Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency. Our work provides a direct link between geometric properties of single-cell expression profiles and the level of differentiation of a cell population.

The intrinsic dimension of gene expression during cell differentiation

Marta Biondo
First
;
Filippo Valle;Silvia Lazzardi;Michele Caselle;Matteo Osella
Last
2025-01-01

Abstract

Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency. Our work provides a direct link between geometric properties of single-cell expression profiles and the level of differentiation of a cell population.
2025
53
16
1
11
https://academic.oup.com/nar/article/53/16/gkaf805/8245241?login=false
computational biology, data science, statistical physics, cell differentiation
Marta Biondo; Niccolò Cirone; Filippo Valle; Silvia Lazzardi; Michele Caselle; Matteo Osella
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2123247
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