bioRxivBioengineering
Single cell transcriptomics reveals chondrocyte differentiation dynamics in vivo and in vitro
J. E. G. Lawrence, S. Woods, K. Roberts, D. N. Sumanaweera, P. Balogh, A. V. Predeus, P. He, T. Li, K. Polanski, E. Prigmore, E. Tuck, L. Mamanova, D. Zhou, S. Webb, L. Jardine, X. He, R. A. Barker, M. Haniffa, A. M. Flanagan, M. D. Young, S. Behjati, O. Bayraktar, S. J. Kimber, S. Teichmann
Abstract
The consistent production of in vitro chondrocytes that faithfully recapitulate in vivo development would be of great benefit for musculoskeletal disease modelling and regenerative medicine. Current efforts are often limited by off-target differentiation, resulting in a heterogeneous product. Furthermore, the lack of comparison to human embryonic tissue, precludes detailed evaluation of in vitro cells. Here, we perform single-cell RNA sequencing of embryonic long bones dissected from first trimester hind limbs from a range of gestational ages. We combine this with publicly available data to form a detailed atlas of endochondral ossification, which we then use to evaluate a series of published in vitro chondrogenesis protocols, finding substantial variability in cell states produced by each. We apply single-nuclear RNA sequencing to one protocol to enable direct comparison between in vitro and in vivo, and perform trajectory alignment between the two to reveal differentiation dynamics at the single-cell level, shedding new light on off-target differentiation in vitro. Using this information, we inhibit the activity of FOXO1, a transcription factor predicted to be active in embryonic bone development and in chondrogenic cells in vitro, and increase chondrocyte transcripts in vitro. This study therefore presents a new framework for evaluating tissue engineering protocols, using single-cell data from human development to drive improvement and bring the prospect of true engineered cartilage closer to reality.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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