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Peer-reviewedCC BYPLoS Biology

Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data

  • Marine Fontaine1
  • M. Joaquina Delàs2
  • Meritxell Sáez3
  • Rory J. Maizels2
  • Elizabeth Finnie2
  • James Briscoe2
  • David A. J. Rand1,4
4 affiliations
  1. 1Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
  2. 2The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
  3. 3IQS, Universitat Ramon Llull, Via Augusta 390, 08017 Barcelona, Spain
  4. 4Zeeman Institute for Systems Biology and Infectious Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
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Abstract

Building a mechanistic understanding of cell fate decisions remains a fundamental goal of developmental biology, with implications for stem cell therapies, regenerative medicine and understanding disease mechanisms. Single-cell transcriptomics provides a detailed picture of the cellular states observed during these decisions, but building dynamic and predictive models from these data remains a challenge. Here, we present dynamic landscape analysis (DLA), an integrative framework that applies dynamical systems theory to identify stable cell states, map transition pathways, and generate a predictive cell fate decision landscape from single-cell data. Applying this framework to vertebrate neural tube development revealed that progenitor specification by Sonic Hedgehog (Shh) can be captured in a landscape with an unexpected topology in which initially divergent lineages converge to the same fate through multiple distinct routes. The model accurately predicted cellular responses and cell fate allocation for unseen dynamic signalling regimes. Cross-species validation using human embryonic organoid data demonstrated conservation of this decision-making architecture. By modelling the dynamic responses that drive cell fate decisions, the DLA framework provides a quantitative and generative framework for extracting mechanistic insights from high-dimensional single-cell data.

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Cite

BibTeXRIS
BibTeX
@article{fontaine2026dynamic,
  title = {{Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data}},
  author = {Marine Fontaine and M. Joaquina Delàs and Meritxell Sáez and Rory J. Maizels and Elizabeth Finnie and James Briscoe and David A. J. Rand},
  journal = {PLoS Biology},
  year = {2026},
  month = aug,
  doi = {10.1371/journal.pbio.3003953},
  url = {https://doi.org/10.1371/journal.pbio.3003953},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data
AU  - Marine Fontaine
AU  - M. Joaquina Delàs
AU  - Meritxell Sáez
AU  - Rory J. Maizels
AU  - Elizabeth Finnie
AU  - James Briscoe
AU  - David A. J. Rand
PY  - 2026
DA  - 2026/08/26
JO  - PLoS Biology
DO  - 10.1371/journal.pbio.3003953
UR  - https://doi.org/10.1371/journal.pbio.3003953
AB  - Building a mechanistic understanding of cell fate decisions remains a fundamental goal of developmental biology, with implications for stem cell therapies, regenerative medicine and understanding disease mechanisms. Single-cell transcriptomics provides a detailed picture of the cellular states observed during these decisions, but building dynamic and predictive models from these data remains a challenge. Here, we present dynamic landscape analysis (DLA), an integrative framework that applies dynamical systems theory to identify stable cell states, map transition pathways, and generate a predictive cell fate decision landscape from single-cell data. Applying this framework to vertebrate neural tube development revealed that progenitor specification by Sonic Hedgehog (Shh) can be captured in a landscape with an unexpected topology in which initially divergent lineages converge to the same fate through multiple distinct routes. The model accurately predicted cellular responses and cell fate allocation for unseen dynamic signalling regimes. Cross-species validation using human embryonic organoid data demonstrated conservation of this decision-making architecture. By modelling the dynamic responses that drive cell fate decisions, the DLA framework provides a quantitative and generative framework for extracting mechanistic insights from high-dimensional single-cell data.
SN  - 1544-9173
SN  - 1545-7885
ER  -