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Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development

Nature Genetics volume 55, pages 209–220 (2023)Cite this article

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Abstract

Malformations of cortical development (MCD) are neurological conditions involving focal disruptions of cortical architecture and cellular organization that arise during embryogenesis, largely from somatic mosaic mutations, and cause intractable epilepsy. Identifying the genetic causes of MCD has been a challenge, as mutations remain at low allelic fractions in brain tissue resected to treat condition-related epilepsy. Here we report a genetic landscape from 283 brain resections, identifying 69 mutated genes through intensive profiling of somatic mutations, combining whole-exome and targeted-amplicon sequencing with functional validation including in utero electroporation of mice and single-nucleus RNA sequencing. Genotype–phenotype correlation analysis elucidated specific MCD gene sets associated with distinct pathophysiological and clinical phenotypes. The unique single-cell level spatiotemporal expression patterns of mutated genes in control and patient brains indicate critical roles in excitatory neurogenic pools during brain development and in promoting neuronal hyperexcitability after birth.

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Fig. 1: Comprehensive genetic profiling and validation of somatic variants in 283 MCD patients.
Fig. 2: Genes mutated in MCD highlight four major gene networks.
Fig. 3: Selected new MCD somatic variants show functional defects in embryonic mouse brain and patient samples.
Fig. 4: Clinical phenotypic outcomes correlate with genotype-based classifications in MCD.
Fig. 5: Single-nucleus transcriptomes reveal MCD gene enrichment in radial glia and excitatory neurons in the developing human cortex.
Fig. 6: Single-nucleus transcriptomes showed MCD gene expression enriched in MCD-specific cell types.

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Data availability

WES and AmpliSeq data are deployed on the NIMH Data Archive under study number 1484 ‘Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development’ and SRA under accession number PRJNA821916: ‘Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development’. The BSMN neurotypical brain data are available at NIMH Data Archive (NDA study 644, 792 and 919, https://nda.nih.gov/study.html?tab=result&id=644, https://nda.nih.gov/study.html?tab=result&id=792 and https://nda.nih.gov/study.html?tab=result&id=919) and SRA PRJNA736951. The raw and processed snRNA-seq dataset was deposited in the Gene Expression Omnibus (GEO) under accession number GSE218022. gnomAD frequencies were extracted from https://gnomad.broadinstitute.org/; the implication in cancers for each sSNV was evaluated in COSMIC, https://cancer.sanger.ac.uk/cosmic/; STRING analysis was performed using STRING, https://string-db.org/; single-cell RNA sequencing data from the developing cortex (Nowakowski et al.48), https://cells.ucsc.edu/?ds=cortex-dev; GRCh37 human reference genome under accession number SRA PRJNA31257 was used for the alignment of all types of sequencing data. Source data are provided with this paper.

Code availability

Code to generate the figures and analyze the data are publicly available on GitHub71 (https://github.com/shishenyxx/MCD_mosaic).

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Acknowledgements

AmpliSeq, TASeq and snRNA-seq were supported by NIH P30CA023100 and S10OD026929 at the UCSD IGM Genomics Center. Rady Children’s Institute for Genomic Medicine, Broad Institute (U54HG003067, UM1HG008900), the Yale Center for Mendelian Disorders (U54HG006504) and the New York Genome Center provided WES. The UCSD Microscopy Core (NINDS P30NS047101) provided imaging support. The UCSD Tissue Technology Shared Resources Team (National Cancer Institute Cancer Center Support Grant, P30CA23100) supported paraffin sectioning and H&E staining. This study was supported by the 2021 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (30598 to C.C.), NIH (NIMH U01MH108898 and R01MH124890 to J.G.G. and G.W.M. and NIA R21AG070462, NINDS R01NS083823 to J.G.G.), the Regione Toscana under the Call for Health 2018 (DECODE-EE to R.G.) and Fondazione Cassa di Risparmio di Firenze (to R.G.). Figures 1b and 3a were created with BioRender.com. The funders had no role in the study design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Changuk Chung, Xiaoxu Yang.

