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PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids


Crucial decisions involving cell fate and connectivity that shape the distinctive development of the human brain occur in the embryonic and fetal stages—stages that are difficult to access and investigate in humans. The last decade has seen an impressive increase in resources—from atlases and databases to biological models—that is progressively lifting the curtain on this critical period. In this review, we describe the current state of genomic, transcriptomic, and epigenomic datasets charting the development of normal human brain with a particular focus on recent single-cell technologies. We discuss the emergence of brain organoids generated from pluripotent stem cells as a model to compensate for the limited availability of fetal tissue. Indeed, comparisons of neural lineages, transcriptional dynamics, and noncoding element activity between fetal brain and organoids have helped identify gene regulatory networks functioning at early stages of brain development. Altogether, we argue that large multi-omics investigations have pushed brain development into the “big data” era, and that current and future transversal approaches needed to leverage both fetal brain and organoid resources promise to answer major questions of brain biology and psychiatry.

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Fig. 1: Integrative approaches to study human brain development.


  1. 1.

    Sansom SN, Livesey FJ. Gradients in the brain: the control of the development of form and function in the cerebral cortex. Cold Spring Harb Perspect Biol. 2009;1:a002519.

    CAS  Article  Google Scholar 

  2. 2.

    Molnar Z, Clowry GJ, Sestan N, Alzu’bi A, Bakken T, Hevner RF, et al. New insights into the development of the human cerebral cortex. J Anat. 2019;235:432–51.

    CAS  Article  Google Scholar 

  3. 3.

    Rakic P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 1995;18:383–8.

    CAS  Article  Google Scholar 

  4. 4.

    Shine JM, Breakspear M, Bell PT, Ehgoetz Martens KA, Shine R, Koyejo O, et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci. 2019;22:289–96.

    CAS  Article  Google Scholar 

  5. 5.

    Helfrich RF, Knight RT. Cognitive neurophysiology of the prefrontal cortex. Handb Clin Neurol. 2019;163:35–59.

    Article  Google Scholar 

  6. 6.

    Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, Szafer A, et al. Transcriptional landscape of the prenatal human brain. Nature. 2014;508:199–206.

    CAS  Article  Google Scholar 

  7. 7.

    Kim S, Shendure J. Mechanisms of interplay between transcription factors and the 3D genome. Mol Cell. 2019;76:306–19.

    CAS  Article  Google Scholar 

  8. 8.

    Kim TK, Shiekhattar R. Architectural and functional commonalities between enhancers and promoters. Cell. 2015;162:948–59.

    CAS  Article  Google Scholar 

  9. 9.

    Long HK, Prescott SL, Wysocka J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell. 2016;167:1170–87.

    CAS  Article  Google Scholar 

  10. 10.

    Visel A, Taher L, Girgis H, May D, Golonzhka O, Hoch RV, et al. A high-resolution enhancer atlas of the developing telencephalon. Cell. 2013;152:895–908.

    CAS  Article  Google Scholar 

  11. 11.

    Dickel DE, Ypsilanti AR, Pla R, Zhu Y, Barozzi I, Mannion BJ, et al. Ultraconserved enhancers are required for normal development. Cell. 2018;172:491–9.

    CAS  Article  Google Scholar 

  12. 12.

    Rajarajan P, Borrman T, Liao W, Schrode N, Flaherty E, Casino C, et al. Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk. Science. 2018;362.

  13. 13.

    Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51:431–44.

    CAS  Article  Google Scholar 

  14. 14.

    Eiraku M, Watanabe K, Matsuo-Takasaki M, Kawada M, Yonemura S, Matsumura M, et al. Self-organized formation of polarized cortical tissues from ESCs and its active manipulation by extrinsic signals. Cell Stem Cell. 2008;3:519–32.

    CAS  Article  Google Scholar 

  15. 15.

    Mariani J, Simonini MV, Palejev D, Tomasini L, Coppola G, Szekely AM, et al. Modeling human cortical development in vitro using induced pluripotent stem cells. Proc Natl Acad Sci USA. 2012;109:12770–5.

    Article  Google Scholar 

  16. 16.

    Lancaster MA, Renner M, Martin CA, Wenzel D, Bicknell LS, Hurles ME, et al. Cerebral organoids model human brain development and microcephaly. Nature. 2013;501:373–9.

    CAS  Article  Google Scholar 

  17. 17.

    Bershteyn M, Nowakowski TJ, Pollen AA, Di Lullo E, Nene A, Wynshaw-Boris A, et al. Human iPSC-derived cerebral organoids model cellular features of lissencephaly and reveal prolonged mitosis of outer radial glia. Cell Stem Cell. 2017;20:435–49.

