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Lilliana Adames

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Aug 3, 2024, 5:02:03 PM8/3/24

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The human brain has undergone substantial change since humans diverged from chimpanzees and the other great apes1,2. However, the genetic and developmental programs that underlie this divergence are not fully understood. Here we have analysed stem cell-derived cerebral organoids using single-cell transcriptomics and accessible chromatin profiling to investigate gene-regulatory changes that are specific to humans. We first analysed cell composition and reconstructed differentiation trajectories over the entire course of human cerebral organoid development from pluripotency, through neuroectoderm and neuroepithelial stages, followed by divergence into neuronal fates within the dorsal and ventral forebrain, midbrain and hindbrain regions. Brain-region composition varied in organoids from different iPSC lines, but regional gene-expression patterns remained largely reproducible across individuals. We analysed chimpanzee and macaque cerebral organoids and found that human neuronal development occurs at a slower pace relative to the other two primates. Using pseudotemporal alignment of differentiation paths, we found that human-specific gene expression resolved to distinct cell states along progenitor-to-neuron lineages in the cortex. Chromatin accessibility was dynamic during cortex development, and we identified divergence in accessibility between human and chimpanzee that correlated with human-specific gene expression and genetic change. Finally, we mapped human-specific expression in adult prefrontal cortex using single-nucleus RNA sequencing analysis and identified developmental differences that persist into adulthood, as well as cell-state-specific changes that occur exclusively in the adult brain. Our data provide a temporal cell atlas of great ape forebrain development, and illuminate dynamic gene-regulatory features that are unique to humans.

S.K. and M.J.B. grew organoids with assistance from A.W., L.S. and M.H. S.K. performed scRNA-seq and snRNA-seq with assistance from M.S. M.J.B. performed scATAC-seq. Z.H., M.J.B. and S.K. analysed the data. F.S.C. and M.H. performed immunohistochemical stainings. J.S.F. compared organoid scRNA-seq data to mouse voxel maps. P.G. dissected and sliced tissue for snRNA-seq. D.H. and Z.Q. performed bulk RNA-seq of adult tissue. S.K., M.J.B., Z.H., B.T. and J.G.C. designed the study and wrote the manuscript with support from P.K., W.B.H. and S.P.

a, scRNA-seq was performed on two-month-old cerebral organoids from one human ESC and six iPSC lines. b, All data (49,153 cells) were combined and cell heterogeneity was assessed using t-SNE with the top 20 PCs as the input. Cells are also coloured by marker gene expression and RSS. c, t-SNE plot with RSS against Brainspan fetal reference data as the input (RSS-t-SNE), coloured by cell lines. Cells from different lines are well integrated. d, SPRING plot of two-month-old human organoid pseudocells (9,650), coloured by neuronal trajectory branches and pseudotimes. e, SPRING plots of two-month-old human organoid cells, coloured by marker gene expression. f, SPRING plots coloured by cell line show contributions of each line to different branches of the trajectory. g, Correlations of expression trajectories of genes with pseudotime-dependent expression patterns between cortical cells from each line to the others (pink), ventral cells from each line to others (blue), and cortical and ventral cells from the same lines after or before aligning the cortical and ventral pseudotimes (purple). h, Spatial location inference of neuron subtypes in human cerebral organoids. Left, bar plots show proportion of cells of each cell type that show highest gene-expression-pattern similarity to the average expression patterns in different structures, on the basis of the processed in situ hybridization image data (E13.5) provided in the Developing Mouse Brain database of Allen Brain Atlas (available from -map.org/). Expression similarity was calculated based on highly variable genes of the scRNA-seq data (top) or regional markers defined with the in situ hybridization data (bottom). Right, correlation patterns of average regional marker gene expression of each neuron subtype to voxels in five example sections (E13.5), as well as the structural annotation of the sections. i, Expression of two marker genes of diencephalon inhibitory neurons (PCP4 and RSPO3) in the SPRING embeddings, and their spatial expression patterns in E13.5 mouse brain (data from Allen Brain Atlas, available from -map.org/).

a, Overview of the Fluidigm C1 scRNA-seq data. Each dot represents a cerebral organoid or fetal brain sample from one cell line or species at a certain age, with its size showing the number of cells measured. The left panel shows organoid sample information as published in Pollen et al. (2019)16 (excluding redundant cells from Camp et al. (2015)11 and Mora-Bermudez et al. (2016)15), including the data initially published in Sloan et al. (2017)70. The middle panel shows organoid sample information generated in Camp et al. (2015)11, Mora-Bermudez et al. (2016)15 and in this study. The right panel shows fetal prefrontal cortex sample information reported in Nowakowski et al. (2017)19. b, All cerebral organoid data (5,838 cells) were combined and cell heterogeneity was assessed using t-SNE with the RSS profiles to the fetal Brainspan data as the input. Cells are coloured by cell type or cluster, species, institutions generating the data, dorsal trajectory pseudotimes and marker gene expression. c, t-SNE plots for all fetal brain data (5,080 cells) to assess cell heterogeneity, with the RSS profiles to the fetal Brainspan references as the input. Cells are coloured by cell type or cluster, species, dorsal excitatory neuron trajectory pseudotimes and marker gene expression. d, Heat map showing marker gene-expression patterns across different cell types in the droplet-based organoid scRNA-seq data generated in this manuscript and the above described C1-based scRNA-seq data.