Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Primate cell fusion disentangles gene regulatory divergence in neurodevelopment

Abstract

Among primates, humans display a unique trajectory of development that is responsible for the many traits specific to our species. However, the inaccessibility of primary human and chimpanzee tissues has limited our ability to study human evolution. Comparative in vitro approaches using primate-derived induced pluripotent stem cells have begun to reveal species differences on the cellular and molecular levels1,2. In particular, brain organoids have emerged as a promising platform to study primate neural development in vitro3,4,5, although cross-species comparisons of organoids are complicated by differences in developmental timing and variability of differentiation6,7. Here we develop a new platform to address these limitations by fusing human and chimpanzee induced pluripotent stem cells to generate a panel of tetraploid hybrid stem cells. We applied this approach to study species divergence in cerebral cortical development by differentiating these cells into neural organoids. We found that hybrid organoids provide a controlled system for disentangling cis- and trans-acting gene-expression divergence across cell types and developmental stages, revealing a signature of selection on astrocyte-related genes. In addition, we identified an upregulation of the human somatostatin receptor 2 gene (SSTR2), which regulates neuronal calcium signalling and is associated with neuropsychiatric disorders8,9. We reveal a human-specific response to modulation of SSTR2 function in cortical neurons, underscoring the potential of this platform for elucidating the molecular basis of human evolution.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Generation of human–chimpanzee hybrid iPS cells.
Fig. 2: Cortical differentiation of hybrid iPS cells.
Fig. 3: Disentangling cis-regulatory effects on gene expression in cortical spheroids.
Fig. 4: Functional validation of allele-specific gene-expression changes.

Data availability

Raw and processed data are publicly available through the Gene Expression Omnibus under accession GSE144825. The alignment of human and chimpanzee genomes from Ensembl is available at ftp://ftp.ensembl.org/pub/release-84/maf/ensembl-compara/pairwise_alignments/homo_sapiens.GRCh38.vs.pan_troglodytes.CHIMP2.1.4.tar. The SFARI database is available at https://www.sfari.org/resource/sfari-gene/.

Code availability

All code for the described analyses of RNA-seq data and for making figures is publicly available at https://github.com/TheFraserLab/Agoglia_HumanChimpanzee2020.

References

  1. 1.

    Gallego Romero, I. et al. A panel of induced pluripotent stem cells from chimpanzees: a resource for comparative functional genomics. eLife 4, e07103 (2015).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–83 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

    Pașca, S. P. The rise of three-dimensional human brain cultures. Nature 553, 437–445 (2018).

    PubMed  ADS  Google Scholar 

  4. 4.

    Muchnik, S. K., Lorente-Galdos, B., Santpere, G. & Sestan, N. Modeling the evolution of human brain development using organoids. Cell 179, 1250–1253 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  5. 5.

    Qian, X., Song, H. & Ming, G. L. Brain organoids: advances, applications and challenges. Development 146, dev166074 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Pollen, A. A. et al. Establishing cerebral organoids as models of human-specific brain evolution. Cell 176, 743–756 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Kanton, S. et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature 574, 418–422 (2019).

    PubMed  ADS  CAS  Google Scholar 

  8. 8.

    Beneyto, M., Morris, H. M., Rovensky, K. C. & Lewis, D. A. Lamina- and cell-specific alterations in cortical somatostatin receptor 2 mRNA expression in schizophrenia. Neuropharmacology 62, 1598–1605 (2012).

    PubMed  CAS  Google Scholar 

  9. 9.

    Ádori, C. et al. Critical role of somatostatin receptor 2 in the vulnerability of the central noradrenergic system: new aspects on Alzheimer’s disease. Acta Neuropathol. 129, 541–563 (2015).

    PubMed  Google Scholar 

  10. 10.

    Ward, M. C. et al. Silencing of transposable elements may not be a major driver of regulatory evolution in primate iPSCs. eLife 7, e33084 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Prud’homme, B., Gompel, N. & Carroll, S. B. Emerging principles of regulatory evolution. Proc. Natl Acad. Sci. USA 104 (Suppl 1), 8605–8612 (2007).

