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Long-term maturation of human cortical organoids matches key early postnatal transitions

Nature Neuroscience volume 24, pages 331–342 (2021)Cite this article

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Abstract

Human stem-cell-derived models provide the promise of accelerating our understanding of brain disorders, but not knowing whether they possess the ability to mature beyond mid- to late-fetal stages potentially limits their utility. We leveraged a directed differentiation protocol to comprehensively assess maturation in vitro. Based on genome-wide analysis of the epigenetic clock and transcriptomics, as well as RNA editing, we observe that three-dimensional human cortical organoids reach postnatal stages between 250 and 300 days, a timeline paralleling in vivo development. We demonstrate the presence of several known developmental milestones, including switches in the histone deacetylase complex and NMDA receptor subunits, which we confirm at the protein and physiological levels. These results suggest that important components of an intrinsic in vivo developmental program persist in vitro. We further map neurodevelopmental and neurodegenerative disease risk genes onto in vitro gene expression trajectories to provide a resource and webtool (Gene Expression in Cortical Organoids, GECO) to guide disease modeling.

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Fig. 1: Methylation and transcriptional maturation in long-term hCS.
Fig. 2: Biological processes and cell-type marker changes in long-term hCS.
Fig. 3: RNA editing in hCS.
Fig. 4: Developmental isoform switches in hCS.
Fig. 5: Mapping neurodevelopmental and neuropsychiatric disorder genes onto hCS differentiation.
Fig. 6: Mapping neurodegenerative disorder genes onto hCS differentiation.

Data availability

Gene expression data and methylation data are available in the Gene Expression Omnibus (GEO) under accession numbers GSE150122 and GSE150123. The accompanying GECO webtool can be accessed at https://labs.dgsom.ucla.edu/geschwind/files/view/html/GECO.html. The BrainSpan data are available in the database of Genotypes and Phenotypes (dbGaP) under Study Accession phs000755.v2.p1. Single-cell data from human fetal cerebral cortex can be found at http://geschwindlab.dgsom.ucla.edu/pages/codexviewer and at dbGaP under Study accession phs001836. eCLIP data for FXR1 and FMR1are available in GEO with accession number GSE107895. Human cortical organoid single-cell sequencing data are available in GEO with accession number GSE107771. Source data are provided with this paper.

Code availability

The code used in this manuscript can be found at https://github.com/dhglab/human_cortical_organoid_maturation.

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Acknowledgements

We acknowledge experimental support from members of the Pasca Lab at Stanford University: A. M. Pasca, N. Huber, T. Khan, F. Birey, A. Puno, L. Li and T. Li., and members of the Pasca lab and Geschwind lab for helpful discussions and support, including A. Elkins for setting up the GECO web browser. This work was supported by a Distinguished Investigator Award from the Paul G. Allen Frontiers Group (to D.H.G.); by grants from California Institute of Regenerative Medicine (CIRM) and the National Institute of Mental Health Convergent Neuroscience Consortium (U01 MH115745) (to D.H.G. and S.P.P.), the Stanford Human Brain Organogenesis Program in the Wu Tsai Neuroscience Institute (to S.P.P.), Stanford Bio-X (to S.P.P.), the Stanford Wu Tsai Neuroscience Institute Big Idea Grant (to S.P.P.), the Kwan Funds (to S.P.P.), the Senkut Research Fund (to S.P.P.), the Autism Science Foundation (ASF) and the Brain and Behavior Research Foundation Young Investigator award (Brain & Behavior Research Foundation) (to A.G.). S.P.P. is a New York Stem Cell Foundation (NYSCF) Robertson Stem Cell Investigator and a Chan Zuckerberg Initiative (CZI) Ben Barres Investigator.

