Summary of ssbd-repos-000167

Repository URL
DOI
Title
Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space
Description

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 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.

Submited Date
2021-03-09
Release Date
2021-05-12
Updated Date
2021-07-15
License
Data size
4.9 GB
Data formats
.m (MATLAB), .py (python), .cpp, .h, .txt, .jpg, .png, .eps, .fig (MATLAB), .mp4

Organism
Homo sapiens, Unknown (fish keratocyte), Dictyostelium discoideum
Strain
-
Cell Line
HL-60, fish keratocyte, AX-4(Dictyostelium)
Molecular Function (MF)
-
Biological Process (BP)
Spontaneous cell migration
Cellular Component (CC)
-
Study Type
-
Imaging Methods
-

Method Summary

Deep Learning based morphology analysis and phase-field morpho-dynamics simulation for a fast migrating cell

Related paper(s)

Daisuke Imoto, Nen Saito, Akihiko Nakajima, Gen Honda, Motohiko Ishida, Toyoko Sugita, Sayaka Ishihara, Koko Katagiri, Chika Okimura, Yoshiaki Iwadate, Satoshi Sawai (2021) Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space., PLoS computational biology, Volume 17, Number 8, pp. e1009237

Published in 2021 Aug (Electronic publication in Aug. 12, 2021, midnight )

(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.
(MeSH Terms)

Contact(s)
Daisuke Imoto
Organization(s)
The University of Tokyo , Department of Basic Science, Graduate School of Arts and Sciences , Sawai Laboratory
Image Data Contributors
Daisuke Imoto, Akihiko Nakajima, Gen Honda, Makoto Ishida, Toyoko Sugita, Sayaka Ishihara, Koko Katagiri, Chika Okimura, Yoshiaki Iwadate, Satoshi Sawai
Quantitative Data Contributors
Daisuke Imoto, Nen Saito, Akihiko Nakajima, Satoshi Sawai

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