Authors and Affiliations

  1. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA

    Changuk Chung, Xiaoxu Yang, Keng Ioi Vong, Swapnil Mittal, Zhen Li, Ashley P. L. Marsh, Martin W. Breuss, Laurel L. Ball, Renee D. George, Jing Gu, Mingchu Xu, Chelsea Barrows, Kiely N. James, Valentina Stanley, Anna S. Nidhiry, Sami Khoury, Gabrielle Howe, Emily Riley, Xin Xu, Brett Copeland & Joseph G. Gleeson

  2. Rady Children’s Institute for Genomic Medicine, San Diego, CA, USA

    Changuk Chung, Xiaoxu Yang, Keng Ioi Vong, Swapnil Mittal, Zhen Li, Ashley P. L. Marsh, Martin W. Breuss, Laurel L. Ball, Renee D. George, Jing Gu, Mingchu Xu, Chelsea Barrows, Kiely N. James, Valentina Stanley, Anna S. Nidhiry, Sami Khoury, Gabrielle Howe, Emily Riley, Xin Xu, Brett Copeland & Joseph G. Gleeson

  3. Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA

    Taejeong Bae, Yifan Wang & Alexej Abyzov

  4. Department of Neurosurgery, Experimental Epilepsy Research, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Catharina Donkels & Carola A. Haas

  5. Department of Neurosurgery, University of California at Los Angeles, Los Angeles, CA, USA

    H. Westley Phillips & Gary W. Mathern

  6. Department of Pediatrics, Section of Clinical Genetics and Metabolism, University of Colorado Aurora, Aurora, CO, USA

    Martin W. Breuss

  7. Laboratory of Pediatric Neurosurgery and Developmental Neuropathology, Department of Surgery and Anatomy, University of São Paulo, Ribeirão Preto, Brazil

    Camila Araújo Bernardino Garcia & Helio R. Machado

  8. Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

    Se Hoon Kim

  9. Division of Pediatric Neurology, Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Seoul, South Korea

    Hoon-Chul Kang

  10. Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Andreas Schulze-Bonhage, Carola A. Haas & Marco Prinz

  11. Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany

    Horst Urbach

  12. Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany

    Marco Prinz

  13. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

  14. Department of Neurology, St. Louis Children’s Hospital, Washington University St Louis, Washington, MO, USA

    David D. Limbrick Jr. & Christina A. Gurnett

  15. Department of Neurosurgery, St. Louis Children’s Hospital, Washington University St Louis, Washington, MO, USA

    Matthew D. Smyth

  16. Epilepsy Center, Rady Children’s Hospital, San Diego, CA, USA

    Shifteh Sattar, Mark Nespeca & David D. Gonda

  17. National Epilepsy Center, NHO Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan

    Katsumi Imai & Yukitoshi Takahashi

  18. Division of Pediatric Neurosurgery, The Neurological Institute, Taipei Veterans General Hospital, Taipei City, Taiwan

    Hsin-Hung Chen

  19. Institute of Brain Science, Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Jin-Wu Tsai

  20. Pediatric Neurology Unit and Laboratories, IRCCS Meyer Children’s Hospital University of Florence, Firenze, Italy

    Valerio Conti & Renzo Guerrini

  21. Comprehensive Epilepsy Center, Department of Neurology, New York University Langone Health, New York, NY, USA

    Orrin Devinsky

  22. Department of Genetics, Center for Cell-Based Therapy, Center for Integrative Systems Biology, University of São Paulo, Ribeirão Preto, Brazil

    Wilson A. Silva Jr.

  23. Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Hôpital de la Pitié Salpêtrière, Paris, France

    Sara Baldassari & Stéphanie Baulac

  24. Rady Children’s Hospital, San Diego, CA, USA

    Joseph G. Gleeson, Marilyn Jones, Diane Masser-Frye, Shifteh Sattar, Mark Nespeca & David D. Gonda

  25. NHO Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan

    Katsumi Imai & Yukitoshi Takahashi

  26. Taipei Veterans General Hospital, Taipei City, Taiwan

    Hsin-Hung Chen

  27. National Yang Ming Chiao Tung University, Taipei City, Taiwan

    Jin-Wu Tsai

  28. Barrow Neurological Institute at Phoenix Children’s Hospital, University Arizona College of Medicine, Phoenix, AZ, USA

    Valerio Conti, Richard Frye, Angus Wilfong & David Adelson

  29. IRCCS Meyer Children’s Hospital, University of Florence, Firenze, Italy

    Renzo Guerrini

  30. New York University Langone Health, New York, NY, USA

    Orrin Devinsky

  31. University of São Paulo, Ribeirão Preto, Brazil

    Helio R. Machado, Camila Araújo Bernardino Garcia & Wilson A. Silva Jr.

