Skip to main content

Genomic Analysis and In Vivo Functional Validation of Brain Somatic Mutations Leading to Focal Cortical Malformations

  • Protocol
  • First Online:
Genomic Mosaicism in Neurons and Other Cell Types

Part of the book series: Neuromethods ((NM,volume 131))

  • 741 Accesses

Abstract

Focal cortical malformation (FCM), such as focal cortical dysplasia (FCD) and hemimegalencephaly (HME), is a major developmental brain malformation in the cerebral cortex leading to intractable epilepsy. The sporadic occurrence of most FCM and histologic characteristics of surgically resected brain tissue showing scattered dysmorphic cells suggest that FCM might be caused by a somatic mutation in an area affecting brain development. Indeed, recent genomic studies of these conditions have shown that low-frequency somatic mutations in PI3K-AKT-mTOR pathway genes are a major genetic cause of FCM. In addition, functional validation using an in vivo disease model not only confirmed the causality of the identified somatic mutations but also helped to reveal their molecular genetic mechanisms. Here, we highlight the key points to be considered regarding the application of sequencing methods and bioinformatics analysis to identify brain somatic mutations with a low allelic frequency in FCM patients. In addition, we describe the generation of an in vivo disease model recapitulating the pathologic phenotype of FCM such as dysmorphic neurons, migration defects, and electrographic seizures. Our goal is to provide guidelines for the analysis of sequencing data and functional validation using a disease model of FCM caused by somatic mutations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lupski JR (2013) Genome mosaicism-one human, multiple genomes. Science 341:358–359. doi:10.1126/science.1239503

    Article  CAS  PubMed  Google Scholar 

  2. Lynch M (2010) Evolution of the mutation rate. Trends Genet 26:345–352. doi:10.1016/j.tig.2010.05.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bozic I, Antal T, Ohtsuki H et al (2010) Accumulation of driver and passenger mutations during tumor progression. Proc Natl Acad Sci U S A 107:18545–18550. doi:10.1073/pnas.1010978107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Poduri A, Evrony GD, Cai X, Walsh CA (2013) Somatic mutation, genomic variation, and neurological disease. Science 341:1237758. doi:10.1126/science.1237758

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kennedy SR, Loeb LA, Herr AJ (2012) Somatic mutations in aging, cancer and neurodegeneration. Mech Ageing Dev 133:118–126. doi:10.1016/j.mad.2011.10.009

    Article  CAS  PubMed  Google Scholar 

  6. Weinstein LS, Shenker A, Gejman PV et al (1991) Activating mutations of the stimulatory G protein in the McCune-Albright syndrome. N Engl J Med 325:1688–1695. doi:10.1056/NEJM199112123252403

    Article  CAS  PubMed  Google Scholar 

  7. Shirley MD, Tang H, Gallione CJ et al (2013) Sturge–weber syndrome and port-wine stains caused by somatic mutation in GNAQ. N Engl J Med 368:1971–1979. doi:10.1056/NEJMoa1213507

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lindhurst MJ, Sapp JC, Teer JK et al (2011) A mosaic activating mutation in AKT1 associated with the Proteus syndrome. N Engl J Med 365:611–619. doi:10.1056/NEJMoa1104017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Insel TR (2014) Brain somatic mutations: the dark matter of psychiatric genetics? Mol Psychiatry 19:156–158. doi:10.1038/mp.2013.168

    Article  CAS  PubMed  Google Scholar 

  10. Barkovich AJ, Guerrini R, Kuzniecky RI et al (2012) A developmental and genetic classification for malformations of cortical development: update 2012. Brain 135:1348–1369. doi:10.1093/brain/aws019

    Article  PubMed  PubMed Central  Google Scholar 

  11. Guerrini R (2005) Genetic malformations of the cerebral cortex and epilepsy. Epilepsia 46(Suppl 1):32–37. doi:10.1111/j.0013-9580.2005.461010.x

    Article  CAS  PubMed  Google Scholar 

  12. Pang T, Atefy R, Sheen V (2008) Malformations of cortical development. Neurologist 14:181–191. doi:10.1097/NRL.0b013e31816606b9

