Altered neuronal migratory trajectories in human cerebral organoids derived from individuals with neuronal heterotopia

Abstract

Malformations of the human cortex represent a major cause of disability1. Mouse models with mutations in known causal genes only partially recapitulate the phenotypes and are therefore not unlimitedly suited for understanding the molecular and cellular mechanisms responsible for these conditions2. Here we study periventricular heterotopia (PH) by analyzing cerebral organoids derived from induced pluripotent stem cells (iPSCs) of patients with mutations in the cadherin receptor–ligand pair DCHS1 and FAT4 or from isogenic knockout (KO) lines1,3. Our results show that human cerebral organoids reproduce the cortical heterotopia associated with PH. Mutations in DCHS1 and FAT4 or knockdown of their expression causes changes in the morphology of neural progenitor cells and result in defective neuronal migration dynamics only in a subset of neurons. Single-cell RNA-sequencing (scRNA-seq) data reveal a subpopulation of mutant neurons with dysregulated genes involved in axon guidance, neuronal migration and patterning. We suggest that defective neural progenitor cell (NPC) morphology and an altered navigation system in a subset of neurons underlie this form of PH.

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Fig. 1: Mutations in DCHS1 and FAT4 cause neuronal heterotopia and disturbances in the morphology of NPCs in cerebral organoids.
Fig. 2: scRNA-seq reveals alterations in mutant NPCs.
Fig. 3: Time-lapse imaging of mutant or knockdown neurons reveals an altered migration pattern.
Fig. 4: scRNA-seq reveals an altered population of neurons.

Data availability

The scRNA-seq data used in this study have been deposited in the Gene Expression Omnibus under accession number GSE124031. All relevant accession codes are provided. Further details on the methods can be found in the Life Sciences Reporting Summary. Additional data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. 1.

    Cappello, S. et al. Mutations in genes encoding the cadherin receptor-ligand pair DCHS1 and FAT4 disrupt cerebral cortical development. Nat. Genet. 45, 1300–1308 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Romero, D. M., Bahi-Buisson, N. & Francis, F. Genetics and mechanisms leading to human cortical malformations. Semin. Cell. Dev. Biol. 76, 33–75 (2018).

    CAS  Article  Google Scholar 

  3. 3.

    Mansour, S. et al. Van Maldergem syndrome: further characterisation and evidence for neuronal migration abnormalities and autosomal recessive inheritance. Eur. J. Hum. Genet. 20, 1024–1031 (2012).

    Article  Google Scholar 

  4. 4.

    Liu, J. S. Molecular genetics of neuronal migration disorders. Curr. Neurol. Neurosci. Rep. 11, 171–178 (2011).

    CAS  Article  Google Scholar 

  5. 5.

    Cardoso, C. et al. Periventricular heterotopia, mental retardation, and epilepsy associated with 5q14.3-q15 deletion. Neurology 72, 784–792 (2009).

    CAS  Article  Google Scholar 

  6. 6.

    Dubeau, F. et al. Periventricular and subcortical nodular heterotopia. A study of 33 patients. Brain 118(Pt 5), 1273–1287 (1995).

    Article  Google Scholar 

  7. 7.

    Tassi, L. et al. Electroclinical, MRI and neuropathological study of 10 patients with nodular heterotopia, with surgical outcomes. Brain 128, 321–337 (2004).

    Article  Google Scholar 

  8. 8.

    Aghakhani, Y. et al. The role of periventricular nodular heterotopia in epileptogenesis. Brain 128, 641–651 (2005).

    Article  Google Scholar 

  9. 9.

    Heinzen, E. L. et al. De novo and inherited private variants in MAP1B in periventricular nodular heterotopia. PLoS Genet. 14, e1007281 (2018).

    Article  Google Scholar 

  10. 10.

    O’Neill, A. C. et al. A primate-specific isoform of PLEKHG6 regulates neurogenesis and neuronal migration. Cell Rep. 25, 2729–2741.e6 (2018).

    Article  Google Scholar 

  11. 11.

    Lancaster, M. A. & Knoblich, J. A. Generation of cerebral organoids from human pluripotent stem cells. Nat. Protoc. 9, 2329–2340 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Ishiuchi, T., Misaki, K., Yonemura, S., Takeichi, M. & Tanoue, T. Mammalian fat and dachsous cadherins regulate apical membrane organization in the embryonic cerebral cortex. J. Cell Biol. 185, 959–967 (2009).

    CAS  Article  Google Scholar 

  13. 13.

    Cappello, S. et al. The Rho-GTPase cdc42 regulates neural progenitor fate at the apical surface. Nat. Neurosci. 9, 1099–1107 (2006).

