Full L-1-regularized Traction Force Microscopy over whole cells
| Autor: | Alejandro Sune-Aunon, Miguel Vicente-Manzanares, Hans Van Oosterwyck, Alvaro Jorge-Peñas, Rocío Aguilar-Cuenca, Arrate Muñoz-Barrutia |
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| Přispěvatelé: | Ministerio de Economía y Competitividad (España) |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Traction Force Microscopy Mathematical optimization Computer science medicine.medical_treatment CHO Cells lcsh:Computer applications to medicine. Medical informatics Spatial domain 01 natural sciences Biochemistry Traction force microscopy Regularization (mathematics) Tikhonov regularization Background noise 03 medical and health sciences Cricetulus Structural Biology Cricetinae 0103 physical sciences Regularization medicine Animals 010306 general physics Molecular Biology Image resolution lcsh:QH301-705.5 Biología y Biomedicina Spatial resolution Applied Mathematics Hydrogels Inverse problem Traction (orthopedics) Computer Science Applications Biomechanical Phenomena 030104 developmental biology Microscopy Fluorescence lcsh:Biology (General) lcsh:R858-859.7 Algorithm Algorithms Research Article |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname BMC Bioinformatics, Vol 18, Iss 1, Pp 1-14 (2017) BMC Bioinformatics Repositorio Institucional de la Consejería de Sanidad de la Comunidad de Madrid Consejería de Sanidad de la Comunidad de Madrid |
| Popis: | 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 L2-regularization, which uses the L2-norm for both terms in the cost function. Recently, some studies have demonstrated an improved performance using L1-regularization (L1-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 L2-norm for the data fidelity term) and the full L1-regularization (using the L1-norm for both terms in the cost function) for synthetic and real data. Results Our results reveal that L1-regularizations give an improved spatial resolution (more important for full L1-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 L1-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. This work was partially supported by the Spanish Ministry of Economy and Competitiveness (TEC2013-48552-C2-1-R, TEC2015-73064-EXP and TEC2016-78052-R) (AMB, ASA) and (SAF2014-54705-R) (MVM, RAC), the European Research Council (ERC) under the EU-FP7/2007-2013 through ERC Grant Agreement n° 308,223 (HVO, AJP). ASA is supported by an FPI grant of the Spanish Ministry of Economy and Competitiveness. MVM is supported by a Marie Curie Grant (CIG293719) and a Ramon y Cajal fellowship (RYC2010-06094) from the Spanish Ministry of Economy and Competitiveness. |
| Databáze: | OpenAIRE |
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