Gradeci, D. and Bove, A. and Charras, G. and Lowe, Alan and Banerjee, S. (2020) Single-cell approaches to cell competition: high-throughput imaging, machine learning and simulations. Seminars in Cancer Biology 63 , pp. 60-68. ISSN 1044-579X.
|
Text
review_revision.pdf - Author's Accepted Manuscript Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview |
Abstract
Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Cell competition, Single-cell approach, Machine learning, Imaging, Computational modeling |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Natural Sciences |
| Research Centres and Institutes: | Structural Molecular Biology, Institute of (ISMB) |
| Depositing User: | Alan Lowe |
| Date Deposited: | 25 Jun 2019 12:33 |
| Last Modified: | 02 Aug 2023 17:52 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/27929 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
