Nature Cell BiologyDevelopmental BiologyPeer-reviewed journal article
Single-cell atlas of human liver development reveals pathways directing hepatic cell fates
Brandon Tyler WesleyiD, Alexander RossiD, Daniele MuraroiD, Zhichao MiaoiD, Sarah H. SaxtoniD, Rute A. TomaziD, Carola Maria MorelliD, Katherine RidleyiD, Ekaterini D. ZacharisiD, Sandra Petrus-ReureriD, Judith KraiczyiD, Krishnaa T. A. MahbubaniiD, Stephanie Emma BrowniD, José Garcia‐BernardoiD, Clara AlsinetiD, Daniel J. GaffneyiD, Dave HorsfalliD, Olivia TysoeiD, Rachel Anne BottingiD, Emily StephensoniD, Dorin-Mirel Popescu, Sonya A. MacParlandiD, Gary D. BaderiD, Ian D. McGilvrayiD, Daniel Ortmann, , Ludovic VallieriD
Abstract
The liver has been studied extensively due to the broad number of diseases affecting its vital functions. However, therapeutic advances, especially in regenerative medicine, are currently hampered by the lack of knowledge concerning human hepatic cell development. Here, we addressed this limitation by describing the developmental trajectories of different cell types comprising the human fetal liver at single-cell resolution. These transcriptomic analyses revealed that sequential cell-to-cell interactions direct functional maturation of hepatocytes, with non-parenchymal cells playing critical, supportive roles during organogenesis. We utilised this information to derive bipotential hepatoblast organoids and then exploited this novel model system to validate the importance of key signalling pathways and developmental cues. Furthermore, these insights into hepatic maturation enabled the identification of stage-specific transcription factors to improve the functionality of hepatocyte-like cells generated from human pluripotent stem cells. Thus, our study establishes a new platform to investigate the basic mechanisms of human liver development and to produce cell types for clinical applications.
- organism: human
- assay: scRNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: experimental assay
- Citations
- 145
- FWCI
- 8.97
- Field percentile
- top 1%
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