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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...

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Detalles Bibliográficos
Autores principales: Suñé-Auñón, Alejandro, Jorge-Peñas, Alvaro, Aguilar-Cuenca, Rocío, Vicente-Manzanares, Miguel, Van Oosterwyck, Hans, Muñoz-Barrutia, Arrate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
Descripción
Sumario: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.