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...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Yunfei, Schell, Christoph, Huber, Tobias B., Şimşek, Ahmet Nihat, Hersch, Nils, Merkel, Rudolf, Gompper, Gerhard, Sabass, Benedikt
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