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Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy
Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large dis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216574/ https://www.ncbi.nlm.nih.gov/pubmed/35731725 http://dx.doi.org/10.1371/journal.pone.0268614 |
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author | Haarman, Samuel E. Kim, Sue Y. Isogai, Tadamoto Dean, Kevin M. Han, Sangyoon J. |
author_facet | Haarman, Samuel E. Kim, Sue Y. Isogai, Tadamoto Dean, Kevin M. Han, Sangyoon J. |
author_sort | Haarman, Samuel E. |
collection | PubMed |
description | Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods, and newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43–77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software. |
format | Online Article Text |
id | pubmed-9216574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92165742022-06-23 Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy Haarman, Samuel E. Kim, Sue Y. Isogai, Tadamoto Dean, Kevin M. Han, Sangyoon J. PLoS One Research Article Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods, and newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43–77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software. Public Library of Science 2022-06-22 /pmc/articles/PMC9216574/ /pubmed/35731725 http://dx.doi.org/10.1371/journal.pone.0268614 Text en © 2022 Haarman et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Haarman, Samuel E. Kim, Sue Y. Isogai, Tadamoto Dean, Kevin M. Han, Sangyoon J. Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title | Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title_full | Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title_fullStr | Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title_full_unstemmed | Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title_short | Particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
title_sort | particle retracking algorithm capable of quantifying large, local matrix deformation for traction force microscopy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216574/ https://www.ncbi.nlm.nih.gov/pubmed/35731725 http://dx.doi.org/10.1371/journal.pone.0268614 |
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