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wong M, Crino PB (2010) mTOR and epileptogenesis in developmental brain malformations. Epilepsia 51:72–72. doi:10.1111/j.1528-1167.2010.02858.x

    Article  Google Scholar 

  14. Salamon N (2005) Contralateral hemimicrencephaly and clinical-pathological correlations in children with hemimegalencephaly. Brain 129:352–365. doi:10.1093/brain/awh681

    Article  PubMed  Google Scholar 

  15. Lim JS, Kim W-I, Kang HC et al (2015) Brain somatic mutations in MTOR cause focal cortical dysplasia type II leading to intractable epilepsy. Nat Med 21:395–400. doi:10.1038/nm.3824

    Article  CAS  PubMed  Google Scholar 

  16. Nakashima M, Saitsu H, Takei N et al (2015) Somatic mutations in the MTOR gene cause focal cortical dysplasia type IIb. Ann Neurol 78:375–386. doi:10.1002/ana.24444

    Article  CAS  PubMed  Google Scholar 

  17. Sakai K, Horiike A, Irwin DL et al (2013) Detection of epidermal growth factor receptor T790M mutation in plasma DNA from patients refractory to epidermal growth factor receptor tyrosine kinase inhibitor. Cancer Sci 104:1198–1204. doi:10.1111/cas.12211

    Article  CAS  PubMed  Google Scholar 

  18. Tabata H, Nakajima K (2001) Efficient in utero gene transfer system to the developing mouse brain using electroporation: visualization of neuronal migration in the developing cortex. Neuroscience 103:865–872

    Article  CAS  PubMed  Google Scholar 

  19. Tabata H, Nakajima K (2008) Labeling embryonic mouse central nervous system cells by in uteroelectroporation. Develop Growth Differ 50:507–511. doi:10.1111/j.1440-169X.2008.01043.x

    Article  CAS  Google Scholar 

  20. McKenna A, Hanna M, Banks E et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. doi:10.1101/gr.107524.110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498. doi:10.1038/ng.806

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McKenna A, Hanna M, Banks E, et al (2011) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.https://software.broadinstitute.org/gatk/. Accessed 27 Dec 2016

  23. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform.http://bio-bwa.sourceforge.net. Accessed 27 Dec 2016

  24. Kim S, Jeong K, Bhutani K et al (2013) Virmid: accurate detection of somatic mutations with sample impurity inference. Genome Biol 14:R90. doi:10.1186/gb-2013-14-8-r90

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219. doi:10.1038/nbt.2514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim S, Jeong K, Bhutani K, et al (2013) Virmid: accurate detection of somatic mutations with sample impurity inference.https://sourceforge.net/projects/virmid/. Accessed 27 Dec 2016

  27. Cibulskis K, Lawrence MS, Carter SL, et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples.http://archive.broadinstitute.org/cancer/cga/mutect. Accessed 27 Dec 2016

  28. Cingolani P, Platts A, Le LW et al (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Flying 6:80–92. doi:10.4161/fly.19695

    CAS  Google Scholar 

  29. Andrews S (2016) FastQC: a quality control tool for high throughput sequence data.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 27 Dec 2016

  30. Kim J, Maeng JH, Lim JS et al (2016) Vecuum: identification and filtration of false somatic variants caused by recombinant vector contamination. Bioinformatics 32:3072–3080. doi:10.1093/bioinformatics/btw383

    Article  CAS  PubMed  Google Scholar 

  31. Illumina (2016) Illumina adapter sequences document.http://support.illumina.com/downloads/illumina-customer-sequence-letter.html. Accessed 27 Dec 2016

