Literature DB >> 31606369

High-Content Imaging of Unbiased Chemical Perturbations Reveals that the Phenotypic Plasticity of the Actin Cytoskeleton Is Constrained.

Nicole S Bryce1, Tim W Failes2, Justine R Stehn1, Karen Baker3, Stefan Zahler4, Yulia Arzhaeva5, Leanne Bischof5, Ciaran Lyons1, Irina Dedova1, Greg M Arndt2, Katharina Gaus6, Benjamin T Goult3, Edna C Hardeman1, Peter W Gunning1, John G Lock7.   

Abstract

Although F-actin has a large number of binding partners and regulators, the number of phenotypic states available to the actin cytoskeleton is unknown. Here, we quantified 74 features defining filamentous actin (F-actin) and cellular morphology in >25 million cells after treatment with a library of 114,400 structurally diverse compounds. After reducing the dimensionality of these data, only ∼25 recurrent F-actin phenotypes emerged, each defined by distinct quantitative features that could be machine learned. We identified 2,003 unknown compounds as inducers of actin-related phenotypes, including two that directly bind the focal adhesion protein, talin. Moreover, we observed that compounds with distinct molecular mechanisms could induce equivalent phenotypes and that initially divergent cellular responses could converge over time. These findings suggest a conceptual parallel between the actin cytoskeleton and gene regulatory networks, where the theoretical plasticity of interactions is nearly infinite, yet phenotypes in vivo are constrained into a limited subset of practicable configurations.
Copyright © 2019 Elsevier Inc. All rights reserved.

Keywords:  F-actin organization; actin cytoskeleton; attractor state; high content analysis; high content screening; high throughput screening; phenotypic analysis; plasticity; talin; talin inhibitor

Year:  2019        PMID: 31606369     DOI: 10.1016/j.cels.2019.09.002

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  4 in total

1.  Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning.

Authors:  John G Lock; Smita Krishnaswamy; Christine L Chaffer; Daniel B Burkhardt; Beatriz P San Juan
Journal:  Cancer Discov       Date:  2022-08-05       Impact factor: 38.272

2.  The Mechanical Basis of Memory - the MeshCODE Theory.

Authors:  Benjamin T Goult
Journal:  Front Mol Neurosci       Date:  2021-02-25       Impact factor: 5.639

3.  BioProfiling.jl: Profiling biological perturbations with high-content imaging in single cells and heterogeneous populations.

Authors:  Loan Vulliard; Joel Hancock; Anton Kamnev; Christopher W Fell; Joana Ferreira da Silva; Joanna I Loizou; Vanja Nagy; Loïc Dupré; Jörg Menche
Journal:  Bioinformatics       Date:  2021-12-22       Impact factor: 6.937

4.  Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

Authors:  Vlada S Rozova; Ayad G Anwer; Anna E Guller; Hamidreza Aboulkheyr Es; Zahra Khabir; Anastasiya I Sokolova; Maxim U Gavrilov; Ewa M Goldys; Majid Ebrahimi Warkiani; Jean Paul Thiery; Andrei V Zvyagin
Journal:  PLoS Comput Biol       Date:  2021-07-23       Impact factor: 4.475

  4 in total

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