    CAS  Article  Google Scholar 

  18. 18.

    Gandal MJ, Leppa V, Won H, Parikshak NN, Geschwind DH. The road to precision psychiatry: translating genetics into disease mechanisms. Nat Neurosci. 2016;19:1397–407.

    CAS  Article  Google Scholar 

  19. 19.

    Talkowski ME, Rosenfeld JA, Blumenthal I, Pillalamarri V, Chiang C, Heilbut A, et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell. 2012;149:525–37.

    CAS  Article  Google Scholar 

  20. 20.

    Coe BP, Stessman HAF, Sulovari A, Geisheker MR, Bakken TE, Lake AM, et al. Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity. Nat Genet. 2019;51:106–16.

    CAS  Article  Google Scholar 

  21. 21.

    Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell. 2020;180:568–84. e23

    CAS  Article  Google Scholar 

  22. 22.

    Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, et al. DECIPHER: database of chromosomal imbalance and phenotype in humans using Ensembl resources. Am J Hum Genet. 2009;84:524–33.

    CAS  Article  Google Scholar 

  23. 23.

    Philippakis AA, Azzariti DR, Beltran S, Brookes AJ, Brownstein CA, Brudno M, et al. The Matchmaker Exchange: a platform for rare disease gene discovery. Hum Mutat 2015;36:915–21.

    Article  Google Scholar 

  24. 24.

    Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015;43:D789–98.

    CAS  Article  Google Scholar 

  25. 25.

    Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, et al. ClinGen-the clinical genome resource. N Engl J Med. 2015;372:2235–42.

    CAS  Article  Google Scholar 

  26. 26.

    Landrum MJ, Chitipiralla S, Brown GR, Chen C, Gu B, Hart J, et al. ClinVar: improvements to accessing data. Nucleic Acids Res. 2020;48:D835–D44.

    CAS  Article  Google Scholar 

  27. 27.

    Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016;536:285–91.

    CAS  Article  Google Scholar 

  28. 28.

    Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-Andre V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–47.

    CAS  Article  Google Scholar 

  29. 29.

    Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    CAS  Article  Google Scholar 

  30. 30.

    Psychiatric GCSC. A framework for interpreting genome-wide association studies of psychiatric disorders. Mol Psychiatry. 2009;14:10–7.

    CAS  Article  Google Scholar 

  31. 31.

    McConnell MJ, Moran JV, Abyzov A, Akbarian S, Bae T, Cortes-Ciriano I, et al. Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. Science. 2017;356.

  32. 32.

    Abyzov A, Vaccarino FM, Urban AE, Sarangi V. Approaches and methods for variant analysis in the genome of a single cell. In: Moskalev A, editor. Biomarkers of Human Aging and Longevity: Springer, Cham; 2019. p. 203–28.

  33. 33.

    D’Gama AM, Walsh CA. Somatic mosaicism and neurodevelopmental disease. Nat Neurosci. 2018;21:1504–14.

    CAS  Article  Google Scholar 

  34. 34.

    Keil JM, Qalieh A, Kwan KY. Brain transcriptome databases: a user’s guide. J Neurosci. 2018;38:2399–412.

    CAS  Article  Google Scholar 

  35. 35.

    Bakken TE, Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, et al. A comprehensive transcriptional map of primate brain development. Nature. 2016;535:367–75.

    CAS  Article  Google Scholar 

  36. 36.

    Johnson MB, Kawasawa YI, Mason CE, Krsnik Z, Coppola G, Bogdanovic D, et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron. 2009;62:494–509.

    CAS  Article  Google Scholar 

  37. 37.

    Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.

    CAS  Article  Google Scholar 

  38. 38.

    Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature. 2011;478:519–23.

    CAS  Article  Google Scholar 

  39. 39.

    Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 2012;489:391–9.

    CAS  Article  Google Scholar 

  40. 40.

    Ayoub AE, Oh S, Xie Y, Leng J, Cotney J, Dominguez MH, et al. Transcriptional programs in transient embryonic zones of the cerebral cortex defined by high-resolution mRNA sequencing. Proc Natl Acad Sci USA. 2011;108:14950–5.

    Article  Google Scholar 

  41. 41.

    Belgard TG, Marques AC, Oliver PL, Abaan HO, Sirey TM, Hoerder-Suabedissen A, et al. A transcriptomic atlas of mouse neocortical layers. Neuron. 2011;71:605–16.

    CAS  Article  Google Scholar 

  42. 42.

    Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    CAS  Article  Google Scholar 

  43. 43.

    Consortium GT. Erratum: Genetic effects on gene expression across human tissues. Nature. 2018;553:530.