    PubMed  ADS  Google Scholar 

  12. 12.

    Paşca, A. M. et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat. Methods 12, 671–678 (2015).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

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

    PubMed  ADS  CAS  Google Scholar 

  14. 14.

    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).

    PubMed  ADS  CAS  Google Scholar 

  15. 15.

    Sloan, S. A. et al. Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron 95, 779–790 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

    Mora-Bermúdez, F. et al. Differences and similarities between human and chimpanzee neural progenitors during cerebral cortex development. eLife 5, e18683 (2016).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Otani, T., Marchetto, M. C., Gage, F. H., Simons, B. D. & Livesey, F. J. 2D and 3D stem cell models of primate cortical development identify species-specific differences in progenitor behavior contributing to brain size. Cell Stem Cell 18, 467–480 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Amps, K. et al. Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage. Nat. Biotechnol. 29, 1132–1144 (2011).

    PubMed  CAS  Google Scholar 

  19. 19.

    Taapken, S. M. et al. Karotypic abnormalities in human induced pluripotent stem cells and embryonic stem cells. Nat. Biotechnol. 29, 313–314 (2011).

    PubMed  CAS  Google Scholar 

  20. 20.

    Müller, F.-J. et al. A bioinformatic assay for pluripotency in human cells. Nat. Methods 8, 315–317 (2011).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yin, X. et al. Engineering stem cell organoids. Cell Stem Cell 18, 25–38 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  23. 23.

    Stingl, J., Eaves, C. J., Zandieh, I. & Emerman, J. T. Characterization of bipotent mammary epithelial progenitor cells in normal adult human breast tissue. Breast Cancer Res. Treat. 67, 93–109 (2001).

    PubMed  CAS  Google Scholar 

  24. 24.

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

    PubMed  PubMed Central  CAS  Google Scholar 

  25. 25.

    Greig, L. C., Woodworth, M. B., Galazo, M. J., Padmanabhan, H. & Macklis, J. D. Molecular logic of neocortical projection neuron specification, development and diversity. Nat. Rev. Neurosci. 14, 755–769 (2013).

    PubMed  CAS  Google Scholar 

  26. 26.

    Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259 (2018).

    PubMed  PubMed Central  CAS  Google Scholar 

  27. 27.

    Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl Acad. Sci. USA 106, 5743–5748 (2009).

    PubMed  ADS  CAS  Google Scholar 

  28. 28.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Fraser, H. B. Genome-wide approaches to the study of adaptive gene expression evolution: systematic studies of evolutionary adaptations involving gene expression will allow many fundamental questions in evolutionary biology to be addressed. BioEssays 33, 469–477 (2011).

    PubMed  CAS  Google Scholar 

  30. 30.

    Oberheim, N. A., Wang, X., Goldman, S. & Nedergaard, M. Astrocytic complexity distinguishes the human brain. Trends Neurosci. 29, 547–553 (2006).

    PubMed  CAS  Google Scholar 

  31. 31.

    Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010).

    PubMed  ADS  CAS  Google Scholar 

  32. 32.

    Bozek, K. et al. Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness. PLoS Biol. 12, e1001871 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V. & Oldham, M. C. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat. Neurosci. 21, 1171–1184 (2018).

    PubMed  PubMed Central  CAS  Google Scholar 

  34. 34.

    Basu, S. N., Kollu, R. & Banerjee-Basu, S. AutDB: a gene reference resource for autism research. Nucleic Acids Res. 37, D832–D836 (2009).

    PubMed  CAS  Google Scholar 

  35. 35.

    Sousa, A. M. M., Meyer, K. A., Santpere, G., Gulden, F. O. & Sestan, N. Evolution of the human nervous system function, structure, and development. Cell 170, 226–247 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

    Fujii, Y. et al. Somatostatin receptor subtype SSTR2 mediates the inhibition of high-voltage-activated calcium channels by somatostatin and its analogue SMS 201-995. FEBS Lett. 355, 117–120 (1994).

    PubMed  CAS  Google Scholar 

  37. 37.

