Null distribution of the frequency of the number of inhibitory neurons carrying MVs exclusively detected in one lobe and shared with at least two other local cells, including one excitatory neuron within the same lobe. 10,000 permutations. The portion to the right of the red dashed line, compared to the entire distribution, represents the probability (p < 0.0001, one-tailed permutation test) of having 15 or more InNs. (h-m) RNA expression levels of informative genes between InN1 (n = 17) and InN2 (n = 16) (Fig. 4b) in snRNA-seq. (h) Comparable expression levels of inhibitory neuronal markers between both groups. (i) Decreased tendency for the expression of CGE-derived cell markers in InN2 compared to InN1, implying COUPTFII+ inhibitory neurons are unlikely InN1, consistent with previous observations in sorted nuclear populations. (j) RELN+ inhibitory neuronal marker showed decreased expression tendency in InN2 compared to InN1. (k) Increased expression tendency for parvalbumin-positive (PV+) inhibitory neuronal marker in InN2 compared to InN1, implying dorsally derived inhibitory neurons include PV+ neurons. (l, m) top 3 genes increased (l) or decreased (m) in InN2 compared to InN1 among the most variable 3000 protein-coding genes.

Extended Data Fig. 9 Phylogenic tree analysis.

(a) Phylogenic tree generated after 1000 bootstrap replications based on the 68 MVs in 118 single nuclei in Fig. 4b. Bootstrap values supporting each edge are labeled beside branches of the tree. (b) The number of pairs diverging from the latest branch that has the local highest-confident edge is shown based on the lobe and cell type. For example, the number of excitatory-excitatory neuron pairs within the same lobe clustered with the local highest-confident edge was 20.

Extended Data Fig. 10 UMAP plots with sorted nuclear pools based on sqrt-t AFs of 186 informative MVs from Fig. 5.

Colors of data points correspond to the spatial information in the grey box.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Data 1

Sample information

Supplementary Data 2

MPAS and snMPAS results with annotations

Supplementary Data 3

Visualization of AFs of all validated MVs for bulk and sorted nuclei

Supplementary Data 4

Validated variant list with annotations

Supplementary Data 5

Visualization of AFs of all validated MVs for three cell types in the cortex

Supplementary Data 6

Pearson correlation between MVs based on regional AF patterns within the three sorted nuclei groups

Supplementary Data 7

Visualization of AFs of all MVs for 191 NEUN+ nuclei

Supplementary Data 8

Visualization of AFs of informative 186 MVs for TBR1+ and DLX1+ nuclei in 17 positions of the ID05 right parietal cortex

Supplementary Data 9

Targeted genomic regions for MPAS and snMPAS

Supplementary Data 10

Basic metrics of snRNA-seq in Fig. 4

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Chung, C., Yang, X., Hevner, R.F. et al. Cell-type-resolved mosaicism reveals clonal dynamics of the human forebrain. Nature 629, 384–392 (2024).

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