    CAS  Article  Google Scholar 

  44. 44.

    e GP. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat Genet. 2017;49:1664–70.

    CAS  Article  Google Scholar 

  45. 45.

    Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.

    CAS  Article  Google Scholar 

  46. 46.

    Psych EC, Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, et al. The PsychENCODE project. Nat Neurosci. 2015;18:1707–12.

    CAS  Article  Google Scholar 

  47. 47.

    Lahnemann D, Koster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21:31.

    Article  Google Scholar 

  48. 48.

    Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014.

  49. 49.

    Fan HC, Fu GK, Fodor SP. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science. 2015;347:1258367.

    CAS  Article  Google Scholar 

  50. 50.

    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.

    CAS  Article  Google Scholar 

  51. 51.

    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187–201.

    CAS  Article  Google Scholar 

  52. 52.

    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360:176–82.

    CAS  Article  Google Scholar 

  53. 53.

    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.

    CAS  Article  Google Scholar 

  54. 54.

    Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, et al. Orchestrating single-cell analysis with bioconductor. Nat Methods. 2020;17:137–45.

    CAS  Article  Google Scholar 

  55. 55.

    Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15:e8746.

    Article  Google Scholar 

  56. 56.

    Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495–502.

    CAS  Article  Google Scholar 

  57. 57.

    Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20:296

    CAS  Article  Google Scholar 

  58. 58.

    Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019;37:547–54.

    CAS  Article  Google Scholar 

  59. 59.

    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2018.

  60. 60.

    Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol. 2019;37:1482–92.

    CAS  Article  Google Scholar 

  61. 61.

    La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, et al. RNA velocity of single cells. Nature. 2018;560:494–8.

    CAS  Article  Google Scholar 

  62. 62.

    van den Hurk M, Bardy C. Single-cell multimodal transcriptomics to study neuronal diversity in human stem cell-derived brain tissue and organoid models. J Neurosci Methods. 2019;325:108350.

    CAS  Article  Google Scholar 

  63. 63.

    Macaulay IC, Ponting CP, Voet T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 2017;33:155–68.

    CAS  Article  Google Scholar 

  64. 64.

    Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019;363:1463–7.

    CAS  Article  Google Scholar 

  65. 65.

    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888–902.e1821.

    CAS  Article  Google Scholar 

  66. 66.

    Cadwell CR, Palasantza A, Jiang X, Berens P, Deng Q, Yilmaz M, et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat Biotechnol. 2016;34:199–203.

    CAS  Article  Google Scholar 

  67. 67.

    Mayer S, Chen J, Velmeshev D, Mayer A, Eze UC, Bhaduri A, et al. Multimodal single-cell analysis reveals physiological maturation in the developing human neocortex. Neuron. 2019;102:143–58.e7.

    CAS  Article  Google Scholar 

  68. 68.

    Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science. 2018;362.

  69. 69.

    Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science. 2018;362.

  70. 70.

    Amiri A, Coppola G, Scuderi S, Wu F, Roychowdhury T, Liu F, et al. Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science. 2018;362.

  71. 71.

    Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362.

  72. 72.

    Rhie SK, Schreiner S, Witt H, Armoskus C, Lay FD, Camarena A, et al. Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation. Sci Adv 2018;4:eaav8550.

    CAS  Article  Google Scholar 

  73. 73.

    An JY, Lin K, Zhu L, Werling DM, Dong S, Brand H, et al. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science. 2018;362.

  74. 74.

    de la Torre-Ubieta L, Stein JL, Won H, Opland CK, Liang D, Lu D, et al. The dynamic landscape of open chromatin during human cortical neurogenesis. Cell. 2018;172:289–304.

    CAS  Article  Google Scholar 

  75. 75.

    Won H, de la Torre-Ubieta L, Stein JL, Parikshak NN, Huang J, Opland CK, et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature. 2016;538:523–7.

    CAS  Article  Google Scholar 

  76. 76.

    Hodge RD, Bakken TE, Miller JA, Smith KA, Barkan ER, Graybuck LT, et al. Conserved cell types with divergent features in human versus mouse cortex. Nature. 2019.

  77. 77.

    Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci USA. 2015;112:7285–90.

    CAS  Article  Google Scholar 

  78. 78.

    Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352:1586–90.

    CAS  Article  Google Scholar 

  79. 79.

    Lake BB, Chen S, Sos BC, Fan J, Kaeser GE, Yung YC, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol. 2018;36:70–80.

    CAS  Article  Google Scholar 

  80. 80.

    Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019;364:685–9.

    CAS  Article  Google Scholar 

  81. 81.