    CAS  Article  Google Scholar 

  14. 14.

    Pacary, E. et al. Proneural transcription factors regulate different steps of cortical neuron migration through Rnd-mediated inhibition of RhoA signaling. Neuron 69, 1069–1084 (2011).

    CAS  Article  Google Scholar 

  15. 15.

    Camp, J. G. et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl Acad. Sci. USA 112, 15672–15677 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Topol, A., Tran, N. N. & Brennand, K. J. A guide to generating and using hiPSC derived NPCs for the study of neurological diseases. J. Vis. Exp. https://doi.org/10.3791/52495 (2015).

  17. 17.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell developmental trajectories.. Nat. Methods 14, 979–982 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Wang, J. et al. Epilepsy-associated genes. Seizure 44, 11–20 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Riesenberg, S. & Maricic, T. Targeting repair pathways with small molecules increases precise genome editing in pluripotent stem cells. Nat. Commun. 9, 2164 (2018).

    Article  Google Scholar 

  20. 20.

    Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).

    Article  Google Scholar 

  21. 21.

    Renaud, G., Stenzel, U. & Kelso, J. leeHom: adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res. 42, e141 (2014).

    Article  Google Scholar 

  22. 22.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  23. 23.

    Boyer, L. F., Campbell, B., Larkin, S., Mu, Y. & Gage, F. H. Dopaminergic differentiation of human pluripotent cells. Curr. Protoc. Stem Cell Biol. Chapter 1, Unit1H.6 (2012).

    PubMed  Google Scholar 

  24. 24.

    Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Pilz, G.-A. et al. Amplification of progenitors in the mammalian telencephalon includes a new radial glial cell type. Nat. Commun. 4, 2125 (2013).

    Article  Google Scholar 

  26. 26.

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  Google Scholar 

  27. 27.

    Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  Article  Google Scholar 

  28. 28.

    Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    CAS  Article  Google Scholar 

  29. 29.

    Renaud, G., Kircher, M., Stenzel, U. & Kelso, J. freeIbis: an efficient basecaller with calibrated quality scores for Illumina sequencers. Bioinformatics 29, 1208–1209 (2013).

    CAS  Article  Google Scholar 

  30. 30.

    Renaud, G., Stenzel, U., Maricic, T., Wiebe, V. & Kelso, J. deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31, 770–772 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  32. 32.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2008).

    Article  Google Scholar 

  34. 34.

    Fietz, S. A. et al. Transcriptomes of germinal zones of human and mouse fetal neocortex suggest a role of extracellular matrix in progenitor self-renewal. Proc. Natl Acad. Sci. 109, 11836–11841 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank the families participating in this study for their involvement. We thank Y. Lu for help generating the microRNAs, M. Karow and I. Buchsbaum for helping with experiments and fruitful discussions in the lab, T. Öztürk for excellent technical support, A. Weigert for organoid culture, J. Kageyama for helping with data processing, R. Snabel for helping with Smart-seq2 libraries, the Core Unit Flow Cytometry at the Zentrum für Infektionsmedizin (veterinary faculty of the University of Leipzig) and the Core Unit Qualitätsmanagement/Technologieplattform at the Sächsischer Inkubator für Klinische Translation (SIKT) in Leipzig for karyotyping. This work was supported by funding from the DFG CA1205/2-1 (S.C.), ForIPS (M.G.), by the Max Planck Society (S.C., B.T.), by the Boehringer Ingelheim Fonds (S.K.), by the Health Research Council of NZ and Curekids (S.P.R.) and by an ERC Starting Grant (B.T.).

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Contributions

S.C. conceived and designed the research project, J.K., S.K., C.K., A.C.A.-M., R.D.G., S.R., A.C.O., C.T., M. Santel. and E.R. performed experiments and collected data, J.K., C.K., S.K., J.G.C., M. Schroeder, B.T. and S.C. analyzed data, M.D. reprogrammed patients’ samples, M.G. was involved in the start of the project, contributed to data discussion and supervision of J.K. S.P.R. was involved in patient sample collection and critical discussion, J.K., S.K., C.K., B.T. and S.C. wrote the manuscript. All authors provided ongoing critical review of results and commented on the manuscript.