  32. Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

    Se Hoon Kim & Hoon-Chul Kang

  33. Acibadem Hospital, Istanbul, Turkey

    Yasemin Alanay

  34. Lok Nayak Hospital & Maualana Azad Medical Center, New Delhi, India

    Seema Kapoor

  35. University of Freiburg, Freiburg, Germany

    Carola A. Haas

  36. Albert-Ludwigs University, Freiburg, Germany

    Georgia Ramantani & Thomas Feuerstein

  37. University Hopsital Erlangen, Erlangen, Germany

    Ingmar Blumcke, Robyn Busch & Zhong Ying

  38. Romodanov Institute of Neurosurgery, Kyiv, Ukraine

    Vadym Biloshytsky, Kostiantyn Kostiuk & Eugene Pedachenko

  39. University of California at Los Angeles, Los Angeles, CA, USA

    Gary W. Mathern

  40. St. Louis Children’s Hospital, Washington University St Louis, Washington, MO, USA

    Christina A. Gurnett & Matthew D. Smyth

  41. Children’s Hospital Philadelphia, Philadelphia, PA, USA

    Ingo Helbig & Benjamin C. Kennedy

  42. Brown University, Providence, RI, USA

    Judy Liu & Felix Chan

  43. Cincinnati Children’s Hospital, Cincinnati, OH, USA

    Darcy Krueger

  44. Children’s National Hospital, Washington, DC, USA

    William Gaillard & Chima Oluigbo

  45. Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA

    Anne Anderson

  46. Boston Children’s Hospital, Boston, MA, USA

    Alice Lee, August Yue Huang, Alissa D’Gama, Caroline Dias, Christopher A. Walsh, Eduardo Maury, Javier Ganz, Michael Lodato, Michael Miller, Pengpeng Li, Rachel Rodin, Rebeca Borges-Monroy, Robert Hill, Sara Bizzotto, Sattar Khoshkhoo, Sonia Kim & Zinan Zhou

  47. Harvard University, Boston, MA, USA

    Alice Lee, Alison Barton, Alon Galor, Chong Chu, Craig Bohrson, Doga Gulhan, Eduardo Maury, Elaine Lim, Euncheon Lim, Giorgio Melloni, Isidro Cortes, Jake Lee, Joe Luquette, Lixing Yang, Maxwell Sherman, Michael Coulter, Minseok Kwon, Peter J. Park, Rebeca Borges-Monroy, Semin Lee, Sonia Kim, Soo Lee, Vinary Viswanadham & Yanmei Dou

  48. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew J. Chess, Attila Jones, Chaggai Rosenbluh & Schahram Akbarian

  49. Kennedy Krieger Institute, Baltimore, MD, USA

    Ben Langmead, Jeremy Thorpe & Sean Cho

  50. Lieber Institute for Brain Development, Baltimore, MD, USA

    Andrew Jaffe, Apua Paquola, Daniel Weinberger, Jennifer Erwin, Jooheon Shin, Michael McConnell, Richard Straub & Rujuta Narurkar

  51. Mayo Clinic, Rochester, MN, USA

    Alexej Abyzov, Taejeong Bae, Yeongjun Jang & Yifan Wang

  52. National Institute of Mental Health (NIMH), Bethesda, MD, USA

    Anjene Addington & Geetha Senthil

  53. Sage Bionetworks Seattle, Seattle, WA, USA

    Cindy Molitor & Mette Peters

  54. Salk Institute for Biological Studies, La Jolla, CA, USA

    Fred H. Gage, Meiyan Wang, Patrick Reed & Sara Linker

  55. Stanford University, CA, USA

    Alexander Urban, Bo Zhou, Reenal Pattni & Xiaowei Zhu

  56. Universitat Pompeu Fabra, Barcelona, Spain

    Aitor Serres Amero, David Juan, Inna Povolotskaya, Irene Lobon, Manuel Solis Moruno, Raquel Garcia Perez & Tomas Marques-Bonet

