The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.
Investigating cell mechanics with atomic force microscopy
Open accessAbstract
Transmission of mechanical force is crucial for normal cell development and functioning. However, the process of mechanotransduction cannot be studied in isolation from cell mechanics. Thus, in order to understand how cells ‘feel’, we must first understand how they deform and recover from physical perturbations. Owing to its versatility, atomic force microscopy (AFM) has become a popular tool to study intrinsic cellular mechanical properties. Used to directly manipulate and examine whole and subcellular reactions, AFM allows for top-down and reconstitutive approaches to mechanical characterization. These studies show that the responses of cells and their components are complex, and largely depend on the magnitude and time scale of loading. In this review, we generally describe the mechanotransductive process through discussion of well-known mechanosensors. We then focus on discussion of recent examples where AFM is used to specifically probe the elastic and inelastic responses of single cells undergoing deformation. We present a brief overview of classical and current models often used to characterize observed cellular phenomena in response to force. Both simple mechanistic models and complex nonlinear models have been used to describe the observed cellular behaviours, however a unifying description of cell mechanics has not yet been resolved.
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1 retracted
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Metadata
- Authors
- Haase, K., & Pelling, A. E.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2015-03
- Volume
- 12
- Issue
- 104
- Pages
- 20140970
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2125649895
- Cited by
- 396
- References
- 179
- FWCI
- 18.19
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