Corresponding authors

Correspondence to Barbara Treutlein or Silvia Cappello.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Expression of DCHS1 and FAT4 in cerebral organoids and temporal development of DCHS1- and FAT4-mutant organoids.

a, Axial T1 image demonstrating laminar PH lining the occipital horns and peritrigonal region of the ventricles (arrows). b, Axial T2 demonstrating linear lesions iso-intense with cortical gray matter adjacent to the lateral walls of the bodies of the lateral ventricles2. c, Schematic representation of the main experimental approach used. d, Timeline of the organoid generation. EBs, embryoid bodies; hESC, low-bFGF human embryonic stem cell medium; NIM, neural induction medium; Y, Rock inhibitor; NDM, neural differentiation medium; +/– A: B27 supplement with or without vitamin; IHC, immunohistochemistry; RNA-seq, RNA sequencing. eh′, Detection of DCHS1 and FAT4 by in situ hybridization, b = 2, o = 5 per condition. i,j, mRNA expression of DCHS1 and FAT4 in single cells (c = 316) derived from control organoids, showing similar expression patterns between DCHS1 and FAT4 but often in different cells. i, Cells (columns) are ordered based on their PC 2 loading, corresponding to the trajectory from NPCs to neurons. Side bar shows maximal zone correlation for each single cell (ventricular zone, VZ, yellow; inner subventricular zone, iSVZ, orange; outer SVZ, oSVZ, red; cortical plate, CP, purple). j, Biplot showing transcript levels (in log2(FPKM)) of FAT4 (x axis) and DCHS1 (y axis) in 316 single cells of control organoids. k,l, Temporal development of patient-derived cerebral organoids compared with control organoids (o = 14 CTRL, 17 DCHS1, 17 FAT4); the diameters of organoids derived from DCHS1- and FAT4-mutant cells are slightly smaller compared to those of control organoids until day 12 (d12), shown in k. Significance based on two-way ANOVA, P = 0.000, Tukey HSD post hoc for multiple comparisons was performed for defining statistical differences between the three genotypes. Dotted lines highlight ventricles (V). Data in graphs are represented as mean ± s.e.m. Scale bars, 100 µm in eh,k. Source data

Extended Data Fig. 2 Heterotopically located neurons in DCHS1 and FAT4-mutant and KO organoids.

ac,ej,lo, Micrographs of sections of mutant or KO organoids immunostained as indicated in the panels. Note the mispositioning of neurons marked by arrows in mutant or in electroporated cerebral organoids. ac, b = 2, o = 6 per condition; ej, b = 2, o = 6 per condition; llʹʹʹ, b = 5, o = 15 per condition; mo, b = 5, o = 15 per condition. d, MAP2 fluorescence intensity measured only in the ventricular zones (VZ) (v = 6 CTRL, 18 DCHS1, 11 FAT4; significance based on one-way ANOVA, P = 0.0166, Tukey HSD post hoc for multiple comparisons for defining statistical differences between the three genotypes). k, Quantification measured by qPCR of the knockdown of DCHS1 and FAT4 by microRNAs (miRNA) against DCHS1 or FAT4, respectively (b = 2, independent cultures per time = 3 CTRL, 3 miRNA DCHS1, 3 miRNA FAT4, significance based on one-way ANOVA, P = 0.0377, Tukey HSD post hoc for multiple comparisons for defining statistical differences between the three genotypes) in SH-SY5Y cells 48 h after nucleofection. llʹʹʹ, Nodule of TUBB3+ neurons intermingling with NESTIN+ processes of NPCs in the germinal zone of DCHS1-mutant organoids. mo DCHS1- and FAT4-mutant organoids show changes in the morphology and thickness of their neuritis, as depicted by arrowheads. Dotted lines highlight ventricles (V). Data in graphs are represented as mean ± s.e.m. Scale bars, 100 µm in ac and ej and 30 µm in lo. Source data.

Extended Data Fig. 3 Neural progenitor proliferation and signatures in mutant organoids.

ai,km, Micrographs of sections of mutant cerebral organoids from day 20 immunostained for NESTIN (ac) and DCX (df) and from day 42 immunostained for PAX6 (gi) and PH3 (km). af, b = 1, o = 3 per condition; gi,km, b = 3, o = 9 per condition. j, Quantification of thickness of ventricular zone (VZ) and cortical plate (CP) structures in cerebral organoids (v = 6 CTRL, 11 DCHS1, 12 FAT4). n, Quantification of PH3+ cells per length of apical surface (o = 3, v = 23 CTRL, 41 DCHS1, 17 FAT4, F(2,80) = 2.41, P = 0.097, comparison between CTRL and FAT4 F(1,39) = 5.18, P = 0.029). o, Hierarchical clustering visualizing for all NPCs (338 single cells), expression of genes identified by PCA (top 50 positively and negatively correlating with PC 1) on all NPCs. p, Number of NPCs and neurons for each experiment shown in o. q, FACS plots depicting the definition of the sorting gates (secondary antibodies control) and sorting of KI67+ or DCX+ cells in control, DCHS1- and FAT4-mutant organoids (b = 2, o = 6 CTRL, 6 DCHS1, 3 FAT4). r,s, Z scores of the quantification of KI67+ cells (r) and DCX+ cells (s) from FACs analysis shown in q. Statistical analysis was performed using two-tailed Mann Whitney test. r, CTRL to DCHS1 P = 0.0022, CTRL to FAT4 P = 0.0238. s, CTRL to DCHS1 P = 0.0317, CTRL to FAT4 P = 0.0357. Results are mean ± s.e.m. (j,n) or z scores as mean ± s.e.m. (r,s). Dotted lines highlight ventricles (V). Scale bars, 30 µm for af and 50 µm for gm.Source data.