    Bhaduri A, Di Lullo E, Jung D, Muller S, Crouch EE, Espinosa CS, et al. Outer radial glia-like cancer stem cells contribute to heterogeneity of glioblastoma. Cell Stem Cell. 2020;26:48–63. e6

    CAS  Article  Google Scholar 

  82. 82.

    Jakel S, Agirre E, Mendanha Falcao A, van Bruggen D, Lee KW, Knuesel I, et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature. 2019;566:543–7.

    CAS  Article  Google Scholar 

  83. 83.

    Schirmer L, Velmeshev D, Holmqvist S, Kaufmann M, Werneburg S, Jung D, et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature. 2019;573:75–82.

    CAS  Article  Google Scholar 

  84. 84.

    Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–7.

    CAS  Article  Google Scholar 

  85. 85.

    Camp JG, Badsha F, Florio M, Kanton S, Gerber T, Wilsch-Brauninger M, et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl Acad Sci USA. 2015;112:15672–7.

    CAS  Article  Google Scholar 

  86. 86.

    Nowakowski TJ, Bhaduri A, Pollen AA, Alvarado B, Mostajo-Radji MA, Di Lullo E, et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science. 2017;358:1318–23.

    CAS  Article  Google Scholar 

  87. 87.

    Bhaduri A, Andrews MG, Mancia Leon W, Jung D, Shin D, Allen D, et al. Cell stress in cortical organoids impairs molecular subtype specification. Nature. 2020;578:142–8.

    CAS  Article  Google Scholar 

  88. 88.

    Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature. 2018;555:524–8.

    CAS  Article  Google Scholar 

  89. 89.

    Fan X, Dong J, Zhong S, Wei Y, Wu Q, Yan L, et al. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res 2018;28:730–45.

    CAS  Article  Google Scholar 

  90. 90.

    Polioudakis D, de la Torre-Ubieta L, Langerman J, Elkins AG, Shi X, Stein JL, et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron. 2019;103:785–801.e8.

    CAS  Article  Google Scholar 

  91. 91.

    Zhong S, Ding W, Sun L, Lu Y, Dong H, Fan X, et al. Decoding the development of the human hippocampus. Nature 2020;577:531–6.

    CAS  Article  Google Scholar 

  92. 92.

    La Manno G, Gyllborg D, Codeluppi S, Nishimura K, Salto C, Zeisel A, et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell. 2016;167:566–80.e19.

    CAS  Article  Google Scholar 

  93. 93.

    Pollen AA, Nowakowski TJ, Chen J, Retallack H, Sandoval-Espinosa C, Nicholas CR, et al. Molecular identity of human outer radial glia during cortical development. Cell. 2015;163:55–67.

    CAS  Article  Google Scholar 

  94. 94.

    Thomsen ER, Mich JK, Yao Z, Hodge RD, Doyle AM, Jang S, et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nat Methods. 2016;13:87–93.

    CAS  Article  Google Scholar 

  95. 95.

    Johnson MB, Wang PP, Atabay KD, Murphy EA, Doan RN, Hecht JL, et al. Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex. Nat Neurosci. 2015;18:637–46.

    CAS  Article  Google Scholar 

  96. 96.

    Nowakowski TJ, Pollen AA, Sandoval-Espinosa C, Kriegstein AR. Transformation of the radial glia scaffold demarcates two stages of human cerebral cortex development. Neuron. 2016;91:1219–27.

    CAS  Article  Google Scholar 

  97. 97.

    Lee JH, Huynh M, Silhavy JL, Kim S, Dixon-Salazar T, Heiberg A, et al. De novo somatic mutations in components of the PI3K-AKT3-mTOR pathway cause hemimegalencephaly. Nat Genet. 2012;44:941–5.

    CAS  Article  Google Scholar 

  98. 98.

    Poduri A, Evrony GD, Cai X, Elhosary PC, Beroukhim R, Lehtinen MK, et al. Somatic activation of AKT3 causes hemispheric developmental brain malformations. Neuron. 2012;74:41–8.

    CAS  Article  Google Scholar 

  99. 99.

    Marin-Valencia I, Guerrini R, Gleeson JG. Pathogenetic mechanisms of focal cortical dysplasia. Epilepsia. 2014;55:970–8.

    Article  Google Scholar 

  100. 100.

    Yu X, Zecevic N. Dorsal radial glial cells have the potential to generate cortical interneurons in human but not in mouse brain. J Neurosci. 2011;31:2413–20.

    CAS  Article  Google Scholar 

  101. 101.

    Hansen DV, Lui JH, Flandin P, Yoshikawa K, Rubenstein JL, Alvarez-Buylla A, et al. Non-epithelial stem cells and cortical interneuron production in the human ganglionic eminences. Nat Neurosci. 2013;16:1576–87.

