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  Cells use molecular working memory to navigate inchanging chemoattractant fields

Nandan, A. P., Das, A., Lott, R., & Koseska, A. (2022). Cells use molecular working memory to navigate inchanging chemoattractant fields. eLife, 76825. doi:10.7554/eLife.76825.

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Research article: Physics of Living Systems
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https://elifesciences.org/articles/76825 (Publisher version)
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 Creators:
Nandan, Akhilesh P.1, Author                 
Das, Abhishek1, Author                 
Lott, Robert1, Author                 
Koseska, Aneta1, Author                 
Affiliations:
1Lise Meitner Group Cellular Computations and Learning, Max Planck Institute for Neurobiology of Behavior – caesar, Max Planck Society, ou_3361763              

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Free keywords: real-time navigation, changing environments, molecular working memory, EGF-induced migration, criticality, single cell polarization, Human
 Abstract: In order to migrate over large distances, cells within tissues and organisms rely on sensing local gradient cues which are irregular, conflicting, and changing over time and space. The mechanism how they generate persistent directional migration when signals are disrupted, while still remaining adaptive to signal's localization changes remain unknown. Here we find that single cells utilize a molecular mechanism akin to a working memory to satisfy these two opposing demands. We derive theoretically that this is characteristic for receptor networks maintained away from steady states. Time-resolved live-cell imaging of Epidermal growth factor receptor (EGFR) phosphorylation dynamics shows that cells transiently memorize position of encountered signals via slow-escaping remnant of the polarized signaling state, a dynamical 'ghost', driving memory-guided persistent directional migration. The metastability of this state further enables migrational adaptation when encountering new signals. We thus identify basic mechanism of real-time computations underlying cellular navigation in changing chemoattractant fields.

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Language(s): eng - English
 Dates: 2022-06-06
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.7554/eLife.76825
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Title: eLife
  Abbreviation : eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: - Sequence Number: 76825 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X