Alvaro Jorge-Peñas

Free Form Deformation-Based Image Registration Improves Accuracy of Traction Force Microscopy.

Traction Force Microscopy (TFM) is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV) is commonly used to quantify the substrate’s deformations, due to its simplicity and efficiency. However, PIV relies on a block-matching scheme that easily underestimates the deformations. This is especially relevant in the case of large, locally non-uniform deformations as those usually found in the vicinity of a cell’s adhesions to the substrate. To overcome these limitations, we formulate the calculation of the deformation of the substrate in TFM as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one. In particular, we propose to use a B-spline -based Free Form Deformation (FFD) algorithm that uses a connected deformable mesh to model a wide range of flexible deformations caused by cellular tractions. Our FFD approach is validated in 3D fields using synthetic (simulated) data as well as with experimental data obtained using isolated endothelial cells lying on a deformable, polyacrylamide substrate. Our results show that FFD outperforms PIV providing a deformation field that allows a better recovery of the magnitude and orientation of tractions. Together, these results demonstrate the added value of the FFD algorithm for improving the accuracy of traction recovery.

Numerical estimation of 3D mechanical forces exerted by cells on non-linear materials

The exchange of physical forces in both cell-cell and cell-matrix interactions play a significant role in a variety of physiological and pathological processes, such as cell migration, cancer metastasis, inflammation and wound healing. Therefore, great interest exists in accurately quantifying the forces that cells exert on their substrate during migration. Traction Force Microscopy (TFM) is the most widely used method for measuring cell traction forces. Several mathematical techniques have been developed to estimate forces from TFM experiments. However, certain simplifications are commonly assumed, such as linear elasticity of the materials and/or free geometries, which in some cases may lead to inaccurate results. Here, cellular forces are numerically estimated by solving a minimization problem that combines multiple non-linear FEM solutions. Our simulations, free from constraints on the geometrical and the mechanical conditions, show that forces are predicted with higher accuracy than when using the standard approaches.

Biomechanical Characterization of Ascending Aortic Aneurysms

Ascending thoracic aortic aneurysms (ATAAs) are a silent disease, ultimately leading to dissection or rupture of the arterial wall. There is a growing consensus that diameter information is insufficient to assess rupture risk, whereas wall stress and strength provide a more reliable estimate. The latter parameters cannot be measured directly and must be inferred through biomechanical assessment, requiring a thorough knowledge of the mechanical behaviour of the tissue. However, for healthy and aneurysmal ascending aortic tissues, this knowledge remains scarce. This study provides the geometrical and mechanical properties of the ATAA of six patients with unprecedented detail. Prior to their ATAA repair, pressure and diameter were acquired non-invasively, from which the distensibility coefficient, pressure–strain modulus and wall stress were calculated. Uniaxial tensile tests on the resected tissue yielded ultimate stress and stretch values. Parameters for the Holzapfel–Gasser–Ogden material model were estimated based on the pre-operative pressure–diameter data and the post-operative stress–stretch curves from planar biaxial tensile tests. Our results confirmed that mechanical or geometrical information alone cannot provide sufficient rupture risk estimation. The ratio of physiological to ultimate wall stress seems a more promising parameter. However, wall stress estimation suffers from uncertainties in wall thickness measurement, for which our results show large variability, between patients but also between measurement methods. Our results also show a large strength variability, a value which cannot be measured non-invasively. Future work should therefore be directed towards improved accuracy of wall thickness estimation, but also towards the large-scale collection of ATAA wall strength data.

L1-regularized reconstruction for traction force microscopy

Traction Force Microscopy (TFM) is a technique widely used to recover cellular tractions from the deformation they cause in their surrounding substrate. Traction recovery is an ill-posed inverse problem that benefits of a regularization scheme constraining the solution. Typically, Tikhonov regularization is used but it is well known that L1-regularization is a superior alternative to solve this type of problems. For that, recent approaches have started to explore what could be their contribution to increase the sensitivity and resolution in the estimation of the exerted tractions. In this manuscript, we adapt the L1-regularization of the curl and divergence to 2D TFM and compare the recovered tractions on simulated and real data with those obtained using Tikhonov and L1-norm regularization.

Full L 1 -regularized Traction Force Microscopy over whole cells

BackgroundTraction 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.ResultsOur 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.ConclusionsThe 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.

Combustion-derived particles inhibit in vitro human lung fibroblast-mediated matrix remodeling

BackgroundThe continuously growing human exposure to combustion-derived particles (CDPs) drives in depth investigation of the involved complex toxicological mechanisms of those particles. The current study evaluated the hypothesis that CDPs could affect cell-induced remodeling of the extracellular matrix due to their underlying toxicological mechanisms. The effects of two ultrafine and one fine form of CDPs on human lung fibroblasts (MRC-5 cell line) were investigated, both in 2D cell culture and in 3D collagen type I hydrogels. A multi-parametric analysis was employed.ResultsIn vitro dynamic 3D analysis of collagen matrices showed that matrix displacement fields induced by human lung fibroblasts are disturbed when exposed to carbonaceous particles, resulting in inhibition of matrix remodeling. In depth analysis using general toxicological assays revealed that a plausible explanation comprises a cascade of numerous detrimental effects evoked by the carbon particles, including oxidative stress, mitochondrial damage and energy storage depletion. Also, ultrafine particles revealed stronger toxicological and inhibitory effects compared to their larger counterparts. The inhibitory effects can be almost fully restored when treating the impaired cells with antioxidants like vitamin C.ConclusionsThe unraveled in vitro pathway, by which ultrafine particles alter the fibroblasts’ vital role of matrix remodeling, extends our knowledge about the contribution of these biologically active particles in impaired lung tissue repair mechanisms, and development and exacerbation of chronic lung diseases. The new insights may even pave the way to precautionary actions. The results provide justification for toxicological assessments to include mechanism-linked assays besides the traditional in vitro toxicological screening assays.

Super-resolved Traction Force Microscopy over whole cells

Traction Force Microscopy (TFM) is a commonly used technique to compute cellular tractions that cells exert to the surrounding substrate. Traction recovery is an ill-posed inverse problem, which needs regularization to stabilize the solution. Due to its simplicity, Tikhonov or L2-regularization is usually used, but recent studies have demonstrated the increase of sensitivity and resolution in the recovered tractions using an L1-regularization scheme. In this manuscript, we present an approximation that makes feasible the traction recovery on full-size microscope images when working in the spatial domain. We perform also a comparison between the two regularization schemes named before (relying in L2-norm for the data fidelity term) and the full L1-regularization (using L1-norm for both the regularization and data fidelity terms). Our proof of concept using real data reveal that L1-regularizations might give an improved resolution (more accused for full L1-regularization) and a reduction in the background noise with respect to the classical zero-order Tikhonov regularization.