    CAS  Article  Google Scholar 

  102. 102.

    Letinic K, Zoncu R, Rakic P. Origin of GABAergic neurons in the human neocortex. Nature. 2002;417:645–9.

    CAS  Article  Google Scholar 

  103. 103.

    Ma T, Wang C, Wang L, Zhou X, Tian M, Zhang Q, et al. Subcortical origins of human and monkey neocortical interneurons. Nat Neurosci. 2013;16:1588–97.

    CAS  Article  Google Scholar 

  104. 104.

    Liu SJ, Nowakowski TJ, Pollen AA, Lui JH, Horlbeck MA, Attenello FJ, et al. Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol. 2016;17:67

    CAS  Article  Google Scholar 

  105. 105.

    Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, et al. The accessible chromatin landscape of the human genome. Nature. 2012;489:75–82.

    CAS  Article  Google Scholar 

  106. 106.

    Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.

    CAS  Article  Google Scholar 

  107. 107.

    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WMI, et al. Comprehensive integration of single cell data. Cell 2019;177:1888–1902.e1821.

    CAS  Article  Google Scholar 

  108. 108.

    Nott A, Holtman IR, Coufal NG, Schlachetzki JCM, Yu M, Hu R, et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science. 2019;366:1134–9.

    CAS  Article  Google Scholar 

  109. 109.

    Kadoshima T, Sakaguchi H, Nakano T, Soen M, Ando S, Eiraku M, et al. Self-organization of axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human ES cell-derived neocortex. Proc Natl Acad Sci USA. 2013;110:20284–9.

    CAS  Article  Google Scholar 

  110. 110.

    Mariani J, Coppola G, Zhang P, Abyzov A, Provini L, Tomasini L, et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell. 2015;162:375–90.

    CAS  Article  Google Scholar 

  111. 111.

    Pasca AM, Sloan SA, Clarke LE, Tian Y, Makinson CD, Huber N, et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat Methods. 2015.

  112. 112.

    Rigamonti A, Repetti GG, Sun C, Price FD, Reny DC, Rapino F, et al. Large-scale production of mature neurons from human pluripotent stem cells in a three-dimensional suspension culture system. Stem cell Rep. 2016;6:993–1008.

    CAS  Article  Google Scholar 

  113. 113.

    Sakaguchi H, Kadoshima T, Soen M, Narii N, Ishida Y, Ohgushi M, et al. Generation of functional hippocampal neurons from self-organizing human embryonic stem cell-derived dorsomedial telencephalic tissue. Nat Commun. 2015;6:8896.

    CAS  Article  Google Scholar 

  114. 114.

    Muguruma K, Nishiyama A, Kawakami H, Hashimoto K, Sasai Y. Self-organization of polarized cerebellar tissue in 3D culture of human pluripotent stem cells. Cell Rep. 2015;10:537–50.

    CAS  Article  Google Scholar 

  115. 115.

    Monzel AS, Smits LM, Hemmer K, Hachi S, Moreno EL, van Wuellen T, et al. Derivation of human midbrain-specific organoids from neuroepithelial stem cells. Stem cell Rep. 2017;8:1144–54.

    CAS  Article  Google Scholar 

  116. 116.

    Xiang Y, Tanaka Y, Cakir B, Patterson B, Kim KY, Sun P, et al. hESC-derived thalamic organoids form reciprocal projections when fused with cortical organoids. Cell Stem Cell. 2019;24:487–97. e7

    CAS  Article  Google Scholar 

  117. 117.

    Qian X, Nguyen HN, Song MM, Hadiono C, Ogden SC, Hammack C, et al. Brain-region-specific organoids using mini-bioreactors for modeling ZIKV exposure. Cell. 2016;165:1238–54.

    CAS  Article  Google Scholar 

  118. 118.

    Cederquist GY, Asciolla JJ, Tchieu J, Walsh RM, Cornacchia D, Resh MD, et al. Specification of positional identity in forebrain organoids. Nat Biotechnol 2019;37:436–44.

    CAS  Article  Google Scholar 

  119. 119.

    Trujillo CA, Gao R, Negraes PD, Gu J, Buchanan J, Preissl S, et al. Complex oscillatory waves emerging from cortical organoids model early human brain network development. Cell Stem Cell. 2019;25:558–69.

    CAS  Article  Google Scholar 

  120. 120.

    Giandomenico SL, Mierau SB, Gibbons GM, Wenger LMD, Masullo L, Sit T, et al. Cerebral organoids at the air-liquid interface generate diverse nerve tracts with functional output. Nat Neurosci. 2019;22:669–79.

    CAS  Article  Google Scholar 

  121. 121.

