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A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies

Nature Neuroscience volume 24, pages 1377–1391 (2021)Cite this article

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Abstract

Fragile X syndrome (FXS) is caused by the loss of fragile X mental retardation protein (FMRP), an RNA-binding protein that can regulate the translation of specific mRNAs. In this study, we developed an FXS human forebrain organoid model and observed that the loss of FMRP led to dysregulated neurogenesis, neuronal maturation and neuronal excitability. Bulk and single-cell gene expression analyses of FXS forebrain organoids revealed that the loss of FMRP altered gene expression in a cell-type-specific manner. The developmental deficits in FXS forebrain organoids could be rescued by inhibiting the phosphoinositide 3-kinase pathway but not the metabotropic glutamate pathway disrupted in the FXS mouse model. We identified a large number of human-specific mRNAs bound by FMRP. One of these human-specific FMRP targets, CHD2, contributed to the altered gene expression in FXS organoids. Collectively, our study revealed molecular, cellular and electrophysiological abnormalities associated with the loss of FMRP during human brain development.

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Fig. 1: FMRP regulates cortical neurogenesis in a human forebrain organoid model.
Fig. 2: Loss of FMRP impairs cortical neurodevelopment.
Fig. 3: Loss of FMRP alters synapse formation and enhances neuronal excitability.
Fig. 4: Loss of FMRP causes a more pervasive gene expression alteration in forebrain organoids than in mouse brain.
Fig. 5: Lack of FMRP causes altered neural differentiation and aberrant developmental trajectory in forebrain organoids.
Fig. 6: PI3K inhibitors, not an mGluR5 antagonist, rescue neurodevelopmental defects in FXS organoids.
Fig. 7: Identification of human FMRP mRNA targets.
Fig. 8: CHD2 is a human-specific mRNA target of FMRP.

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Data availability

We have deposited the scRNA-seq, RNA-seq and eCLIP-seq data into the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. The accession number is GSE146878. Source data are provided with this paper.

Code availability

RNA-seq data were analyzed following the standard pipeline with STAR 2.7 software (https://hbctraining.github.io/Intro-to-rnaseq-hpc-O2/lessons/03_alignment.html) and DESeq2 (https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html). CLIP-seq data were analyzed following the pipeline with STAR 2.7 software and CLIPper (https://github.com/YeoLab/clipper). scRNA-seq data were anlyzed following the pipeline with CellRanger 3.0.2 software (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/tutorial_ov), Seurat 3.1 (https://satijalab.org/seurat/articles/pbmc3k_tutorial.html) and Monocle 3 (https://cole-trapnell-lab.github.io/monocle3/docs/starting/). Analysis code is available upon reasonable request.

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Acknowledgements

This work is dedicated to the late S. Warren and was supported, in part, by the National Institutes of Health (NS091859 to S.T.W. and P.J.; HD104458 to S.T.W., P.J., G.B. and Z.W.; HD082013 to G.B.; AI131130 to Z.W. and P.J.; MH123711 and MH121102 to Z.W.; and NS051630 and NS111602 to P.J.), the Department of Defense (W81XWH1910068 to E.G.A. and W81XWH1910353 to Z.W.), the Edward Mallinckrodt, Jr. Foundation (Z.W.) and the FRAXA Research Foundation (Y.K.). We would like to thank S. Sloan at Emory University for help with scRNA-seq analyses. This study was supported, in part, by the Emory Integrated Genomics Core, which is subsidized by the Emory University School of Medicine and is one of the Emory Integrated Core Facilities. Additional support was provided by the Georgia Clinical & Translational Science Alliance of the National Institutes of Health under Award Number UL1TR002378. This work was performed with the support of the Georgia Genomics and Bioinformatics Core (GGBC) at the University of Georgia. The scRNA-seq work was performed at the GGBC at the University of Georgia, Athens. We thank M. Alabady and his team at the GGBC for their support and contribution to this work.

