ArticlePLoS computational biology2021
Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Signaling and actin waves at a glance.Journal of cell science · 2025Review
- Machine learning-guided reconstruction of cytoskeleton network from live-cell AFM images.iScience · 2024Article
- Article
- A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.NPJ systems biology and applications · 2023Article
- Interpreting T-cell search "strategies" in the light of evolution under constraints.PLoS computational biology · 2023Article
- Random walk and cell morphology dynamics inFrontiers in cell and developmental biology · 2023Article
- Spontaneous transitions between amoeboid and keratocyte-like modes of migration.Frontiers in cell and developmental biology · 2022Article
- Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration.Biophysical journal · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making frequent turns. Model simulations are essential for quantitative understanding of these features and their origins, however systematic comparisons with real data are underdeveloped. Here, by employing deep-learning-based feature extraction combined with phase-field modeling framework, we show that a low dimensional feature space for 2D migrating cell morphologies obtained from the shape stereotype of keratocytes, Dictyostelium and neutrophils can be fully mapped by an interlinked signaling network of cell-polarization and protrusion dynamics. Our analysis links the data-driven shape analysis to the underlying causalities by identifying key parameters critical for migratory morphologies both normal and aberrant under genetic and pharmacological perturbations. The results underscore the importance of deciphering self-organizing states and their interplay when characterizing morphological phenotypes.
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Registered trials
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