    Liguz-Lecznar, M., Urban-Ciecko, J. & Kossut, M. Somatostatin and somatostatin-containing neurons in shaping neuronal activity and plasticity. Front. Neural Circuits 10, 48 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    He, Z. et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat. Neurosci. 20, 886–895 (2017).

    PubMed  CAS  Google Scholar 

  39. 39.

    Gokhman, D. et al. Human-chimpanzee fused cells reveal cis-regulation underlying skeletal evolution. Nat. Genet. https://doi.org/10.1038/s41588-021-00804-3 (2021).

  40. 40.

    Yoon, S. J. et al. Reliability of human cortical organoid generation. Nat. Methods 16, 75–78 (2019).

    PubMed  CAS  Google Scholar 

  41. 41.

    van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Tehranchi, A. et al. Fine-mapping cis-regulatory variants in diverse human populations. eLife 8, e39595 (2019).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Combs, P. A. & Fraser, H. B. Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic. PLoS Genet. 14, e1007631 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  45. 45.

    Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    CAS  Google Scholar 

  46. 46.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    PubMed  CAS  Google Scholar 

  47. 47.

    Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  49. 49.

    Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48–502 (2009).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  51. 51.

    Paşca, S. P. et al. Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat. Med. 17, 1657–1662 (2011).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H. Blau and G. Markov for advice on the hybridization experiments; D. Bangs and J. Erdmann for assistance with iPS cell karyotyping; R. Jones, S. D. Conley and R. Sinha for assistance in constructing the single-cell RNA-seq libraries; and members of the Pașca and Fraser laboratories for advice and feedback on the manuscript. This work was supported by a Stanford Bio-X Interdisciplinary Initiatives Seed Grant (to S.P.P. and H.B.F.), an NIH grant T32 GM007790 (supporting R.M.A.), the Department of Defense National Defense Science and Engineering Graduate Fellowship (to R.M.A.), the Stanford Center for Computational, Evolutionary and Human Genomics (to R.M.A.), NIH grant 2R01GM097171-05A1 (supporting H.B.F.), the Stanford Medicine’s Dean’s Fellowship (to Y.M. and F.B.), the Stanford Medicine Maternal & Child Health Research Institute Postdoctoral Support Program (to Y.M. and F.B.), the American Epilepsy Society Postdoctoral Research Fellowship (to F.B.), the Stanford Wu Tsai Neurosciences Institute’s Big Idea Grants on Brain Rejuvenation and Human Brain Organogenesis (supporting S.P.P.), the Kwan Research Fund (supporting S.P.P.), the New York Stem Cell Foundation–Robertson Investigator Award (supporting S.P.P.) and the Chan Zuckerberg Ben Barres Investigator Award (to S.P.P.). This study used cell lines derived from the Yerkes National Primate Research Center, which is supported by the National Institutes of Health, Office of Research Infrastructure Programs/OD (P51OD011132).

Author information

Affiliations

Authors

Contributions

D.S. generated the hybrid iPS cells. R.M.A. and D.S. characterized the iPS cells. R.M.A., D.S., S.-J.Y. and K.S. cultured the iPS cells. R.M.A. performed the neural differentiation of hCS, cCS and hyCS. S.-J.Y. contributed to the neural differentiation of hCS, cCS and hyCS. R.M.A. performed the RNA-seq and analysis of RNA-seq data. R.M.A. and F.B. performed the calcium-imaging experiments. F.B. analysed the calcium-imaging data. Y.M. performed the immunocytochemistry in intact and dissociated cortical spheroids. R.M.A., S.P.P. and H.B.F. conceived the project, designed experiments and wrote the paper with input from all authors. S.P.P. and H.B.F. supervised all aspects of the work.

Corresponding authors

Correspondence to Sergiu P. Pașca or Hunter B. Fraser.

Ethics declarations

Competing interests

Stanford University holds a patent covering the generation of brain region-specific organoids (US patent serial no. 62/163,870;8) (S.P.P.).