    Lancaster MA, Corsini NS, Wolfinger S, Gustafson EH, Phillips AW, Burkard TR, et al. Guided self-organization and cortical plate formation in human brain organoids. Nat Biotechnol. 2017;35:659–66.

    CAS  Article  Google Scholar 

  122. 122.

    Qian X, Su Y, Adam CD, Deutschmann AU, Pather SR, Goldberg EM, et al. Sliced human cortical organoids for modeling distinct cortical layer formation. Cell Stem Cell. 2020.

  123. 123.

    Sloan SA, Darmanis S, Huber N, Khan TA, Birey F, Caneda C, et al. Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron. 2017;95:779–90.

    CAS  Article  Google Scholar 

  124. 124.

    Marton RM, Miura Y, Sloan SA, Li Q, Revah O, Levy RJ, et al. Differentiation and maturation of oligodendrocytes in human three-dimensional neural cultures. Nat Neurosci. 2019;22:484–91.

    CAS  Article  Google Scholar 

  125. 125.

    Madhavan M, Nevin ZS, Shick HE, Garrison E, Clarkson-Paredes C, Karl M, et al. Induction of myelinating oligodendrocytes in human cortical spheroids. Nat Methods. 2018;15:700–6.

    CAS  Article  Google Scholar 

  126. 126.

    Mansour AA, Goncalves JT, Bloyd CW, Li H, Fernandes S, Quang D, et al. An in vivo model of functional and vascularized human brain organoids. Nat Biotechnol. 2018;36:432–41.

    CAS  Article  Google Scholar 

  127. 127.

    Cakir B, Xiang Y, Tanaka Y, Kural MH, Parent M, Kang YJ, et al. Engineering of human brain organoids with a functional vascular-like system. Nat Methods 2019;16:1169–75.

    CAS  Article  Google Scholar 

  128. 128.

    Worsdorfer P, Dalda N, Kern A, Kruger S, Wagner N, Kwok CK, et al. Generation of complex human organoid models including vascular networks by incorporation of mesodermal progenitor cells. Sci Rep. 2019;9:15663.

    CAS  Article  Google Scholar 

  129. 129.

    Paredes I, Himmels P, Ruiz de Almodovar C. Neurovascular communication during CNS development. Dev Cell. 2018;45:10–32.

    CAS  Article  Google Scholar 

  130. 130.

    Ormel PR, Vieira de Sa R, van Bodegraven EJ, Karst H, Harschnitz O, Sneeboer MAM, et al. Microglia innately develop within cerebral organoids. Nat Commun. 2018;9:4167.

    CAS  Article  Google Scholar 

  131. 131.

    Song L, Yuan X, Jones Z, Vied C, Miao Y, Marzano M, et al. Functionalization of brain region-specific spheroids with isogenic microglia-like cells. Sci Rep. 2019;9:11055.

    CAS  Article  Google Scholar 

  132. 132.

    Abud EM, Ramirez RN, Martinez ES, Healy LM, Nguyen CHH, Newman SA, et al. iPSC-derived human microglia-like cells to study neurological diseases. Neuron. 2017;94:278–93.

    CAS  Article  Google Scholar 

  133. 133.

    Abreu CM, Gama L, Krasemann S, Chesnut M, Odwin-Dacosta S, Hogberg HT, et al. Microglia increase inflammatory responses in iPSC-derived human BrainSpheres. Front Microbiol. 2018;9:2766.

    Article  Google Scholar 

  134. 134.

    Birey F, Andersen J, Makinson CD, Islam S, Wei W, Huber N, et al. Assembly of functionally integrated human forebrain spheroids. Nature. 2017;545:54–9.

    CAS  Article  Google Scholar 

  135. 135.

    Xiang Y, Tanaka Y, Patterson B, Kang YJ, Govindaiah G, Roselaar N, et al. Fusion of regionally specified hPSC-derived organoids models human brain development and interneuron migration. Cell Stem Cell. 2017;21:383–98.

    CAS  Article  Google Scholar 

  136. 136.

    Dang J, Tiwari SK, Agrawal K, Hui H, Qin Y, Rana TM. Glial cell diversity and methamphetamine-induced neuroinflammation in human cerebral organoids. Mol Psychiatry. 2020.

  137. 137.

    Marchetto MC, Carromeu C, Acab A, Yu D, Yeo GW, Mu Y, et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell. 2010;143:527–39.

    CAS  Article  Google Scholar 

  138. 138.

    Pasca SP, Portmann T, Voineagu I, Yazawa M, Shcheglovitov A, Pasca AM, et al. Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat Med. 2011;17:1657–62.

    CAS  Article  Google Scholar 

  139. 139.