Author information

Author notes

  1. These authors contributed equally: Yunhee Kang, Ying Zhou, Yujing Li.

Authors and Affiliations

  1. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA

    Yunhee Kang, Yujing Li, Feiran Zhang, Stephen T. Warren, Emily G. Allen & Peng Jin

  2. Department of Psychiatry and Behavioral Scieces, Emory University School of Medicine, Atlanta, GA, USA

    Ying Zhou, Yanfei Han, Weibo Niu, Juan Dou, Chongchong Xu & Zhexing Wen

  3. The Graduate Program in Genetics and Molecular Biology, Emory University, Atlanta, GA, USA

    Jie Xu

  4. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ziyi Li

  5. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    Shiying Liu & Hao Feng

  6. Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China

    Wen Huang & Ranhui Duan

  7. Department of Gynecology and Obstetrics, The Second Hospital of Jilin University, Changchun, Jilin, China

    Tianmin Xu

  8. Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, USA

    Nisha Raj, Gary J. Bassell & Zhexing Wen

  9. Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, USA

    Hao Wu

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  1. Yunhee Kang
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Contributions

Y.K. led the molecular aspects of the project, and Y.Z led the cellular aspects of the project. Y.L. performed the eCLIP-seq analysis. Y.H. performed electrophysiology analyses. Z.L., S.L., H.F., F.Z. and H.W. performed bioinformatic analyses. J.X., W.N., J.D. and C.X. helped with data collection. G.J.B. and N.R. provided FXS iPSC lines. G.J.B., J.P., S.T.W. and E.G.A. helped with data analyses and interpretation. Z.W., P.J. and Y.K. designed the project and wrote the manuscript.

Corresponding authors

Correspondence to Peng Jin or Zhexing Wen.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Rudolf Jaenisch 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 FMRP regulates cortical neurogenesis in a human forebrain organoid model.

a, Quantification of the size of control and FXS forebrain organoids. Data are presented as mean ± s.e.m. (n = 30 organoids from each line; one-way ANOVA). b, c, Loss of FMRP reduces NPC proliferation. Shown are representative images (b) and quantification (c) of the proportion of Ki67+ proliferating neuronal progenitor cells in total PAX6+ dorsal forebrain neuronal progenitor cells of both control and FXS-derived forebrain organoids at day 56. Data are presented as mean ± s.e.m. (n = 6 organoids from each line with 15–20 cortical structures analyzed per organoid; ****P < 0.0001, one-way ANOVA). Scale bars: 50 µm. d, e, D56 forebrain organoids were pulsed with EdU (10 μM) for 2 hr. Shown are representative images (d) and quantification (e) of the proportion of EdU+ proliferating cells in total SOX2+ NPCs in both control and FXS-derived forebrain organoids at day 56. Data are presented as mean ± s.e.m. (n = 62 cortical structures from at least ten organoids each condition; ****P < 0.0001, one-way ANOVA). Scale bars: 50 µm.

Extended Data Fig. 2 Loss of FMRP impairs cortical neurodevelopment.

a, Quantification of the proportions of TBR2+ IPCs, CTIP2+ cortical neurons, and SOX2+ NPCs in total DAPI+ cells in control and FXS-derived forebrain organoids at day 56. Data are presented as mean ± s.e.m. (n = 6 organoids from each line with 15–20 cortical structures analyzed per organoid; ****P < 0.0001, one-way ANOVA). b, c, Loss of FMRP dysregulates distribution of TBR2+ intermediate neural progenitor cells. Shown are representative images (b) and quantification (c) of the proportion of TBR2+ IPCs in MAP2+ layer of both control and FXS-derived forebrain organoids. Yellow dashed lines indicate the borders of VZ-like structures. Data are presented as mean ± s.e.m. (n = 6 organoids from each condition with 15–20 cortical structures analyzed per organoid; ****P < 0.0001, one-way ANOVA). Scale bars: 50 µm.

Extended Data Fig. 3 Loss of FMRP accelerates cortical layer formation.

a, b, Shown are sample images (a) and quantification (b) of relative thickness of SOX2+CTIP2−VZ layer and CTIP2+ CP layer in day 56 forebrain organoids. Yellow dashed lines indicate the borders between VZ and CP layers. Data are presented as mean ± s.e.m. (n = 15 cortical structures per organoid from at least 12 organoids each line; ***P = 0.0005, ****P < 0.0001, one-way ANOVA). Scale bars: 50 µm. c, d, Shown are sample images (c) and quantification (d) of relative thickness of SOX2+MAP2− VZ layer and MAP2+ CP layer in day 56 forebrain organoids. Yellow dashed lines indicate the borders between VZ and CP layers. Data are presented as mean ± s.e.m. (n = 15 cortical structures per organoid from at least 12 organoids each line; ****P < 0.0001, two-way ANOVA). Scale bars: 50 µm. e, f, Shown are sample images (e) and quantification (f) of relative thickness of SOX2+TBR1−VZ layer and TBR1+ CP layer in day 56 forebrain organoids. Yellow dashed lines indicate the borders between VZ and CP layers. Data are presented as mean ± s.e.m. (n = 15 cortical structures per organoid from at least 12 organoids each line; ***P = 0.0009, ****P < 0.0001, one-way ANOVA). Scale bars: 50 µm. g, h, Quantification of the proportions of TBR1+ cortical neurons (c) and SOX2+ NPCs (d) in total DAPI+ cells in control and FXS-derived forebrain organoids at day 56. Data are presented as mean ± s.e.m. (n = 6 organoids from each line with 15–20 cortical structures analyzed per organoid; ****P < 0.0001, one-way ANOVA). i-l, Analysis of marker distribution across the VZ/CP layers. Data are presented as mean ± s.e.m. (n = 10 organoids from control or FXS lines each with 15–20 cortical structures analyzed per organoid; ****p < 0.0001; one-way ANOVA).