  32. Acinas SG, Sarma- Rupavtarm R, Klepac- Ceraj V, Polz MF (2005) PCR-induced sequence artifacts and bias: insights from comparison of two 16S rRNA clone libraries constructed from the same sample. Appl Environ Microbiol 71:8966–8969. doi:10.1128/AEM.71.12.8966-8969.2005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Parkinson NJ, Maslau S, Ferneyhough B et al (2012) Preparation of high-quality next-generation sequencing libraries from picogram quantities of target DNA. Genome Res 22:125–133. doi:10.1101/gr.124016.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lasken RS, Stockwell TB (2007) Mechanism of chimera formation during the multiple displacement amplification reaction. BMC Biotechnol 7:19. doi:10.1186/1472-6750-7-19

    Article  PubMed  PubMed Central  Google Scholar 

  35. Robin JD, Ludlow AT, LaRanger R et al (2016) Comparison of DNA quantification methods for next generation sequencing. Sci Rep 6:1–10. doi:10.1038/srep24067

    Article  Google Scholar 

  36. Bhat S, Curach N, Mostyn T et al (2010) Comparison of methods for accurate quantification of DNA mass concentration with traceability to the international system of units. Anal Chem 82:7185–7192. doi:10.1021/ac100845m

    Article  CAS  PubMed  Google Scholar 

  37. Simbolo M, Gottardi M, Corbo V et al (2013) DNA qualification workflow for next generation sequencing of Histopathological samples. PLoS One 8:e62692–e62698. doi:10.1371/journal.pone.0062692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. O’ Neill M, McMillan ND, Smith SRP et al (2011) Performance studies on the transmitted light drop Analyser. J Phys Conf Ser 307:012035–012037. doi:10.1088/1742-6596/307/1/012035

    Article  Google Scholar 

  39. Gilbert MTP, Haselkorn T, Bunce M et al (2007) The isolation of nucleic acids from fixed, paraffin-embedded tissues–which methods are useful when? PLoS One 2:e537–e512. doi:10.1371/journal.pone.0000537

    Article  PubMed  PubMed Central  Google Scholar 

  40. Illumina (2016) Evaluating DNA quality from FFPE samples. 1–4.

    Google Scholar 

  41. Consortium TICG, committee E, committee EAP et al (2010) International network of cancer genome projects. Nature 464:993–998. doi:10.1038/nature08987

    Article  Google Scholar 

  42. Weinstein JN, Collisson EA, Mills GB et al (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45:1113–1120. doi:10.1038/ng.2764

    Article  PubMed  PubMed Central  Google Scholar 

  43. Goode DL, Hunter SM, Doyle MA et al (2012) A simple consensus approach improves somatic mutation prediction accuracy. Genome Med 5:90–90. doi:10.1186/gm494

    Article  Google Scholar 

  44. Xu H, DiCarlo J, Satya RV et al (2014) Comparison of somatic mutation calling methods in amplicon and whole exome sequence data. BMC Genomics 15:244. doi:10.1186/1471-2164-15-244

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wang Q, Jia P, Li F et al (2013) Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med 5:91. doi:10.1186/gm495

    Article  PubMed  PubMed Central  Google Scholar 

  46. Alioto TS, Buchhalter I, Derdak S et al (2015) A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing. Nat Commun 6:1–13. doi:10.1038/ncomms10001

    Article  Google Scholar 

  47. Roberts ND, Kortschak RD, Parker WT et al (2013) A comparative analysis of algorithms for somatic SNV detection in cancer. Bioinformatics 29:2223–2230. doi:10.1093/bioinformatics/btt375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Illumina (2016) Sequencing coverage calculator.http://support.illumina.com/downloads/sequencing_coverage_calculator.html. Accessed 27 Dec 2016

  49. Sims D, Sudbery I, Ilott NE et al (2014) Sequencing depth and coverage: keyconsiderations in genomic analyses. Nat Rev Genet 15:121–132. doi:10.1038/nrg3642

    Article  CAS  PubMed  Google Scholar 

  50. Ng SB, Buckingham KJ, Lee C et al (2010) Exome sequencing identifies the cause of a mendelian disorder. Nat Genet 42:30–35. doi:10.1038/ng.499