    Marchetto MC, Belinson H, Tian Y, Freitas BC, Fu C, Vadodaria K, et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol Psychiatry. 2017;22:820–35.

    CAS  Article  Google Scholar 

  140. 140.

    Brennand KJ, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S. et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature. 2011;473:221–5.

    CAS  Article  Google Scholar 

  141. 141.

    Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

    CAS  Article  Google Scholar 

  142. 142.

    Reilly SK, Yin J, Ayoub AE, Emera D, Leng J, Cotney J, et al. Evolutionary genomics evolutionary changes in promoter and enhancer activity during human corticogenesis. Science. 2015;347:1155–9.

    CAS  Article  Google Scholar 

  143. 143.

    Velasco S, Kedaigle AJ, Simmons SK, Nash A, Rocha M, Quadrato G, et al. Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature. 2019;570:523–7.

    CAS  Article  Google Scholar 

  144. 144.

    Yoon SJ, Elahi LS, Pasca AM, Marton RM, Gordon A, Revah O, et al. Reliability of human cortical organoid generation. Nat Methods. 2019;16:75–8.

    CAS  Article  Google Scholar 

  145. 145.

    Quadrato G, Nguyen T, Macosko EZ, Sherwood JL, Min Yang S, Berger DR, et al. Cell diversity and network dynamics in photosensitive human brain organoids. Nature. 2017;545:48–53.

    CAS  Article  Google Scholar 

  146. 146.

    Tanaka Y, Cakir B, Xiang Y, Sullivan GJ, Park IH. Synthetic analyses of single-cell transcriptomes from multiple brain organoids and fetal brain. Cell Rep. 2020;30:1682–9.

    CAS  Article  Google Scholar 

  147. 147.

    Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16:1289–96.

    CAS  Article  Google Scholar 

  148. 148.

    Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36:421–7.

    CAS  Article  Google Scholar 

  149. 149.

    Mora-Bermudez F, Badsha F, Kanton S, Camp JG, Vernot B, Kohler K, et al. Differences and similarities between human and chimpanzee neural progenitors during cerebral cortex development. eLife. 2016;5.

  150. 150.

    Field AR, Jacobs FMJ, Fiddes IT, Phillips APR, Reyes-Ortiz AM, LaMontagne E, et al. Structurally conserved primate LncRNAs are transiently expressed during human cortical differentiation and influence cell-type-specific genes. Stem cell Rep. 2019;12:245–57.

    CAS  Article  Google Scholar 

  151. 151.

    Pollen AA, Bhaduri A, Andrews MG, Nowakowski TJ, Meyerson OS, Mostajo-Radji MA, et al. Establishing cerebral organoids as models of human-specific brain evolution. Cell. 2019;176:743–56.e17.

    CAS  Article  Google Scholar 

  152. 152.

    Kanton S, Boyle MJ, He Z, Santel M, Weigert A, Sanchis-Calleja F, et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature. 2019;574:418–22.

    CAS  Article  Google Scholar 

  153. 153.

    Dryden NH, Broome LR, Dudbridge F, Johnson N, Orr N, Schoenfelder S, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 2014;24:1854–68.

    CAS  Article  Google Scholar 

  154. 154.

    Fang R, Yu M, Li G, Chee S, Liu T, Schmitt AD, et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 2016;26:1345–8.

    CAS  Article  Google Scholar 

  155. 155.

    Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919–22.

    CAS  Article  Google Scholar 

  156. 156.

    Patwardhan RP, Lee C, Litvin O, Young DL, Pe’er D, Shendure J. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat Biotechnol. 2009;27:1173–5.

    CAS  Article  Google Scholar 

  157. 157.

    Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L, Rogov P, et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat Biotechnol. 2012;30:271–7.

    CAS  Article  Google Scholar 

  158. 158.

    Ulirsch JC, Nandakumar SK, Wang L, Giani FC, Zhang X, Rogov P, et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell. 2016;165:1530–45.

    CAS  Article  Google Scholar 

  159. 159.

    Tewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell. 2016;165:1519–29.

    CAS  Article  Google Scholar 

  160. 160.

    Grossman SR, Zhang X, Wang L, Engreitz J, Melnikov A, Rogov P, et al. Systematic dissection of genomic features determining transcription factor binding and enhancer function. Proc Natl Acad Sci USA. 2017;114:E1291–E300.

    CAS  Article  Google Scholar 

  161. 161.

    Inoue F, Kircher M, Martin B, Cooper GM, Witten DM, McManus MT, et al. A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity. Genome Res. 2017;27:38–52.

    CAS  Article  Google Scholar 

  162. 162.

