Abstract
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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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: http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/. gnomAD: https://gnomad.broadinstitute.org/. Human multiple cortical areas SMART-seq dataset: https://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq.
Code availability
Details and codes for the data processing and annotation are provided on GitHub (https://github.com/shishenyxx/Human_Inhibitory_Neurons)52.
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Acknowledgements
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 (https://biorender.com).
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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.
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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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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 (https://biorender.com).
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
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 (2024). https://doi.org/10.1038/s41586-024-07292-5
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DOI: https://doi.org/10.1038/s41586-024-07292-5
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