    Article  CAS  PubMed  Google Scholar 

  51. Choi M, Scholl UI, Ji W et al (2009) Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci 106:19096–19101. doi:10.1073/pnas.0910672106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Leggett RM, Ramirez- Gonzalez RH, Clavijo BJ et al (2013) Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Front Genet 4:288. doi:10.3389/fgene.2013.00288

    Article  PubMed  PubMed Central  Google Scholar 

  53. Patel RK, Jain M (2011) NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7:e30619–e30619. doi:10.1371/journal.pone.0030619

    Article  Google Scholar 

  54. Trivedi UH, Cézard T, Bridgett S et al (2014) Quality control of next-generation sequencing data without a reference. Front Genet 5:111. doi:10.3389/fgene.2014.00111

    Article  PubMed  PubMed Central  Google Scholar 

  55. Smeds L, Künstner A (2010) ConDeTri—a content dependent read trimmer for Illumina data. PLoS One 6:e26314–e26314. doi:10.1371/journal.pone.0026314

    Article  Google Scholar 

  56. Kim SY, Speed TP (2013) Comparing somatic mutation-callers: beyond Venn diagrams. BMC Bioinformatics 14:189. doi:10.1186/1471-2105-14-189

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lim JS, Lee JH (2016) Brain somatic mutations in MTOR leading to focal cortical dysplasia. BMB Rep 49:71–72. doi:10.5483/BMBRep.2016.49.2.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. doi:10.1093/bioinformatics/btp324

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25. doi:10.1186/gb-2009-10-3-r25

    Article  PubMed  PubMed Central  Google Scholar 

  60. Li R, Yu C, Li Y et al (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967. doi:10.1093/bioinformatics/btp336

    Article  CAS  PubMed  Google Scholar 

  61. Pleasance ED, Cheetham RK, Stephens PJ et al (2010) A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463:191–196. doi:10.1038/nature08658

    Article  CAS  PubMed  Google Scholar 

  62. Roth A, Ding J, Morin R et al (2012) JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 28:907–913. doi:10.1093/bioinformatics/bts053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Saunders CT, Wong WSW, Swamy S et al (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28:1811–1817. doi:10.1093/bioinformatics/bts271

    Article  CAS  PubMed  Google Scholar 

  64. Le Gallo M, O’ Hara AJ, Rudd ML et al (2012) Exome sequencing of serous endometrial tumors identifies recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes. Nat Genet 44:1310–1315. doi:10.1038/ng.2455

    Article  PubMed  PubMed Central  Google Scholar 

  65. Shah SP, Roth A, Goya R et al (2012) The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486:395–399. doi:10.1038/nature10933

    CAS  PubMed  Google Scholar 

  66. Ng PC (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814. doi:10.1093/nar/gkg509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249. doi:10.1038/nmeth0410-248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Cooper GM, Stone EA, Asimenos G et al (2005) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 15:901–913. doi:10.1101/gr.3577405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Kircher M, Witten DM, Jain P et al (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315. doi:10.1038/ng.2892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Consortium E, Project EPG (2013) De novo mutations in epileptic encephalopathies. Nature 501(7466):217–221. doi:10.1038/nature12439

    Article  Google Scholar 

  71. Xu B, Zhi N, Hu G et al (2013) Hybrid DNA virus in Chinese patients with seronegative hepatitis discovered by deep sequencing. Proc Natl Acad Sci 110:10264–10269. doi:10.1073/pnas.1303744110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Naccache SN, Hackett J, Delwart EL, Chiu CY (2014) Concerns over the origin of NIH-CQV, a novel virus discovered in Chinese patients with seronegative hepatitis. Proc Natl Acad Sci 111:E976–E976. doi:10.1073/pnas.1317064111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hué S, Gray ER, Gall A et al (2009) Disease-associated XMRV sequences are consistent with laboratory contamination. Retrovirology 7:111–111. doi:10.1186/1742-4690-7-111

    Article  Google Scholar 

  74. Kjartansdóttir KR, Friis- Nielsen J, Asplund M et al (2015) Traces of ATCV-1 associated with laboratory component contamination. Proc Natl Acad Sci 112:E925–E926. doi:10.1073/pnas.1423756112