  57. University of Barcelona, Barcelona, Spain

    Eduardo Soriano

  58. University of California, Los Angeles, CA, USA

    Gary Mathern

  59. University of California, San Diego, La Jolla, CA, USA

    Danny Antaki, Dan Averbuj, Eric Courchesne, Joseph G. Gleeson, Laurel L. Ball, Martin W. Breuss, Subhojit Roy, Xiaoxu Yang & Changuk Chung

  60. University of Michigan, Ann Arbor, MI, USA

    Chen Sun, Diane A. Flasch, Trenton J. Frisbie Trenton, Huira C. Kopera, Jeffrey M. Kidd, John B. Moldovan, John V. Moran, Kenneth Y. Kwan, Ryan E. Mills, Sarah B. Emery, Weichen Zhou & Xuefang Zhao

  61. University of Virginia, Charlottesville, VA, USA

    Aakrosh Ratan

  62. Yale University, New Haven, CT, USA

    Adriana Cherskov, Alexandre Jourdon, Flora M. Vaccarino, Liana Fasching, Nenad Sestan, Sirisha Pochareddy & Soraya Scuder

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Focal Cortical Dysplasia Neurogenetics Consortium

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Contributions

C.C., X.Y., Sa.B., St.B. and J.G.G. designed the study. C.C., S.M. and S.K. conducted functional validation. C.B., V.S., A.S.N., E.R., C.C. and G.H. coordinated the clinical database. X.Y., C.C., M.W.B., L.L.B., R.D.G., J.G., M.X., A.P.L.M. and K.N.J. organized, handled and sequenced human samples. X.Y., C.C., T.B., Y.W., A.A., X.X., Z.L. and B.C. performed bioinformatics and data analysis. C.C. and K.I.V. performed the RNAscope experiment. C.D., H.W.P., C.A.B.G., S.H.K., H.K., H.U., M.P., A.S., C.A.H., D.D.L., C.A.G., M.D.S., S.S., M.N., D.D.G., K.I., Y.T., H.C., J.T., V.C., R.G., O.D., W.A.S., H.R.M. and G.W.M. provided resected brain tissues and clinical data from FCD patients. C.C., X.Y. and J.G.G. wrote the manuscript. All authors read and commented on the manuscript before submission.

Corresponding author

Correspondence to Joseph G. Gleeson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Jean-Baptiste Rivière 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

Extended Data Fig. 1 Workflow of genetic discovery and bioinformatic pipeline to detect sSNVs in the MCD cohort.

(a) Workflow chart describing the flow of cases for each phase of genetic discovery. QNS: quantity not sufficient. 2 cases labeled by a star are sequenced in phase 1 but not phase 2. (b) The pipeline for paired samples. Notably, the dashed square indicates that the sharing variants between MuTect2 paired mode and Strelka2 were used for the downstream analysis. BSMN common pipeline and DeepMosaic were used only for WES datasets. The DeepMosaic input variants were generated by MuTect2 single mode. (c) The pipeline for unpaired samples. The pipeline is similar except that MuTect2 single mode without Strelka2 is used. PM: paired mode, SM: single mode.

Extended Data Fig. 2 The locations of the selected MCD variants.

(a) The location of two recurrent SNV calls is at the same position between the coiled-coil domain (CC) and the first SAM domain (S) of Liprin-α4. (b) Two different variants in SERCA1. p.R524C mutation is at the nucleotide ATP-binding (N) domain, whereas the p.A846T variant is in the 7th transmembrane (M7) domain. A: Actuator domain, P: Phosphorylation domain, M: Transmembrane domain. (c) Left: The location of p.H226R variant in RAGA protein. GTPase: GTPase domain, CRD: C-terminal roadblock domain. Right: UCSC genome browser screenshot describing that p.H226 is a conserved site across all vertebrates. (d) The location of the p.R38Q variant in the N-terminal region before the BTB (Broad-Complex, Tramtrack, and Bric-à-brac) domain of KLHL22. (e) A variant in the S1 domain of NR2C. S1 and S2 together make the ligand-binding domain (LBD), the target of glutamate. ATD: Amino-terminal domain. (f) RHOA p.P75S variant in the interdomain space between the second GTP/GDP binding domain and Rho insert domain.