Additional information

Peer review information Nature thanks Megan Munsie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 Isolation and characterization of hyiPS cells.

a, FACS of fused hybrid cells (representative plots for fusion of H20961 and C3649). Top, co-cultured cells with no PEG; bottom, co-cultured cells with PEG; from left, initial size selection, gating out doublets, gating out dead cells, sorting for red (human) and green (chimpanzee) double-positive population; FSC, forward scatter; SSC, side scatter; A, area; H, height. Pacific Blue measures DAPI, FITC measures green CMFDA (chimpanzee) and APC measures deep red (human). b, Representative karyotype for female (XX/XX) hybrid iPS cell lines. ce, Immunostaining for the pluripotency markers NANOG and TRA-1-81 (c), OCT4 and SSEA4 (d), and SOX2 and TRA-1-60 (e). f, Results from PluriTest analysis of RNA-seq data from this study and from Ward et al.10 (see Methods); benchmarked thresholds are 20 or higher for pluripotency, 1.6 or lower for novelty (dotted lines). Scale bars, 200 μm (c–e).

Extended Data Fig. 2 Chromosomal instability and X-chromosome inactivation.

a, b, Plots showing aneuploidies on chromosome 20 indicating a gain of a chimpanzee chromosome (a) or a combined loss of the human short arm and gain of the human long arm (b). Top, scatter plot of ASE (log2(human/chimpanzee)) versus genomic location; middle, median ASE in a sliding window of 20 genes; bottom, P-values from a two-sided Wilcoxon rank-sum test comparing a sliding window of 20 genes to the background of the entire genome. c, Total (top) and allelic (bottom; human allele pink, chimpanzee allele blue) expression of XIST in RNA-seq samples; symbols indicate the sex of each iPS cell line; n = 2 technical replicates per cell line. d, Plots of ASE across the X chromosome (as in a, b). e, Total and allelic expression of RNR1 (chrMT), as in c; n = 2 technical replicates per cell line.

Extended Data Fig. 3 RNA-seq of hyiPS cells.

a, Heat map of correlations (Pearson’s) between RNA-seq samples from human (H1, H2 and H3), chimpanzee (C1, C2 and C3) and hybrid (Hy1-25, Hy1-29, Hy1-30, Hy2-9 and Hy2-16) iPS cells. b, Top, pipeline for analysis of RNA-seq data and separation of species-specific sequencing reads; bottom, pile-up of phased allelic reads from human, chimpanzee and hybrid RNA-seq samples for a representative gene. c, Representative scatter plot (from line Hy1-30) showing total gene expression when samples are mapped to the human genome (GRCh38, x-axis) versus the chimpanzee genome (PanTro5, y-axis); n = 1, out of 10 total hyiPS samples sequenced with similar results. d, Scatter plot of ASE in all hybrid samples when mapped to the human versus the chimpanzee genome; genes represented by the points in red are considered to have mapping bias and are eliminated from subsequent analyses; data merged from n = 10 samples from 5 hyiPS cell lines (2 replicates each).

Extended Data Fig. 4 Generation and characterization of hyCS.

a, b, Principal components plots for iPS and cortical spheroid (pilot study) RNA-seq samples based on total (a) or allelic (b) gene expression. c, Rates of success of three protocols used to derive hyCS (success is defined as at least one cortical spheroid from a given cell line surviving to 100 days of differentiation). n refers to the number of independent attempts to differentiate any of 3 hyiPS cell lines. d, Bright-field imaging of hCS and hyCS at day 7 or 8 of differentiation. The experiment was repeated across 3 independent differentiation experiments with 3 hyiPS and 1 hiPS cell lines with similar results. e, Bright-field images of Matrigel-embedded hCS and hyCS, as well as non-embedded hCS, at days 16 and 35 of differentiation. f, Heat map of correlations (Pearson’s) between bulk RNA-seq samples for hyCS. g, Principal components plot for iPS and hyCS (full dataset) RNA-seq samples based on allelic gene expression h, Heat map coloured by the percentage of human reads in each single cell, stratified by chromosome; rows are ordered by hybrid cell line; top bar shows read depth of each chromosome across all cells; bottom left, colour key and histogram for heat map values; bottom middle, scatter plot of total read depth versus variance per chromosome, wherein fewer reads results in higher variance; bottom right, histogram showing the percentage of human reads in each cell, genome-wide. i, j, Histogram of the percentage of human reads in each cell for aneuploid chromosomes 18 (i) and 20 (j), stratified by cell line. Scale bars, 1 mm (d, e).

