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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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.
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.
Santoro, M. R., Bray, S. M. & Warren, S. T. Molecular mechanisms of fragile X syndrome: a twenty-year perspective. Annu. Rev. Pathol. 7, 219–245 (2012).
Richter, J. D., Bassell, G. J. & Klann, E. Dysregulation and restoration of translational homeostasis in fragile X syndrome. Nat. Rev. Neurosci. 16, 595–605 (2015).
Nelson, D. L., Orr, H. T. & Warren, S. T. The unstable repeats—three evolving faces of neurological disease. Neuron 77, 825–843 (2013).
Hagerman, R. J. et al. Fragile X syndrome. Nat. Rev. Dis. Prim. 3, 17065 (2017).
Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).
Pasca, A. M. et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat. Methods 12, 671–678 (2015).
Qian, X. et al. Brain-region-specific organoids using mini-bioreactors for modeling ZIKV exposure. Cell 165, 1238–1254 (2016).
Xie, N. et al. Reactivation of FMR1 by CRISPR/Cas9-mediated deletion of the expanded CGG-repeat of the fragile X chromosome. PLoS ONE 11, e0165499 (2016).
Kostovic, I., Sedmak, G. & Judas, M. Neural histology and neurogenesis of the human fetal and infant brain. Neuroimage 188, 743–773 (2019).
Agulhon, C. et al. Expression of FMR1, FXR1, and FXR2 genes in human prenatal tissues. J. Neuropathol. Exp. Neurol. 58, 867–880 (1999).
Tamanini, F. et al. Differential expression of FMR1, FXR1 and FXR2 proteins in human brain and testis. Hum. Mol. Genet 6, 1315–1322 (1997).
Raj, N. et al. Cell-type-specific profiling of human cellular models of fragile X syndrome reveal PI3K-dependent defects in translation and neurogenesis. Cell Rep. 35, 108991 (2021).
Yoon, K. J. et al. Temporal control of mammalian cortical neurogenesis by m6A methylation. Cell 171, 877–889 (2017).
Kim, J. Y. et al. DISC1 regulates new neuron development in the adult brain via modulation of AKT-mTOR signaling through KIAA1212. Neuron 63, 761–773 (2009).
Gotz, M. & Huttner, W. B. The cell biology of neurogenesis. Nat. Rev. Mol. Cell Biol. 6, 777–788 (2005).
Gleeson, J. G. et al. Doublecortin, a brain-specific gene mutated in human X-linked lissencephaly and double cortex syndrome, encodes a putative signaling protein. Cell 92, 63–72 (1998).
Qian, X. et al. Sliced human cortical organoids for modeling distinct cortical layer formation. Cell Stem Cell 26, 766–781 e769 (2020).
Clancy, B. et al. Web-based method for translating neurodevelopment from laboratory species to humans. Neuroinformatics 5, 79–94 (2007).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
Tsunekawa, Y. et al. Cyclin D2 in the basal process of neural progenitors is linked to non-equivalent cell fates. EMBO J. 31, 1879–1892 (2012).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Huber, K. M., Gallagher, S. M., Warren, S. T. & Bear, M. F. Altered synaptic plasticity in a mouse model of fragile X mental retardation. Proc. Natl Acad. Sci. USA 99, 7746–7750 (2002).
Bear, M. F., Huber, K. M. & Warren, S. T. The mGluR theory of fragile X mental retardation. Trends Neurosci. 27, 370–377 (2004).
Michalon, A. et al. Chronic pharmacological mGlu5 inhibition corrects fragile X in adult mice. Neuron 74, 49–56 (2012).
Dolen, G. et al. Correction of fragile X syndrome in mice. Neuron 56, 955–962 (2007).
Youssef, E. A. et al. Effect of the mGluR5-NAM basimglurant on behavior in adolescents and adults with fragile X syndrome in a randomized, double-blind, placebo-controlled trial: FragXis phase 2 results. Neuropsychopharmacology 43, 503–512 (2018).
Gross, C. et al. Increased expression of the PI3K enhancer PIKE mediates deficits in synaptic plasticity and behavior in fragile X syndrome. Cell Rep. 11, 1–10 (2015).
