bioRxivDevelopmental BiologyPreprint
Induction and in silico staging of human gastruloids with neural tube, segmented somites & advanced cell types
Nobuhiko HamazakiiD, Wei YangiD, Connor A. KuboiD, Chengxiang QiuiD, Beth MartiniD, Riddhiman K. GargeiD, Samuel G. RegaladoiD, Eva K. NicholsiD, Choli LeeiD, Riza M. DazaiD, Sanjay R SrivatsaniD, Jay ShendureiD
Abstract
Embryonic organoids are emerging as powerful models for studying early mammalian development. For example, stem cell-derived gastruloids form elongating structures containing all three germ layers1-4. However, although elongated, human gastruloids do not morphologically resemble post-implantation embryos. Here we show that a specific, discontinuous regimen of retinoic acid (RA) robustly induces human gastruloids with embryo-like morphological structures, including a neural tube and segmented somites. Single cell RNA-seq (sc-RNA-seq) further reveals that these human RA-gastruloids contain more advanced cell types than conventional gastruloids, including neural crest cells, renal progenitor cells, skeletal muscle cells, and, rarely, neural progenitor cells. We apply a new approach to computationally stage human RA-gastruloids relative to somite-resolved mouse embryos, early human embryos and other gastruloid models, and find that the developmental stage of human RA-gastruloids is comparable to that of E9.5 mouse embryos, although some cell types show greater or lesser progression. We chemically perturb WNT and BMP signaling in human RA-gastruloids and find that these signaling pathways regulate somite patterning and neural tube length, respectively, while genetic perturbation of the transcription factors PAX3 and TBX6 markedly compromises the formation of neural crest and somites/renal cells, respectively. Human RA-gastruloids complement other embryonic organoids in serving as a simple, robust and screenable model for decoding early human embryogenesis.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- approach: experimental assay
- Citations
- 3
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