Extended Data Fig. 5 Single-cell gene profiling of hyCS.

a, UMAP clustering of all cells (n = 706); clusters are identified by colour and labelled by letter (A, astroglia; P, cycling progenitors; N1, glutamatergic neurons; N2, GABAergic neurons; M1, mesenchyme cluster 1; M2, mesenchyme cluster 2; E, epithelial cells). b, Proportion of cells from each hybrid cell line in each single cell cluster (from a). c, Dot plot for expression of marker genes for each cluster in a; size corresponds to the percentage of cells in each cluster that express each gene. d, UMAP coloured by expression of mesenchymal and epithelial marker genes. e, Scatter plot of normalized gene expression between embedded (y-axis) and non-embedded (x-axis) hybrid (line Hy1-29) cortical spheroids at day 50 of differentiation; points in red and green indicate genes whose expression is induced by the addition of Matrigel (see Methods). f, t-SNE of all single cells from this study aggregated with cells from non-embedded spheroids in Sloan et al.15, coloured by study. g, t-SNE from f, coloured by expression of cell-type marker genes. h, UMAP from a, coloured according to which cells were defined as neural and used for further analysis in Fig. 2. i, Histograms of per-gene ASE, where ASE is defined as the ratio of all human reads across cells of a given cell type to all chimpanzee reads in those cells.

Extended Data Fig. 6 Generation of hCS and cCS and RNA-seq.

a, b, Representative bright-field images of three cortical spheroids per line for three human (a) and three chimpanzee (b) cell lines at day 166. c, Immunostaining of hCS and cCS for SOX9, PAX6 and CTIP2. At each time point, a maximum of 2 spheroids were fixed for immunostaining across 3 hiPS and 3 ciPS cell lines with 4 independent differentiation experiments per cell line. d, e, Heat map of Pearson’s correlations between bulk RNA-seq samples for hCS (d) and cCS (e). Scale bars, 1 mm (a, b) and 50 μm (c).

Extended Data Fig. 7 RNA-seq and cell-type deconvolution in cortical spheroids.

a, b, Principal components plots for RNA-seq samples based on total gene expression of parent and hybrid samples. c, e, g, Per-sample estimated cell-type proportions in hyCS (c), hCS (e) and cCS (g) (see Methods). d, f, h, Normalized expression across time of cell-type-specific marker genes in hyCS (d), hCS (f) and cCS (h).

Extended Data Fig. 8 Weighted gene co-expression network analysis.

a, Dendrogram of all genes used in WGCNA; genes in the same colour block belong to the same co-expressed module. b, Eigengene values for genes in the blue, brown and red modules over time in hCS and cCS; chimpanzee blue, human red; in order of time points, n = 6, 6, 6, 6, 6, 6 and 5 hCS and n = 6, 6, 6, 6, 5, 5 and 5 cCS samples from 3 human and 3 chimpanzee iPS cell lines (1–2 replicates per cell line). c, Expression of module genes (eigengene, see Methods) in single-cell data; cell clusters are defined in Extended Data Fig. 5a. d, Allelic eigengene values for genes in these modules over time in hyCS (see Methods); chimpanzee blue, human red; in order of time points, n = 7, 9, 7 and 2 hyCS from 3 hyiPS cell lines (2–3 replicates per cell line). e, Single-cell gene expression of PMP2. f, Expression of PMP2 in parental bulk time course; chimpanzee blue, human red; n as in b. g, Allelic expression of PMP2 in hybrid bulk time course; chimpanzee allele blue, human allele red; n as in d. Box plots in b, d, f, g: the centre line shows median, box limits represent upper and lower quartiles and whiskers extend to 1.5× the interquartile range.

