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Full L(1)-regularized Traction Force Microscopy over whole cells
BACKGROUND: Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs regularization to make the solution stable. The typical r...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550960/ https://www.ncbi.nlm.nih.gov/pubmed/28797233 http://dx.doi.org/10.1186/s12859-017-1771-0 |
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author | Suñé-Auñón, Alejandro Jorge-Peñas, Alvaro Aguilar-Cuenca, Rocío Vicente-Manzanares, Miguel Van Oosterwyck, Hans Muñoz-Barrutia, Arrate |
author_facet | Suñé-Auñón, Alejandro Jorge-Peñas, Alvaro Aguilar-Cuenca, Rocío Vicente-Manzanares, Miguel Van Oosterwyck, Hans Muñoz-Barrutia, Arrate |
author_sort | Suñé-Auñón, Alejandro |
collection | PubMed |
description | BACKGROUND: Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs regularization to make the solution stable. The typical regularization scheme is given by the minimization of a cost functional, which is divided in two terms: the error present in the data or data fidelity term; and the regularization or penalty term. The classical approach is to use zero-order Tikhonov or L(2)-regularization, which uses the L(2)-norm for both terms in the cost function. Recently, some studies have demonstrated an improved performance using L(1)-regularization (L(1)-norm in the penalty term) related to an increase in the spatial resolution and sensitivity of the recovered traction field. In this manuscript, we present a comparison between the previous two regularization schemes (relying in the L(2)-norm for the data fidelity term) and the full L(1)-regularization (using the L(1)-norm for both terms in the cost function) for synthetic and real data. RESULTS: Our results reveal that L(1)-regularizations give an improved spatial resolution (more important for full L(1)-regularization) and a reduction in the background noise with respect to the classical zero-order Tikhonov regularization. In addition, we present an approximation, which makes feasible the recovery of cellular tractions over whole cells on typical full-size microscope images when working in the spatial domain. CONCLUSIONS: The proposed full L(1)-regularization improves the sensitivity to recover small stress footprints. Moreover, the proposed method has been validated to work on full-field microscopy images of real cells, what certainly demonstrates it is a promising tool for biological applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1771-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5550960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55509602017-08-15 Full L(1)-regularized Traction Force Microscopy over whole cells Suñé-Auñón, Alejandro Jorge-Peñas, Alvaro Aguilar-Cuenca, Rocío Vicente-Manzanares, Miguel Van Oosterwyck, Hans Muñoz-Barrutia, Arrate BMC Bioinformatics Research Article BACKGROUND: Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs regularization to make the solution stable. The typical regularization scheme is given by the minimization of a cost functional, which is divided in two terms: the error present in the data or data fidelity term; and the regularization or penalty term. The classical approach is to use zero-order Tikhonov or L(2)-regularization, which uses the L(2)-norm for both terms in the cost function. Recently, some studies have demonstrated an improved performance using L(1)-regularization (L(1)-norm in the penalty term) related to an increase in the spatial resolution and sensitivity of the recovered traction field. In this manuscript, we present a comparison between the previous two regularization schemes (relying in the L(2)-norm for the data fidelity term) and the full L(1)-regularization (using the L(1)-norm for both terms in the cost function) for synthetic and real data. RESULTS: Our results reveal that L(1)-regularizations give an improved spatial resolution (more important for full L(1)-regularization) and a reduction in the background noise with respect to the classical zero-order Tikhonov regularization. In addition, we present an approximation, which makes feasible the recovery of cellular tractions over whole cells on typical full-size microscope images when working in the spatial domain. CONCLUSIONS: The proposed full L(1)-regularization improves the sensitivity to recover small stress footprints. Moreover, the proposed method has been validated to work on full-field microscopy images of real cells, what certainly demonstrates it is a promising tool for biological applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1771-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-10 /pmc/articles/PMC5550960/ /pubmed/28797233 http://dx.doi.org/10.1186/s12859-017-1771-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Suñé-Auñón, Alejandro Jorge-Peñas, Alvaro Aguilar-Cuenca, Rocío Vicente-Manzanares, Miguel Van Oosterwyck, Hans Muñoz-Barrutia, Arrate Full L(1)-regularized Traction Force Microscopy over whole cells |
title | Full L(1)-regularized Traction Force Microscopy over whole cells |
title_full | Full L(1)-regularized Traction Force Microscopy over whole cells |
title_fullStr | Full L(1)-regularized Traction Force Microscopy over whole cells |
title_full_unstemmed | Full L(1)-regularized Traction Force Microscopy over whole cells |
title_short | Full L(1)-regularized Traction Force Microscopy over whole cells |
title_sort | full l(1)-regularized traction force microscopy over whole cells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550960/ https://www.ncbi.nlm.nih.gov/pubmed/28797233 http://dx.doi.org/10.1186/s12859-017-1771-0 |
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