Extended Data Fig. 4 Morphological changes in the NPCs upon DCHS1 and FAT4 deletion.

ac, Micrographs of sections of KO cerebral organoids day 40 immunostained for NESTIN. Arrows indicate the disrupted morphology of NPCs (b,c). df, Micrographs of sections of control organoids electroporated with miRNA against DCHS1 or FAT4 at day 42 and analyzed at day 49. Arrows indicate the disrupted morphology upon downregulation of DCHS1 and FAT4 (e,f). ac, b = 2, o = 6 per condition; df, b = 2, o = 6 per condition. Dotted lines highlight ventricles (V). Scale bars, 30 µm.

Extended Data Fig. 5 Apicobasal polarity in cerebral organoids.

ac, Micrographs of sections of organoids (day 42) immunostained for ACETYLATED TUBULIN, b = 2, o = 6 per condition. d,e, Western blot and quantification of ACETYLATED TUBULIN (d) (independent cultures = 5 CTRL, 4 DCHS1, 4 FAT4, significance based on one sample two-tailed t test, P = 0.562 CTRL vs DCHS1, P = 0.013 CTRL vs FAT4) and TYROSINATED TUBULIN (e) (independent cultures = 3 CTRL, 3 DCHS1, 3 FAT4) levels in NPCs, significance based on one sample two-tailed t test, P = 0.967 CTRL vs DCHS1, P = 0.728 CTRL vs FAT4. fn,pr, Micrographs of sections of cerebral organoids (day 42) immunostained as indicated in the panels. fn,pr, b = 3, o = 9 per condition. o, Quantification of the distance from the apical surface (positive for β-CATENIN and PALS1) and DAPI+ nuclei of NPCs (v = 27 CTRL, 26 DCHS1, 26 FAT4; 5 different positions were measured and averaged for each ventricle; significance based on one-sample two-tailed t test, P = 0.0025 CTRL vs DCHS1, P = 0.001 CTRL vs FAT4). s, Ratio of ARL13B fluorescence intensity measured at the apical surface (cilia facing the ventricular lumen) and at basal position (all the rest of the cilia in the germinal and cortical zones) (b = 5, o = 15, v = 9 CTRL, 4 DCHS1, 14 FAT4, significance based on one-sample t test). Results are mean ± s.e.m. Dotted lines highlight ventricles (V). Scale bar, 50 µm (ac) and 20 µm (fr). Source data Source data

Extended Data Fig. 6 2D time-lapse imaging experimental design and morphological changes of mutant neurons.

a, Experimental design for 2D live imaging of migrating neurons. Neural progenitors were differentiated for 7 d and imaged for 3 d every 5 min. b, Quantification of the percentage of cells expressing DCX, MAP2 or both markers in neuronal cultures after 10 d in culture. ccʹʹ, Immunostaining for DoubleCortin (DCX), MAP2 (mature neurons) and VGLUT (glutamatergic neurons) in 2D neurons derived from control cells in monolayer culture, b = 3, independent cultures = 9 per condition. df, Immunostaining for TUBB3 in 2D neurons derived from control and mutant cells in monolayer culture, b = 3, independent cultures = 9 per condition. Results are mean ± s.e.m. Scale bar, 20 µm. Source data

Extended Data Fig. 7 Characterization of mutant and KO organoid regions.

al, Micrographs of sections of mutant and KO organoids immunostained as depicted in the panels. ac,gi, b = 3, o = 9 per condition; df,jl, b = 2, o = 6 per condition. m, Heat map showing expression of genes marking neurons in the cortex/forebrain (columns) for all neuronal cells from control and mutant organoids (rows, 467 single cells). Scale bar, 30 µm.

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Klaus, J., Kanton, S., Kyrousi, C. et al. Altered neuronal migratory trajectories in human cerebral organoids derived from individuals with neuronal heterotopia. Nat Med 25, 561–568 (2019). https://doi.org/10.1038/s41591-019-0371-0

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