    Article  PubMed  PubMed Central  Google Scholar 

  75. Cantalupo PG, Katz JP, Pipas JM (2015) HeLa nucleic acid contamination in the cancer genome atlas leads to the misidentification of human papillomavirus 18. J Virol 89:4051–4057. doi:10.1128/JVI.03365-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cibulskis K, McKenna A, Fennell T et al (2011) ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics 27:2601–2602. doi:10.1093/bioinformatics/btr446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Tao ZY, Sui X, Jun C et al (2015) Vector sequence contamination of the plasmodium vivax sequence database in PlasmoDB and in silico correction of 26 parasite sequences. Parasit Vectors 8:318. doi:10.1186/s13071-015-0927-x

    Article  PubMed  PubMed Central  Google Scholar 

  78. Tang KW, Mahabadi BA, Samuelsson T et al (2013) The landscape of viral expression and host gene fusion and adaptation in human cancer. Nat Commun 4:2513. doi:10.1038/ncomms3513

    PubMed  PubMed Central  Google Scholar 

  79. López- Ríos F, Illei PB, Rusch V, Ladanyi M (2004) Evidence against a role for SV40 infection in human mesotheliomas and high risk of false-positive PCR results owing to presence of SV40 sequences in common laboratory plasmids. Lancet 364:1157–1166

    Article  PubMed  Google Scholar 

  80. Borst A, Box ATA, Fluit AC (2004) False-positive results and contamination in nucleic acid amplification assays: suggestions for a prevent and destroy strategy. Eur J Clin Microbiol Infect Dis 23:289–299. doi:10.1007/s10096-004-1100-1

    Article  CAS  PubMed  Google Scholar 

  81. Robasky K, Lewis NE, Church GM (2013) The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet 15:56–62. doi:10.1038/nrg3655

    Article  PubMed  PubMed Central  Google Scholar 

  82. Costello M, Pugh TJ, Fennell TJ et al (2013) Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res 41:e67–e67. doi:10.1093/nar/gks1443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Schirmer M, Ijaz UZ, D’ Amore R et al (2015) Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res 43:e37–e37. doi:10.1093/nar/gku1341

    Article  PubMed  PubMed Central  Google Scholar 

  84. GLENN TC (2011) Field guide to next-generation DNA sequencers. Mol Ecol Resour 11:759–769. doi:10.1111/j.1755-0998.2011.03024.x

    Article  CAS  PubMed  Google Scholar 

  85. Fox EJ, Reid- Bayliss KS, Emond MJ, Loeb LA (2014) Accuracy of next generation sequencing platforms. Next Gener Seq Appl. doi:10.4172/jngsa.1000106

  86. Crino PB (2011) mTOR: a pathogenic signaling pathway in developmental brain malformations. Trends Mol Med 17:734–742. doi:10.1016/j.molmed.2011.07.008

    Article  CAS  PubMed  Google Scholar 

  87. Lee JH, Huynh M, Silhavy JL et al (2012) De novo somatic mutations in components of the PI3K-AKT3-mTOR pathway cause hemimegalencephaly. Nat Genet 44:941–945. doi:10.1038/ng.2329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Espina V, Wulfkuhle JD, Calvert VS et al (2006) Laser-capture microdissection. Nat Protoc 1:586–603. doi:10.1038/nprot.2006.85

    Article  CAS  PubMed  Google Scholar 

  89. Lutz HL, Marra NJ, Grewe F et al (2016) Laser capture microdissection microscopy and genome sequencing of the avian malaria parasite, plasmodium relictum. Parasitol Res 115:4503–4510. doi:10.1007/s00436-016-5237-5

    Article  PubMed  Google Scholar 

  90. Shapiro E, Biezuner T, Linnarsson S (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14:618–630. doi:10.1038/nrg3542