Author information

Authors and Affiliations

  1. Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Aaron Gordon & Daniel H. Geschwind

  2. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Se-Jin Yoon, Jin Young Park, Jimena Andersen, Alfredo M. Valencia & Sergiu P. Pașca

  3. Stanford Brain Organogenesis, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Se-Jin Yoon, Jin Young Park, Jimena Andersen, Alfredo M. Valencia & Sergiu P. Pașca

  4. Department of Psychiatry, University of California San Diego, San Diego, CA, USA

    Stephen S. Tran

  5. Department of Integrative Biology, University of California Los Angeles, Angeles, CA, USA

    Stephen S. Tran & Xinshu Xiao

  6. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Christopher D. Makinson & John R. Huguenard

  7. Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA

    Steve Horvath

  8. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

    Steve Horvath & Daniel H. Geschwind

  9. Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA

    Xinshu Xiao

  10. Institute for Quantitative and Computational Biology, University of California Los Angeles, Los Angeles, CA, USA

    Xinshu Xiao

  11. Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

  12. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

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  1. Aaron Gordon
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Contributions

A.G., S.P.P. and D.H.G. planned and directed experiments, guided analyses, and wrote the manuscript with assistance from all authors. A.G. performed RNA-seq analysis and methylation analysis. S.-J.Y. performed cell culture, DNA and RNA extraction. S.S.T. performed RNA editing analysis. C.D.M. performed electrophysiology recordings. J.A. performed immunohistochemistry. J.Y.P. and A.M.V. performed western blots. S.H. analyzed the methylation data and interpreted the findings. X.X. supervised RNA editing analysis and interpretation. J.R.H. supervised electrophysiology experiments and interpretation.

Corresponding authors

Correspondence to Sergiu P. Pașca or Daniel H. Geschwind.

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Competing interests

S.P.P. is listed on a patent held by Stanford University that covers the generation of region-specific brain organoids (US patent 62/477,858). All other authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Kristen Brennand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Data description and quality.

a, Timepoints and hiPSC line information for the 62 samples used for RNA sequencing (left). Samples were differentiated from 5 cell lines derived from 4 individuals. Timepoints and hiPSC information for the 50 samples used for DNA methylation (right). Samples were differentiated from 6 cell lines derived from 5 individuals (see Supplementary Tables 1 and 2). Two samples (blue) were hybridized in replicate for quality control purposes and their values were averaged. Each point represents one sample from a specific cell line (y-axis) and differentiation day (x-axis). Full circles represent sample coming from males and rings represent samples coming from females. Gray and white background shading show aggregation of differentiation days into stages. b, Principal component analysis (PCA) of the expression data. The values represent the adjusted r squared of the PC with the covariates indicated. The numbers in brackets on axis titles are the percent of variance explained by the PC. The first 5 PCs, which explain 57.1% of the total variance, show high association with differentiation day. c, Dendrogram of hierarchical clustering of samples demonstrating that differentiation day but no other covariates (individual, Sex, batch) is driving the clustering of samples. d, Violin plots of the variance explained by each of the covariates for each gene. Outlines represent the density of the percent of variance explained. The numbers are the median value of percent of explained variance for each variable. Boxplots in d show: center – median, lower hinge – 25% quantile, upper hinge – 75% quantile, lower whisker – smallest observation greater than or equal to lower hinge –1.5× interquartile range, upper whisker – largest observation less than or equal to upper hinge +1.5× interquartile range. n = 62 samples from 5 hiPSC lines derived from 4 individuals.

Extended Data Fig. 2 Cell stress in hCS.

a, Trajectories of metabolic cell stress genes20 hCS (top) and in vivo (bottom). b, In vitro (left) and in vivo (right) module eigen genes of glycolysis (organoid.Sloan.human.ME.paleturquoise) and ER stress (organoid.human.ME.darkred) previously suggested to be upregulated in vitro20. Gray areas denote time of shift from prenatal to postnatal gene expression. In (a) and (b) shaded gray area around the trajectory represents the 95% confidence interval, vertical gray lines represent birth and vertical gray bars denote the shift from prenatal to postnatal gene expression based on matching to in vivo patterns. For in vitro data n = 62 samples from 5 hiPSC lines derived from 4 individuals and for in vivo data n = 196 from 24 individuals. c, Scatterplot visualization of cells in in developing fetal cortex colored by major cell types22. vRG, ventral radial glia; oRG, outer radial glia; CGE, caudal ganglionic eminence; MGE, medial ganglionic eminence; OPC, oligodendrocyte precursor cell; IP, intermediate progenitors.

