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A primer to traction force microscopy
A. Zancla, P. Mozetic, M. Orsini, G. Forte, A. Rainer
Language English Country United States
Document type Journal Article, Review, Research Support, Non-U.S. Gov't
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- MeSH
- Biophysics MeSH
- Cell Adhesion MeSH
- Microscopy, Atomic Force methods MeSH
- Traction * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
Traction force microscopy (TFM) has emerged as a versatile technique for the measurement of single-cell-generated forces. TFM has gained wide use among mechanobiology laboratories, and several variants of the original methodology have been proposed. However, issues related to the experimental setup and, most importantly, data analysis of cell traction datasets may restrain the adoption of TFM by a wider community. In this review, we summarize the state of the art in TFM-related research, with a focus on the analytical methods underlying data analysis. We aim to provide the reader with a friendly compendium underlying the potential of TFM and emphasizing the methodological framework required for a thorough understanding of experimental data. We also compile a list of data analytics tools freely available to the scientific community for the furtherance of knowledge on this powerful technique.
Center for Translational Medicine St Anne's University Hospital Brno Czechia
Department of Engineering Università Campus Bio Medico di Roma Rome Italy
Department of Engineering Università degli Studi Roma Tre Rome Italy
Institute of Nanotechnology National Research Council Lecce Italy
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- $a Traction force microscopy (TFM) has emerged as a versatile technique for the measurement of single-cell-generated forces. TFM has gained wide use among mechanobiology laboratories, and several variants of the original methodology have been proposed. However, issues related to the experimental setup and, most importantly, data analysis of cell traction datasets may restrain the adoption of TFM by a wider community. In this review, we summarize the state of the art in TFM-related research, with a focus on the analytical methods underlying data analysis. We aim to provide the reader with a friendly compendium underlying the potential of TFM and emphasizing the methodological framework required for a thorough understanding of experimental data. We also compile a list of data analytics tools freely available to the scientific community for the furtherance of knowledge on this powerful technique.
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- $a Orsini, Monica $u Department of Engineering, Università degli Studi Roma Tre, Rome, Italy
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