Extended Data Fig. 3 Mutational signature analysis shows cell-division-related clock-like signatures in the MCD cohort.

SBS5 (39.1%) and SBS1 (33%) revealed by Mutalisk are clock-like mutational signatures. SBS1 especially correlates with stem cell division and mitosis.

Extended Data Fig. 4 Four major gene networks were reconstructed from the WES dataset.

(a) STRING DB pathway analysis of the 59 MCD discovered genes and six novel genes (a total of 65 genes) from recent publications identifies mTOR/MAP kinase pathway (pink, Cluster 1), Calcium dynamics (green, Cluster 2), Synapse (purple, Cluster 3), Gene expression (blue, Cluster 4). Edge thickness: confidence score calculated by STRING. Size and color of a node: square root transformed (sqrt-t) number of patients carrying a given mutation and average VAF across all patients, respectively. Non-clustered orphan genes are listed on the right. (b) Gene Ontology (GO) analysis results confirmed the functions of compositions in each network. Top GO terms or KEGG pathways. Strength calculation and cluster generation were performed by STRING.

Extended Data Fig. 5 ClueGO analysis using the MCD genes result identifies the biological processes and molecular pathways.

The main cluster is related to TOR signaling, regulation of cell-matrix adhesion, regulation of focal adhesion assembly, and artery morphogenesis. Notably, there are also isolated clusters that were not covered in previous studies, for example, cardiac muscle cell contraction, calcium ion import, and protein localization to the synapse. Corrected p-value with Bonferroni step down was reflected in node size (two-sided hypergeometric test, Large: p < 0.0005, medium: p < 0.005, small: p < 0.05). All p-values are in Supplementary Table 9.

Extended Data Fig. 6 Additional functional analyses for new RRAGA and RHOA mutations.

(a) Over-expression of RHOA WT and P75S mutant form in cortical neurogenic pool induce both significant defects in migration. Notably, some portion of WT form-expressing cells migrate to the superior cortical area (white arrow), whereas mutant form-expressing cells did not show any migrating cells at all. The dashed square area is magnified to the right side images. Scale bar: 100 µm and 20 µm for left and right images, respectively. Right, Quantification of the migration level. EV data was exported from Fig. 3b. Two-way ANOVA and Sidak multiple comparisons with p-values of interaction between genotype and bin factor. Ten bins from the surface of the cortex (top) to SVZ (bottom). n = 3, 3, 2 biologically independent mice for EV, RHOA WT and RHOA P75S, respectively. Mean ± SEM. (b) Immunofluorescence in postnatal day 21 mouse cortices for RRAGA wild-type (WT) or mutant isoform. Yellow dashed lines: examples of cell body size quantification. Dashed lines and dotted lines in the violin plots indicate median and quartiles, respectively. Two-tailed Mann-Whitney U–test. Scale bar: 20 μm. n = 61 cells (3 mice), 61 (2) for RRAGA WT and RRAGA H226R, respectively * or # indicates a p-value in comparison between WT and mutant group, or EV and mutant group, respectively. ####P < 0.0001; ##P < 0.01; #P < 0.05; **P < 0.01; ns, non-significant.

Source data

Extended Data Fig. 7 Cell-type identification by DEGs and WGCNA in the MCD snRNA-seq dataset.

(a) DEG analysis using FindAllMarker function in Seurat v4 package. The top 10 genes for each cluster were presented. Some notable marker genes are presented on the left side. Color: scaled gene expression level. (b) Description of WGCNA. The most variable 3000 genes were used for generating six co-expression module eigengenes (ME1 to ME11). The members of each ME are described in Supplementary Table 5,b. (c) Enrichment of module eigengenes in cell type clusters. Atypical clusters showing similar patterns with a normal cell cluster were classified as the same lineage. We identified 5 different lineages (Ast, OD, ExN, InN, OPC) coded as different colors. Notably, Ast-L1/2/3 and OPC-L1/2 show excessively increased expression of ME6 or ME7, Ast or OPC signature ME, respectively. OPC-L2 shows upregulation of ME4, related to the cell cycle, implying that HME has many over-proliferating OPC-L cells. Excitatory neuronal lineage typically expresses ME5 and ME10, but ExN-L1/2 also shows increased expression of ME9, a signature of inhibitory neurons, compared to ExN1/2/3. OD-L cells are classified as OD lineage because they express excessive ME11, a signature to OD. U cluster, dominant in TSC, does not show a clear signature. The size and color of the dot plot are the Pearson correlation coefficient and corresponding non-adjusted asymptotic p-value derived from a two-sided Student’s t-test, respectively.