Extended Data Fig. 9 Summary of ASE genes.

ad, Overlap in genes with significant ASE between hyCS at days 50 versus 100 (a), hyCS at days 100 versus 150 (b), hyiPS cells versus hyCS at day 150 (c), and differential expression between hCS and cCS at day 150 versus ASE in hyCS at day 150 (d). e, Scatter plot showing differences in gene expression between parental lines (y-axis) versus between alleles in the hybrid (x-axis) at day 150; data are from bulk RNA-seq of 6 human, 5 chimpanzee and 7 hybrid cortical spheroid samples, collected across 3 human, 3 chimpanzee and 3 hybrid iPS cell lines. f, Overlap between ASE genes and SFARI genes. g, ASE in SFARI genes from the overlapping genes in f. h, i, Allelic expression over time in GRIN2A (h) and SCN1A (i); human allele pink, chimpanzee allele blue; in order of time points n = 7, 9, 7 and 2 hyCS samples (1–2 spheroids per sample) from 3 independent differentiations of 3 hyiPS cell lines. j, Filtering pipeline for prioritizing candidate genes. k, Scatter plot of hybrid ASE (x-axis) and parental differential expression (y-axis) for top candidate genes at day 150; n = 7 hyCS, 6 hCS and 5 cCS samples (1–3 spheroids per sample) derived from 3 iPS cell lines per species and 2 independent differentiations per hiPS and ciPS cell line, n = 3 independent differentiations per hyiPS cell line. Box plots in h, i: the centre line shows median, box limits represent upper and lower quartiles and whiskers extend to 1.5× the interquartile range.

Extended Data Fig. 10 Validation of SSTR2.

a, Expression of SSTR2 in parental bulk time course; chimpanzee blue, human red; in order of time points, n = 6, 6, 6, 6, 6, 6 and 5 hCS and n = 6, 6, 6, 6, 5, 5 and 5 cCS samples from 3 human and 3 chimpanzee iPS cell lines (1–2 replicates per cell line). b, Expression of SSTR2 across cortical sections in adult primate brain tissue (data from He et al.38); dotted lines indicate approximate boundaries of cortical layers; WM, white matter. c, Immunostaining for MAP2 (neuronal) and SSTR2 protein in dissociated hCS (H20682) and cCS (C3649) at day 225–250; right panels show SSTR2 only; 10 images were taken per sample and quantified. d, Quantification of fluorescence intensity (arbitrary units) of MAP2 for the images in c. n = 13 cells for hCS, 14 cells for cCS; ****P < 0.0001, two-tailed Mann–Whitney test. e, Quantification of fluorescence intensity (arbitrary units) of SSTR2 relative to MAP2 for the images in c; n = 13 cells for hCS, 14 cells for cCS; ****P < 0.0001, two-tailed Mann–Whitney test. f, Additional immunostaining for TUBB3 (neuronal) and SSTR2 in dissociated hCS (H20682) and cCS (C3649) at day 225–250; 10 images were taken per sample and quantified. g, Immunostaining for MAP2 and SSTR2 in whole hCS (H20961) and cCS (C3651) at day 160; imaging was reproduced across 3 human and 2 chimpanzee cell lines from 1 differentiation experiment with n = 3, 2, 3, 2 and 3 images for lines H21792, H20682, H20961, C3649 and C3651, respectively. h, Representative still frame images of hCS (H20682)- and cCS (C3649)-derived neurons infected with AAV-DJ-hSyn1-eYFP; images are taken from one of the samples in Fig. 4g–i; the experiment was reproduced across 2 human and 1 chimpanzee cell lines. i, Representative still frame images of hCS (H20682) infected with the viral vector co-encoding stable red fluorophore mRuby2 and genetically encoded calcium indicator GCaMP6s; images are taken from one of the samples in Fig. 4j–l; the experiment was reproduced across 3 human and 3 chimpanzee cell lines. Box plots in a, d, e: the centre line shows median, box limits represent upper and lower quartiles and whiskers extend to 1.5× the interquartile range.; dotted lines connect average values (a). Scale bars, 50 μm (f), 10 μm (c, g), 60 μm (h), 30 μm (i).

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1-14.

Reporting Summary

Peer Review File

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Agoglia, R.M., Sun, D., Birey, F. et al. Primate cell fusion disentangles gene regulatory divergence in neurodevelopment. Nature 592, 421–427 (2021). https://doi.org/10.1038/s41586-021-03343-3

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links