Extended Data Fig. 4 Loss of FMRP prevents differentiation of GABAergic interneurons.

a, b, Quantifications of the numbers of GABA+ inhibitory neurons (a) and CaMKIIα+ excitatory neurons (b) in a field of 588 µm X 588 µm in both control and FXS-derived forebrain organoids. Data are presented as mean ± s.e.m. (n = 10 sections from 10 organoids each line; **p = 0.0012 (FXS2 v.s. CTRL1 in b) or 0.0097 (FXS3 v.s. CTRL1 in b), ***p = 0.0008 (b), ****P < 0.0001, one-way ANOVA). c, Sample images of RNA expression of DLX2, PAX6 and SOX2 by RNAscope in control and FXS forebrain organoids at day 56. Blue staining represents DAPI. Scale bars: 50 µm. d, Quantification of ratio of DLX2+ MGE-like NPC area v.s. PAX6+ dorsal forebrain NPC area in D28 and D56 control and FXS-derived forebrain organoids. Data are presented as mean ± s.e.m. (n = 5 organoids from each condition with 15–20 sections analyzed per organoid; ****P < 0.0001, one-way ANOVA).

Extended Data Fig. 5 Basic electrophysiological characterization of FXS neurons in forebrain organoids.

a, Shown are sample images of a CTIP2+ cortical neurons that was filled with Alexa Fluor-594 dye after the electrophysiological recording. Scale bars: 20 µm. Experiment was repeated at least 13 times independently for each condition with similar results. b–d, Characterization of passive membrane properties, including the resting membrane potential (RMP; b), input resistance (RIN; c), and membrane capacitance (d). Data are presented as mean ± s.e.m. (two-tailed unpaired t test or one-way ANOVA). e-h, Basic properties of action potentials, including the amplitude (e), threshold (f), half-width (g), and the rise time (h) of the first action potentials. Data are presented as mean ± s.e.m. (two-tailed unpaired t test or one-way ANOVA). i-k, Characterization of transient inward currents and sustained outward currents of FXS neurons. Shown are sample tracings of transient inward and sustained outward currents (i), quantification of transient inward current-voltage curve (j) and peak density of transient inward currents (k). Data are presented as mean ± s.e.m. (two-tailed unpaired t test or one-way ANOVA). Cell number (n) recorded and analyzed in each condition is indicated.

Extended Data Fig. 6 Expression of Kv4.2 voltage-gated potassium channel in human forebrain organoids.

Sample images (a) and quantification (b) of Western blots are presented for comparing Kv4.2 protein level in D56 control and FXS forebrain organoids using GAPDH as loading control. Data are presented as mean ± s.d. (n = 3 cultures; **P = 0.0085 (FXS1 v.s. CTRL1) or 0.0033 (FXS2 v.s. CTRL1), ***P = 0.0003, one-way ANOVA).

Source data

Extended Data Fig. 7 The PANTHER overrepresentation test on the upregulated genes in FXS organoid at each stage show enrichment in distinct pathways.

The upregulated genes in FXS at a given developmental stage, D28, D56, or D84 show specific pathway enrichment. The upregulated genes at D28 in FXS organoids are enriched in ciliary locomotion of neuron, axoneme assembly, and other synaptogenesis related pathways while the up-regulated genes at D56 in FXS organoids show more relevance to the pathways associated with synaptic function. Interestingly, genes with higher expression in FXS than in control organoids at the more developed D84 are concentrated in DNA replication, cell division and cell cycle pathways. This suggests aberrant developmental manifestation in FXS organoids. The numbers on the bars indicate the two-sided p values by Fisher’s exact test. The p values have been adjusted for multiple testing using Bonferroni correction.

Extended Data Fig. 8 Lack of FMRP causes altered neural differentiation and aberrant developmental trajectory in forebrain organoids.

a, A heat map of expression of annotation reference genes in 14 cell type specific clusters present during human forebrain organoids shows the differential expression of various marker genes for specific cell types in each cluster. (C1: fate determining stage neurons toward excitatory neuron, C2: excitatory neuron, C3: neural stem cell /radial glia2, C4: immature neuron, C5: neural stem cell /radial glia1 cell, C6: glial progenitor, C7: inhibitory neuron, C8: astrocyte, C9: radial glia, C10: astrocyte, C11: immature neuron very early stage, C12: oligodendrocyte, C13: ectodermal origin non-neuronal cells, C14: non-neuronal cells) b, The expression of neural stem cell/progenitor marker, SOX2 (red) and differentiated cortical plate neuron marker, BCL11B (CTIP2, green) were presented simultaneously in the UMAP plot. Compared to control, cells in FXS organoids expressing BCL11B/CTIP2 at low level were increased and widely distributed spanning various cell types regardless of differentiation status and cell function. Many of these are accompanied by the expression of SOX2. Significantly high co-expression rate of the NPC marker, SOX2, and cortical plate marker, BCL11B in the C7, young inhibitory neuron cluster (19% in FXS forebrain organoids compared to 0% in control forebrain organoids), suggest that the spatiotemporal regulation of SOX2 and BCL11B expression critical for proper specification and lamination of neurons is severely perturbed in FXS organoids. Data are presented as mean ± s.e.m. (n=3 single cell RNAseq of 3 independent culture sets, **P=0.0025, two-tailed unpaired t test) (c) Among the 14 clusters, the highest number of DEGs were detected in the young inhibitory neuron cluster, C7. PANTHER analyses show high relevance to regulation of synapse organization, learning and memory, and forebrain development with down-regulated DEGs and protein targeting. mRNA stability and regulation of cell cycle. Yellow represents up-regulated genes and blue represents down-regulated genes. The numbers on the bars are the associated two-sided p-values by Fisher’s Exact test. The p values have been adjusted for multiple testing using Bonferroni correction. d, Transcriptional features of the cluster 6 at the developmental break point between FXS and control (arrow in red) in the time trajectory was assessed. The Monocle cluster 4, one of the major break point in the time trajectory, has marker genes associated with cell proliferation and regulation of DNA methylation, (for example, KMT2A), neuron migration and regulation of neuron projection development (ACAP3), synapse organization and axon guidance (NFASC).

Extended Data Fig. 9 The overlap between human fetal brain DEGs and cell type specific DEGs.

(a) all single cell cluster specific DEGs were compared with human fragile X fetal brain RNAseq DEGs. The highest overlap is marked with an asterisk above the bar. b, PANTHER gene ontology revealed that theyare involved in GABAergic neuron differentiation, forebrain neuron generation and differentiation. Downregulated genes are enriched in regulation of neural precursor cell, neurogenesis and proliferation, cerebral cortex and forebrain development, gliogenesis, and cell differentiation. The numbers on the bars are two-sdied p-values by Fisher’s exact test. The p values have been adjusted for multiple testing using Bonferroni correction.

Extended Data Fig. 10 An overlap between disease risk genes and the subset of human and mouse FMRP binding genes are shown.

The percentage of overlap between Schizophrenia and ASD risk genes and human-specific, mouse-specific or human-mouse shared FMRP binding genes are indicated. Statistical significance was calculated by Pearson’s χ2 tests, and p-values are indicated.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and legends for Tables 1–12.

Reporting Summary

Supplementary Table 1

iPSC lines

Supplementary Table 2

Cortical layer marker expression

Supplementary Table 3

Organoid_DEGs

Supplementary Table 4

FetalBrain_DEGs

Supplementary Table 5

Summary of RNA-seq analysis with various analysis packages

Supplementary Table 6

Seurat cluster markers

Supplementary Table 7

Seurat cluster DE

Supplementary Table 8

Seurat cluster ontology

Supplementary Table 9

PI3K pathway DEGs in clusters

Supplementary Table 10

Pseudotime trajectory cluster markers

Supplementary Table 11

eCLIP_targets

Supplementary Table 12

Overlaps_Organoids_CHD2

Source data

Source Data Fig. 8

Unprocessed western blots.

Source Data Extended Data Fig. 6

Unprocessed western blots.

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Kang, Y., Zhou, Y., Li, Y. et al. A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies. Nat Neurosci 24, 1377–1391 (2021). https://doi.org/10.1038/s41593-021-00913-6

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