Gross, C. et al. Selective role of the catalytic PI3K subunit p110β in impaired higher order cognition in fragile X syndrome. Cell Rep. 11, 681–688 (2015).
Gross, C. et al. Isoform-selective phosphoinositide 3-kinase inhibition ameliorates a broad range of fragile X syndrome-associated deficits in a mouse model. Neuropsychopharmacology 44, 324–333 (2019).
Van Nostrand, E. L. et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods 13, 508–514 (2016).
Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).
Basu, S. N., Kollu, R. & Banerjee-Basu, S. AutDB: a gene reference resource for autism research. Nucleic Acids Res 37, D832–D836 (2009).
Jia, P., Sun, J., Guo, A. Y. & Zhao, Z. SZGR: a comprehensive schizophrenia gene resource. Mol. Psychiatry 15, 453–462 (2010).
Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet 48, 1031–1036 (2016).
Flint, J. & Eskin, E. Genome-wide association studies in mice. Nat. Rev. Genet 13, 807–817 (2012).
Tajul-Arifin, K. et al. Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome. Genome Res. 13, 1416–1429 (2003).
Chenier, S. et al. CHD2 haploinsufficiency is associated with developmental delay, intellectual disability, epilepsy and neurobehavioural problems. J. Neurodev. Disord. 6, 9 (2014).
Kim, Y. J. et al. Chd2 is necessary for neural circuit development and long-term memory. Neuron 100, 1180–1193 (2018).
Liu, X. S. et al. Rescue of fragile X syndrome neurons by DNA methylation editing of the FMR1 gene. Cell 172, 979–992 (2018).
Graef, J. D. et al. Partial FMRP expression is sufficient to normalize neuronal hyperactivity in fragile X neurons. Eur. J. Neurosci. 51, 2143–2157 (2020).
Telias, M., Segal, M. & Ben-Yosef, D. Neural differentiation of fragile X human embryonic stem cells reveals abnormal patterns of development despite successful neurogenesis. Dev. Biol. 374, 32–45 (2013).
Doers, M. E. et al. iPSC-derived forebrain neurons from FXS individuals show defects in initial neurite outgrowth. Stem Cells Dev. 23, 1777–1787 (2014).
Halevy, T., Czech, C. & Benvenisty, N. Molecular mechanisms regulating the defects in fragile X syndrome neurons derived from human pluripotent stem cells. Stem Cell Rep. 4, 37–46 (2015).
Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).
Sasai, Y. Next-generation regenerative medicine: organogenesis from stem cells in 3D culture. Cell Stem Cell 12, 520–530 (2013).
Gonzalez, C. et al. Modeling amyloid beta and tau pathology in human cerebral organoids. Mol Psychiatry 23, 2363–2374 (2018).
Lin, Y. T. et al. APOE4 causes widespread molecular and cellular alterations associated with Alzheimer’s disease phenotypes in human iPSC-derived brain cell types. Neuron 98, 1141–1154 (2018).
Seo, J. et al. Inhibition of p25/Cdk5 attenuates tauopathy in mouse and iPSC models of frontotemporal dementia. J. Neurosci. 37, 9917–9924 (2017).
Raja, W. K. et al. Self-organizing 3D human neural tissue derived from induced pluripotent stem cells recapitulate Alzheimer’s disease phenotypes. PLoS ONE 11, e0161969 (2016).
Lee, H. K. et al. Three dimensional human neuro-spheroid model of Alzheimer’s disease based on differentiated induced pluripotent stem cells. PLoS ONE 11, e0163072 (2016).
Shang, L. et al. β-cell dysfunction due to increased ER stress in a stem cell model of Wolfram syndrome. Diabetes 63, 923–933 (2014).
Teo, A. K. et al. Early developmental perturbations in a human stem cell model of MODY5/HNF1B pancreatic hypoplasia. Stem Cell Rep. 6, 357–367 (2016).
Nishita, M. et al. Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness. Sci. Rep. 7, 1 (2017).
Hosokawa, Y. et al. Insulin-producing cells derived from ‘induced pluripotent stem cells’ of patients with fulminant type 1 diabetes: vulnerability to cytokine insults and increased expression of apoptosis-related genes. J. Diabetes Investig. 9, 481–493 (2017).