Extended Data Fig. 3 Changes in biological processes between early and later stages of differentiation.

a, Number of differentially expressed genes when comparing differentiation day 200 to differentiation day 25 (left) and differentiation day 400 to differentiation day 200 (right). Magenta bar represents upregulated genes and the green bar represents down-regulated genes. b, Top 3 up- and downregulated GO terms enriched in genes ranked by logFC using gene set enrichment analysis, (GSEA; FDR < 0.05). c, Normalized expression of marker genes in vivo for neurons, intermediate progenitors, astrocytes, and radial glia as well as superficial and deep layer cortical neurons. d, Scaled expression of fetal and mature astroglial genes7 during differentiation. A shift between fetal and mature gene sets occurs at ~250 days of hCS differentiation. e, Normalized expression of marker genes for inhibitory neurons and oligodendrocyte precursor cells (OPCs) that are not preserved in hCS. f, Normalized expression of activity-dependent genes that are not preserved in hCS. In (c), (e) and (f) shaded gray area around the trajectory represents the 95% confidence interval, vertical gray lines represent birth and vertical gray bars denote the shift from prenatal to postnatal gene expression based on matching to in vivo patterns. For in vitro data n = 62 samples from 5 hiPSC lines derived from 4 individuals and for in vivo data n = 196 from 24 individuals.

Extended Data Fig. 4 Overlap between hCS and in vivo WGCNA modules.

Overlap of genes in hCS and the BrainSpan in vivo modules. Significant ORs are presented. Modules were clustered using complete-linkage hierarchal clustering. Color represents the OR of each overlap. In vivo neuronal modules (green) and glial modules (purple) are marked.

Extended Data Fig. 5 Overlap between hCS and in vivo editing modules.

a, Overlap of editing sites in hCS and BrainSpan in vivo modules. Significant ORs are presented. b, Distributions showing the closest distances between editing sites from BrainSpan editing modules and FMRP or FXR1P eCLIP peaks (blue). The median of 10,000 sets of control sites (black) is depicted for comparison. See Methods for details of P-value calculation. N, number of editing sites shown. c, Overlap of editing sites within 1000bp of a CLIP site in hCS and BrainSpan in vivo modules. Significant ORs are presented. *** FDR < 0.005.

Extended Data Fig. 6 Expression of select genes in the in vivo fetal cerebral cortex.

a, Immunohistochemistry of HDAC2 and the deep layer marker CTIP2 (BCL11B) at post conception week 21 (PCW21). CP, cortical plate. Scale bars, 100 μm. The Immunohistochemistry experiment was performed once. b, Scatterplot visualization of cells in developing fetal human cerebral cortex colored by major cell types 22. vRG, ventral radial glia; oRG, outer radial glia; CGE, caudal ganglionic eminence; MGE, medial ganglionic eminence; OPC, oligodendrocyte precursor cell, IP, Intermediate progenitors.

Extended Data Fig. 7 Mapping neurodegenerative and epilepsy disorder genes onto hCS differentiation.

Mapping of genes associated with progressive supranuclear palsy (PSP) and frontotemporal dementia (FTD) (a), and epilepsy (b) onto hCS differentiation trajectories. The first column shows clustering of scaled normalized expression of genes associated with a disorder. Genes (in rows) are clustered using hierarchical clustering on the Euclidean distance between genes. Samples (columns) are ordered by differentiation day (represented by gray bars) with the earliest days on the left and latest timepoints on the right. The 5 most representative genes (highest correlation with the cluster eigengene) are shown. The second column shows the cluster eigengenes (first principal component) for the identified gene clusters. Shaded gray area around the trajectory line represents the 95% confidence interval. The third column shows the top GO terms enriched in the identified clusters. The fourth column shows cell types over expressed in either all the genes associated with a disorder (above line) or in the genes from the identified clusters. Number and color represent the fold change. Significance was tested using a one-sided permutation test with 100,000 permutations. P values were corrected for multiple testing using the Benjamini-Hochberg method. * FDR < 0.05, ** FDR < 0.01, *** FDR < 0.005. n = 62 samples from 5 hiPSC lines derived from 4 individuals. IP, intermediate progenitors; GlutN, glutamatergic neurons; IN, interneurons; OPC, oligodendrocyte progenitor cells.

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Gordon, A., Yoon, SJ., Tran, S.S. et al. Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat Neurosci 24, 331–342 (2021). https://doi.org/10.1038/s41593-021-00802-y

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