    Article  CAS  PubMed  Google Scholar 

  91. Ding CM, Chiu R, Lau TK et al (2004) MS analysis of single-nucleotide differences in circulating nucleic acids: application to noninvasive prenatal diagnosis. Proc Natl Acad Sci U S A 101:10762–10767. doi:10.1073/pnas.0403962101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Poduri A, Evrony GD, Cai X et al (2012) Somatic activation of AKT3 causes hemispheric developmental brain malformations. Neuron 74:41–48. doi:10.1016/j.neuron.2012.03.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Mirzaa GM, Campbell CD, Solovieff N et al (2016) Association of MTORMutations with developmental brain disorders, including megalencephaly, focal cortical dysplasia, and pigmentary mosaicism. JAMA Neurol 73(7):836–845. doi:10.1001/jamaneurol.2016.0363

    Article  PubMed  PubMed Central  Google Scholar 

  94. Jamuar SS, Lam A-TN, Kircher M et al (2014) Somatic mutations in cerebral cortical malformations. N Engl J Med 371:733–743. doi:10.1056/NEJMoa1314432

    Article  PubMed  PubMed Central  Google Scholar 

  95. Davydov EV, Goode DL, Sirota M et al (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP. PLoS Comp Biol 6:e1001025–e1001013. doi:10.1371/journal.pcbi.1001025

    Article  Google Scholar 

  96. Maschio MD, Ghezzi D, Bony G et al (2012) High-performance and site-directed in utero electroporation by a triple-electrode probe. Nat Commun 3:960–911. doi:10.1038/ncomms1961

    Article  Google Scholar 

  97. Takahashi M, Sato K, Nomura T, Osumi N (2002) Manipulating gene expressions by electroporation in the developing brain of mammalian embryos. Differentiation 70:155–162. doi:10.1046/j.1432-0436.2002.700405.x

    Article  CAS  PubMed  Google Scholar 

  98. Fukuchi-Shimogori T (2001) Neocortex patterning by the secreted signaling molecule FGF8. Science 294:1071–1074. doi:10.1126/science.1064252

    Article  CAS  PubMed  Google Scholar 

  99. Molyneaux BJ, Arlotta P, Menezes JRL, Macklis JD (2007) Neuronal subtype specification in the cerebral cortex. Nat Rev Neurosci 8:427–437. doi:10.1038/nrn2151

    Article  CAS  PubMed  Google Scholar 

  100. Belzung C, Lemoine M (2011) Criteria of validity for animal models of psychiatric disorders: focus on anxiety disorders and depression. Biol Mood Anxiety Disord 1:9. doi:10.1186/2045-5380-1-9

    Article  PubMed  PubMed Central  Google Scholar 

  101. Willner P (1984) The validity of animal models of depression. Psychopharmacology 83:1–16. doi:10.1007/BF00427414

    Article  CAS  PubMed  Google Scholar 

  102. Grone BP, Baraban SC (2015) Animal models in epilepsy research: legacies and new directions. Nat Neurosci 18:339–343. doi:10.1038/nn.3934

    Article  CAS  PubMed  Google Scholar 

  103. Mikuni T, Nishiyama J, Sun Y et al (2016) High-throughput, high-resolution mapping of protein localization in mammalian brain by in vivo genome editing. Cell 165:1803–1817. doi:10.1016/j.cell.2016.04.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kalebic N, Taverna E, Tavano S et al (2016) CRISPR/Cas9-induced disruption of gene expression in mouse embryonic brain and single neural stem cells in vivo. EMBO Rep 17:338–348. doi:10.15252/embr.201541715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (H15C3143, HI13C0208, and H16C0415), Citizens United for Research in Epilepsy, the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2013M3C7A1056564), and the KAIST Future Systems Healthcare Project from the Ministry of Science, ICT and Future Planning. The authors declare that they have no competing interests.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeong Ho Lee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Lim, J.S., Lee, J.H. (2017). Genomic Analysis and In Vivo Functional Validation of Brain Somatic Mutations Leading to Focal Cortical Malformations. In: Frade, J., Gage, F. (eds) Genomic Mosaicism in Neurons and Other Cell Types. Neuromethods, vol 131. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7280-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7280-7_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7279-1

  • Online ISBN: 978-1-4939-7280-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics