Cargando…
Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells
Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the measurement of traction forces on an elastic substrate...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345967/ https://www.ncbi.nlm.nih.gov/pubmed/30679578 http://dx.doi.org/10.1038/s41598-018-36896-x |
_version_ | 1783389668729946112 |
---|---|
author | Huang, Yunfei Schell, Christoph Huber, Tobias B. Şimşek, Ahmet Nihat Hersch, Nils Merkel, Rudolf Gompper, Gerhard Sabass, Benedikt |
author_facet | Huang, Yunfei Schell, Christoph Huber, Tobias B. Şimşek, Ahmet Nihat Hersch, Nils Merkel, Rudolf Gompper, Gerhard Sabass, Benedikt |
author_sort | Huang, Yunfei |
collection | PubMed |
description | Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the measurement of traction forces on an elastic substrate and thereby provides quantitative information on cellular mechanics in a perturbation-free fashion. In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular tractions in different conditions. |
format | Online Article Text |
id | pubmed-6345967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63459672019-01-29 Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells Huang, Yunfei Schell, Christoph Huber, Tobias B. Şimşek, Ahmet Nihat Hersch, Nils Merkel, Rudolf Gompper, Gerhard Sabass, Benedikt Sci Rep Article Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the measurement of traction forces on an elastic substrate and thereby provides quantitative information on cellular mechanics in a perturbation-free fashion. In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular tractions in different conditions. Nature Publishing Group UK 2019-01-24 /pmc/articles/PMC6345967/ /pubmed/30679578 http://dx.doi.org/10.1038/s41598-018-36896-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Huang, Yunfei Schell, Christoph Huber, Tobias B. Şimşek, Ahmet Nihat Hersch, Nils Merkel, Rudolf Gompper, Gerhard Sabass, Benedikt Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title | Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title_full | Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title_fullStr | Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title_full_unstemmed | Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title_short | Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells |
title_sort | traction force microscopy with optimized regularization and automated bayesian parameter selection for comparing cells |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345967/ https://www.ncbi.nlm.nih.gov/pubmed/30679578 http://dx.doi.org/10.1038/s41598-018-36896-x |
work_keys_str_mv | AT huangyunfei tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT schellchristoph tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT hubertobiasb tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT simsekahmetnihat tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT herschnils tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT merkelrudolf tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT gomppergerhard tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells AT sabassbenedikt tractionforcemicroscopywithoptimizedregularizationandautomatedbayesianparameterselectionforcomparingcells |