bioRxivNeurosciencePreprint
Single-cell multiomics atlas of organoid development uncovers longitudinal molecular programs of cellular diversification of the human cerebral cortex
Ana UzquianoiD, Amanda J. KedaigleiD, Martina PigoniiD, Bruna da Silveira PaulseniD, Xian AdiconisiD, Kwanho KimiD, Tyler FaitsiD, Surya NagarajaiD, Noelia Antón-BolañosiD, Chiara GerhardingeriD, Ashley TucewicziD, Evan M. MurrayiD, Xin JiniD, Jason Daniel BuenrostroiD, Fei CheniD, Silvia VelascoiD, Aviv RegeviD, Joshua Z. LeviniD, Paola ArlottaiD
Abstract
Realizing the full utility of brain organoids as experimental systems to study human cortical development requires understanding whether organoids replicate the cellular and molecular events of this complex process precisely, reproducibly, and with fidelity to the embryo. Here we present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, spanning initial generation of neural progenitors through production of differentiated neuronal and glial subtypes. We define the lineage relationships and longitudinal molecular trajectories of cortical cell types during development in organoids, and show that developmental processes of cellular diversification in organoids correlate closely to endogenous ones, irrespective of metabolic state. Using this data, we identify genes with predicted human-specific roles in lineage establishment, and discover a developmental origin for the transcriptional diversity of human callosal projection neurons, a population that has undergone dramatic expansion and diversification during human evolution. Our work provides a comprehensive, single-cell molecular map of human corticogenesis in vitro, identifying developmental trajectories and molecular mechanisms associated with human cellular diversification.
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: statistical model
- Citations
- 21
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