Extended Data Fig. 8 The validation of the snRNA-seq result from HME-6593 shows that MCD dominant clusters are highly correlated with dysplastic cells in MCD.

(a–b) H&E (left) and RNAscope (right) for genes expressed highly in MCD brain (FGFR2, FGFR1, EGFR, top) or (FGFR3, PDGFRA bottom). Dashed lines: blood vessels. White/black arrows: dysplastic cells. One representative section is shown for each probe combination.

Extended Data Fig. 9 Expression patterns of individual MCD genes in the MCD snRNA-seq dataset.

The gene members of each eigen module shown in Fig. 6d were colored according to the name of a given eigengene.

Extended Data Fig. 10 Functional implication of MCD genes in MCD snRNA-seq dataset.

(a) The 75 MCD genes overlap with DEGs of MCDs in contrast to controls. p-values derived from permutation tests (10,000 times) show a chance to show this overlap in a random sampling of DEGs from 19909 protein-coding genes used in these DEGs. Red or blue coloring of gene names indicates upregulated or downregulated DEGs in MCDs compared to CTRLs, respectively. HME and TSC show significant overlap with the MCD genes whereas an FCD case with low VAF did not, which is probably because of low VAF. One-sided permutation test. (b) Cell-lineage specific DEGs compared to the according to normal cell lineage of CTRL represent alterations of mTOR downstream pathways, calcium dynamics, and synaptic functions across. Red dots in UMAPs indicate the cells that participated in the comparison. Top 10 enriched GO or KEGG terms representing lineage-specific DEGs.

Supplementary information

Supplementary Information

Supplementary Tables 1–9.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1. Cohort list and corresponding sequencing methods. The 293 cases are listed in each row and corresponding sequencing methods used for a given sample are described. Supplementary Table 2. AmpliSeq primer pool designs. a, Ampliseq primer pool design used in phase 1. b, Highlighting table for the known genes where somatic or germline variants are previously detected in FCD/HMEs and the candidate ones known as PI3K–AKT3–mTOR interactors. c, Ampliseq primer pool design used in phase 3. Supplementary Table 3. Summary of SNV calls across three phases of genetic discovery. a, 1181 raw sSNV calls derived from the combination of variant callers described in Extended Data Fig. 1b. b, Validated 108 brain sSNVs from a. c, Annotation table of the genes listed in b based on GO terms. Supplementary Table 4. Summary of phenotype and genotype information for the ‘genetically solved’ cases. Supplementary Table 5. Summary table for snRNA-seq DEG and WGCNA analysis. a, Cell markers are generated by the FindAllMarkers function in the Seurat v,4 package. b, 11 MEs and their gene members are generated by WGCNA. c, DEG analysis results include all cell types or in a cell-type-restricted manner. d, All enriched terms from DEGs. Supplementary Table 6. Summary table for false discovery estimation. Supplementary Table 7. Estimation of probability of observing recurrency in mutated genes in MCD. Supplementary Table 8. Primer sequences used in TASeq for orthogonal validation and quantification of mosaic mutations detected in AmpliSeq and WES sequencing. Supplementary Table 9. Detailed statistical information on Fig. 1d, Fig. 3 and Extended Data Figs. 5 and 6.

Source data

Source Data Fig. 3

Actual per-cell immunofluorescence values in Fig. 3c violin plots.

Source Data Extended Data Fig. 6

Actual per-cell immunofluorescence values in Extended Data Fig. 6b violin plots.

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Chung, C., Yang, X., Bae, T. et al. Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development. Nat Genet 55, 209–220 (2023). https://doi.org/10.1038/s41588-022-01276-9

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