Zeng, H. et al. An isogenic human ESC platform for functional evaluation of genome-wide-association-study-identified diabetes genes and drug discovery. Cell Stem Cell 19, 326–340 (2016).
Bhattacharya, A. et al. Genetic removal of p70 S6 kinase 1 corrects molecular, synaptic, and behavioral phenotypes in fragile X syndrome mice. Neuron 76, 325–337 (2012).
Bhattacharya, A. et al. Targeting translation control with p70 S6 kinase 1 inhibitors to reverse phenotypes in fragile X syndrome mice. Neuropsychopharmacology 41, 1991–2000 (2015).
Gross, C. & Bassell, G. J. Excess protein synthesis in FXS patient lymphoblastoid cells can be rescued with a p110β-selective inhibitor. Mol. Med 18, 336–345 (2012).
Gross, C. et al. Excess phosphoinositide 3-kinase subunit synthesis and activity as a novel therapeutic target in fragile X syndrome. J. Neurosci. 30, 10624–10638 (2010).
Hoeffer, C. A. et al. Altered mTOR signaling and enhanced CYFIP2 expression levels in subjects with fragile X syndrome. Genes Brain Behav. 11, 332–341 (2012).
Kumari, D. et al. Identification of fragile X syndrome specific molecular markers in human fibroblasts: a useful model to test the efficacy of therapeutic drugs. Hum. Mutat. 35, 1485–1494 (2014).
Sharma, A. et al. Dysregulation of mTOR signaling in fragile X syndrome. J. Neurosci. 30, 694–702 (2010).
Dockendorff, T. C. & Labrador, M. The fragile X protein and genome function. Mol. Neurobiol. 56, 711–721 (2019).
Bardoni, B., Mandel, J. L. & Fisch, G. S. FMR1 gene and fragile X syndrome. Am. J. Med. Genet 97, 153–163 (2000).
Castrén, M. L. Cortical neurogenesis in fragile X syndrome. Front. Biosci. (Schol. Ed.). 8, 160–168 (2016).
Willemsen, R., Bontekoe, C. J., Severijnen, L. A. & Oostra, B. A. Timing of the absence of FMR1 expression in full mutation chorionic villi. Hum. Genet. 110, 601–605 (2002).
Zhang, F. et al. Fragile X mental retardation protein modulates the stability of its m6A-marked messenger RNA targets. Hum. Mol. Genet 27, 3936–3950 (2018).
Sourial, M. & Doering, L. C. Abnormal neural precursor cell regulation in the early postnatal fragile X mouse hippocampus. Brain Res 1666, 58–69 (2017).
Tervonen, T. A. et al. Aberrant differentiation of glutamatergic cells in neocortex of mouse model for fragile X syndrome. Neurobiol. Dis. 33, 250–259 (2009).
Castren, M. et al. Altered differentiation of neural stem cells in fragile X syndrome. Proc. Natl Acad. Sci. USA 102, 17834–17839 (2005).
Khalfallah, O. et al. Depletion of the fragile X mental retardation protein in embryonic stem cells alters the kinetics of neurogenesis. Stem Cells 35, 374–385 (2017).
Luo, Y. et al. Fragile X mental retardation protein regulates proliferation and differentiation of adult neural stem/progenitor cells. PLoS Genet. 6, e1000898 (2010).
Mariani, J. et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell 162, 375–390 (2015).
Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).
Wen, Z. et al. Synaptic dysregulation in a human iPS cell model of mental disorders. Nature 515, 414–418 (2014).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Xiang, Y. et al. Fusion of regionally specified hPSC-derived organoids models human brain development and interneuron migration. Cell Stem Cell 21, 383–398 (2017).
Xiang, Y. et al. hESC-derived thalamic organoids form reciprocal projections when fused with cortical organoids. Cell Stem Cell 24, 487–497 (2019).
Lovci, M. T. et al. Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nat. Struct. Mol. Biol. 20, 1434–1442 (2013).
Warde-Farley, D. et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214–W220 (2010).
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.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Rudolf Jaenisch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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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).
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 Figs. 1–9 and legends for Tables 1–12.
Supplementary Table 1
Supplementary Table 2
Cortical layer marker expression
Supplementary Table 3
Supplementary Table 4
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
Supplementary Table 12
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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