Determination of Green’s function for three-dimensional traction force reconstruction based on geometry and boundary conditions of cell culture matrices

Du, Yuanmin; Herath, Sahan C. B.; Wang, Q.G.; Asada, H. Harry; Chen, P.C.Y.

doi:10.1016/j.actbio.2017.12.002

Cited by 14 publications

(17 citation statements)

References 63 publications

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“…The recovered tractions present maxima at the tips of the cell's protrusion of around 150Pa (see Fig 3c). While this displacement field pattern around angiogenic sprouts has been reported in previous studies [30], [42]- [44], TFMLAB additionally incorporates quantification of traction magnitude and direction. This can be a crucial tool for the quantification of cell mechanical behavior in 3D, ECM-mimicking hydrogels, both for single cells as well as multicellular structures and for applications such as disease modeling, regenerative medicine and tissue engineering as well as developmental biology.…”

Section: Illustrative Examplesmentioning

confidence: 87%

TFMLAB: a MATLAB toolbox for 4D traction force microscopy

Barrasa‐Fano

Shapeti

Jorge-Peñas

et al. 2020

Preprint

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We present TFMLAB, a MATLAB software package for 4D (x;y;z;t) Traction Force Microscopy (TFM). While various TFM computational workflows are available in the literature, open-source programs that are easy to use by researchers with limited technical experience and that can analyze 4D in vitro systems do not exist. TFMLAB integrates all the computational steps to compute active cellular forces from confocal microscopy images, including image processing, cell segmentation, image alignment, matrix displacement measurement and force recovery. Moreover, TFMLAB eases usability by means of interactive graphical user interfaces. This work describes the package's functionalities and analyses its performance on a real TFM case.

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Section: Illustrative Examplesmentioning

confidence: 87%

TFMLAB: a MATLAB toolbox for 4D traction force microscopy

Barrasa‐Fano

Shapeti

Jorge-Peñas

et al. 2020

Preprint

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“…Nodal forces in the direction of the sprout branches were then prescribed on FA nodes to mimic typical pulling patterns shown in literature [48,56] (see Fig.2e). The sum of all the nodal forces was constant for all the cases and fixed at 70nN (see details in Appendix C).…”

Section: Ground Truth Simulationsmentioning

confidence: 99%

“…This approach minimizes the difference between the measured displacements (obtained analogously to the forward method) and a mathematically consistent (regularized) solution. While the most common way of solving this problem is by minimizing a least square estimate with Tikhonov regularization ( [35]), alternative inverse formulations have recently been developed ( [47,48,49,50]). We recently proposed an inverse method that fulfills the equilibrium of internal forces with real acting forces, which is not always warranted in other inverse methods ( [51]).…”

Section: Introductionmentioning

confidence: 99%

“…However, they often simplify or bypass critical aspects, i.e., cellular geometries, FAs and displacement sampling noise. Authors have used simplified geometries (instead of real cell geome-tries) that ease calculations such as cylindrical ( [48]), spherical ( [52]) or ellipsoidal inclusions ( [53]). Recently, real cell geometries obtained from 3D optical microscopy imaging (such as confocal imaging) have been used ( [54,49,50]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced in silico validation framework for three-dimensional Traction Force Microscopy and application to an in vitro model of sprouting angiogenesis

Barrasa‐Fano

Shapeti

Jong

et al. 2020

Preprint

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In the last decade, cellular forces in three-dimensional hydrogels that mimic the extracellular matrix have been calculated by means of Traction Force Microscopy (TFM). However, characterizing the accuracy limits of a traction recovery method is critical to avoid obscuring physiological information due to traction recovery errors. So far, 3D TFM algorithms have only been validated using simplified cell geometries, bypassing image processing steps or arbitrarily simulating focal adhesions. Moreover, it is still uncertain which of the two common traction recovery methods, i.e., forward and inverse, is more robust against the inherent challenges of 3D TFM. In this work, we established an advanced in silico validation framework that is applicable to any 3D TFM experimental setup and that can be used to correctly couple the experimental and computational aspects of 3D TFM. Advancements relate to the simultaneous incorporation of complex cell geometries, simulation of microscopy images of varying bead densities and different focal adhesion sizes and distributions. By measuring the traction recovery error with respect to ground truth solutions, we found that while highest traction recovery errors occur for cases with sparse and small focal adhesions, our implementation of the inverse method improves two-fold the accuracy with respect to the forward method (average error of 23% vs. 50%). This advantage was further supported by recovering cellular tractions around angiogenic sprouts in an in vitro model of angiogenesis. The inverse method recovered more realistic traction patterns than the forward method, showing higher traction peaks and a clearer pulling pattern at the sprout protrusion tips.

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“…Acto-myosin contractility has been shown indispensable for sprout initiation (Kniazeva & Putnam, 2009), branching and orientation of invading endothelial cells (Elliott et al, 2015), capillary-like tube formation (Mabeta & Pepper, 2009), and sprout maintenance (Kniazeva & Putnam, 2009). However, limited data is available on quantifying tractions exerted by endothelial sprouts (Du et al, 2016;Du, Herath, Wang, Asada, & Chen, 2018;Kniazeva et al, 2012;Vaeyens et al, 2020;Yoon et al, 2019) and discrepancies exist between studies about the impact of cellular tractions on invasiveness (Indra et al, 2011;Koch, Münster, Bonakdar, Butler, & Fabry, 2012;Kraning-Rush, Califano, & Reinhart-King, 2012;Munevar, Wang, & Dembo, 2001;Peschetola et al, 2013). While tip cell pulling on stalk cells, stalk cell pushing on tip cells, or both, have previously been postulated as mechanical forces that underlie sprout elongation (Betz et al, 2016;De Smet et al, 2009;Gerhardt, 2008;Geudens & Gerhardt, 2011;Santos-Oliveira et al, 2015;Sauteur et al, 2014;Schmidt et al, 2007;Travasso, 2011), collagen deformations indicative of tip cell pulling were recently reported (Vaeyens et al, 2020;Yoon et al, 2019).…”

Section: Introductionmentioning

confidence: 99%