    Inoue F, Kreimer A, Ashuach T, Ahituv N, Yosef N. Identification and massively parallel characterization of regulatory elements driving neural induction. Cell Stem Cell. 2019;25:713–27.

    CAS  Article  Google Scholar 

  163. 163.

    Arnold CD, Gerlach D, Stelzer C, Boryn LM, Rath M, Stark A. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 2013;339:1074–7.

    CAS  Article  Google Scholar 

  164. 164.

    Vanhille L, Griffon A, Maqbool MA, Zacarias-Cabeza J, Dao LT, Fernandez N, et al. High-throughput and quantitative assessment of enhancer activity in mammals by CapStarr-seq. Nat Commun. 2015;6:6905.

    CAS  Article  Google Scholar 

  165. 165.

    Vockley CM, D’Ippolito AM, McDowell IC, Majoros WH, Safi A, Song L, et al. Direct GR binding sites potentiate clusters of TF binding across the human Genome. Cell. 2016;166:1269–81.

    CAS  Article  Google Scholar 

  166. 166.

    Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819–23.

    CAS  Article  Google Scholar 

  167. 167.

    Canver MC, Smith EC, Sher F, Pinello L, Sanjana NE, Shalem O, et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature. 2015;527:192–7.

    CAS  Article  Google Scholar 

  168. 168.

    Gasperini M, Findlay GM, McKenna A, Milbank JH, Lee C, Zhang MD, et al. CRISPR/Cas9-mediated scanning for regulatory elements required for HPRT1 expression via thousands of large, programmed genomic deletions. Am J Hum Genet. 2017;101:192–205.

    CAS  Article  Google Scholar 

  169. 169.

    Sanjana NE, Wright J, Zheng K, Shalem O, Fontanillas P, Joung J, et al. High-resolution interrogation of functional elements in the noncoding genome. Science. 2016;353:1545–9.

    CAS  Article  Google Scholar 

  170. 170.

    Rajagopal N, Srinivasan S, Kooshesh K, Guo Y, Edwards MD, Banerjee B, et al. High-throughput mapping of regulatory DNA. Nat Biotechnol. 2016;34:167–74.

    CAS  Article  Google Scholar 

  171. 171.

    Diao Y, Li B, Meng Z, Jung I, Lee AY, Dixon J, et al. A new class of temporarily phenotypic enhancers identified by CRISPR/Cas9-mediated genetic screening. Genome Res. 2016;26:397–405.

    CAS  Article  Google Scholar 

  172. 172.

    Hilton IB, D’Ippolito AM, Vockley CM, Thakore PI, Crawford GE, Reddy TE, et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat Biotechnol. 2015;33:510–7.

    CAS  Article  Google Scholar 

  173. 173.

    Thakore PI, D’Ippolito AM, Song L, Safi A, Shivakumar NK, Kabadi AM, et al. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat Methods. 2015;12:1143–9.

    CAS  Article  Google Scholar 

  174. 174.

    Fulco CP, Munschauer M, Anyoha R, Munson G, Grossman SR, Perez EM, et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science. 2016;354:769–73.

    CAS  Article  Google Scholar 

  175. 175.

    Korkmaz G, Lopes R, Ugalde AP, Nevedomskaya E, Han R, Myacheva K, et al. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat Biotechnol. 2016;34:192–8.

    CAS  Article  Google Scholar 

  176. 176.

    Xie S, Duan J, Li B, Zhou P, Hon GC. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol Cell. 2017;66:285–99.

    CAS  Article  Google Scholar 

  177. 177.

    Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell. 2019;176:377–90.

    CAS  Article  Google Scholar 

  178. 178.

    Klein JC, Chen W, Gasperini M, Shendure J. Identifying novel enhancer elements with CRISPR-based screens. ACS Chem Biol. 2018;13:326–32.

    CAS  Article  Google Scholar 

  179. 179.

    Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14:955–8.

    CAS  Article  Google Scholar 

  180. 180.

    Zhang Y, Sloan SA, Clarke LE, Caneda C, Plaza CA, Blumenthal PD, et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron. 2016;89:37–53.

    CAS  Article  Google Scholar 

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We thank Jeremy Schreiner for proof editing this paper.

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Paper conception/outline: FMV and AJ. Paper writing: FMV, AJ, SS, DC, and AA. Display item preparation (Fig. 1): AJ, SS, and FMV. (Table 1) curated by AJ. (Box 1) curated by AJ, SS, DC, FMV. All authors participated in paper editing and proofreading.

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Correspondence to Flora M. Vaccarino.

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Jourdon, A., Scuderi, S., Capauto, D. et al. PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids. Neuropsychopharmacol. 46, 70–85 (2021).

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