Biology subjects

Sanz-Herrera, J. A.

Publications and source records attributed to Sanz-Herrera, J. A..

7 recordsLinked to original sources

bioRxiv2025

A Natural Programmable Metamaterial Controls 3D Curvature of Compound Eyes

The panoramic vision of the convex compound eyes, common to insects and crustaceans, relies on micrometer-scale curvature variations1. These variations create specialized visual zones adapted to specific tasks, including detecting prey, mates, or predators2,3. However, the mechanisms by which such fine-scale curvature is encoded during development remain unknown. Here we show that the developing eye of Drosophila melanogaster functions as a natural metamaterial that programs the organs precise 3D curvature. We discover a supracellular triangular mesh in the basal retina with a specific pattern of triangles sizes. Computational simulations demonstrate its role directing the small scale curvature variations of the eye. Genetic disruption of this micropattern prevents local curvature establishment. Furthermore, the presence of a homologous mesh-curvature relationship in Drosophila mauritiana indicates evolutionary conservation of this mechanism. These results reveal a novel mechanism of morphogenesis control in which the supracellular 2D patterning give rise to a biological programmable metamaterial that encodes 3D curvature with great precision4. Our in vivo finding offers a novel framework for the design of shape-programmable 3D biological surfaces with broad implications from synthetic morphogenesis to clinical applications.

developmental biology↗

bioRxiv2025

Inverse modeling of heterogeneous ECM mechanical properties in nonlinear 3DTFM

Accurate characterization of cellular tractions is crucial for understanding cell-extracellular matrix (ECM) mechanical interactions and their implications in pathology-related situations, yet their direct measurement in experimental setups remains challenging. Traction Force Microscopy (TFM) has emerged as a key methodology to reconstruct traction fields from displacement data obtained via microscopic imaging techniques. While traditional TFM methods assume homogeneous and static ECM properties, the dynamic nature of the ECM through processes such as enzyme-induced collagen degradation or cellmediated collagen deposition i.e. ECM remodeling, requires approaches that account for spatio-temporal evolution of ECM stiffness heterogeneity and other mechanical properties. In this context, we present a novel inverse methodology for 3DTFM, capable of reconstructing spatially heterogeneous distributions of the ECMs stiffness. Our approach formulates the problem as a PDE-constrained inverse method which searches for both displacement and the stiffness map featured in the selected constitutive law. The elaborated numerical algorithm is integrated then into an iterative Newton-Raphson/Finite Element Method (NR/FEM) framework, bypassing the need for external iterative solvers. We validate our methodology using in silico 3DTFM cases based on real cell geometries, modeled within a nonlinear hyperelastic framework suitable for collagen hydrogels. The performance of our approach is evaluated across different noise levels, and compared versus the commonly used iterative L-BFGS algorithm. Besides the novelty of our formulation, we demonstrate the efficacy of our approach both in terms of accuracy and CPU time efficiency.

biophysics↗

bioRxiv2024

Multiscale characterization of the mechanics of curved fibered structures with application to biological materials

Curved fibered structures are ubiquitous in nature and this organization is found in the majority of biological tissues. Indeed, the mechanical behavior of these materials is of pivotal importance in biomechanics and mechanobiology fields. In this paper, we develop a multiscale formulation to characterize the macroscopic mechanical nonlinear behavior from the microstructure of fibered matrices. From the analysis of the mechanics of a randomly curved single fiber, a fibered matrix model is built to determine the macroscopic behavior following a homogenization approach. The model is tested for tensile, compression and shear loads in a number of applications reminiscent to collagen extracellular matrices. However, any other fibered microstructures can be studied following the proposed formulation. The presented approach naturally recovers instabilities at compression as well as the strain stiffening regime, which are observed experimentally in the mechanical behavior of collagen matrices. Indeed, it was found that the bending energy associated to fiber unrolling, is the most important source of energy developed by fibers for the analyzed cases in tensile and shear in all deformation regions (except the strain stiffening region), whereas bending energy dominates at compression too during buckling. The proposed computational framework can also be used to perform multiscale simulations in the referred applications. As a result, the developed methodology may be an interesting and complementary tool to characterize the nonlinear behavior and evolution of curved fibered structures present in biology and engineered materials.

bioengineering↗

bioRxiv2023

Force-mediated recruitment and reprogramming of healthy endothelial cells drive vascular lesion growth

Force-driven cellular interactions are known to play a critical role in cancer cell invasion, but have remained largely unexplored in the context of vascular abnormalities, partly due to a lack of suitable genetic and cellular models. One such vascular abnormality, cerebral cavernous malformation (CCM) is characterized by leaky, tumor-like vessels in the brain, where CCM mutant cells recruit wild-type cells from the surrounding endothelium to form mosaic lesions and promote lesion growth; however the mechanisms underlying this recruitment remain poorly understood. Here, we use 3D traction force microscopy in a in-vitro model of early angiogenic invasion to reveal that hyper-angiogenic CCM2-silenced endothelial cells enhance angiogenic invasion of neighboring wild-type cells through force and extracellular matrix-guided mechanisms. We show that mechanically hyperactive CCM2-silenced tips guide wild-type cells by exerting and transmitting pulling forces and by leaving degraded paths in the matrix as cues promoting invasion in a ROCKs-dependent manner. This transmission of forces is associated with a reinforcement of {beta}1 integrin-dependent adhesive sites and actin cytoskeleton in the wild-type followers. We also show that during this process wild-type cells are reprogrammed into stalk cells through activation of matrisome and DNA replication programs, eventually leading to cell proliferation. These observations unveil a novel vascular lesion growth mechanism where CCM2 mutants hijack the function of wild-type cells to fuel CCM lesion growth. By integrating biophysical computational methodologies to quantify cellular forces with advanced molecular techniques, we provide new insights in the etiology of vascular malformations, and open up avenues to study the role of cell mechanics in tissue heterogeneity and disease progression.

bioengineering↗

bioRxiv2022

Traction force reconstruction assessment on real three-dimensional matrices and cellular morphologies

Traction force microscopy (TFM) allows to estimate tractions on the surface of cells when they mechanically interact with hydrogel substrates that mimic the extracellular matrix (ECM). The field of mechanobiology has a strong interest in using TFM in 3D in vitro models. However, there are a number of challenges that hamper the accuracy of 3D TFM and that are often bypassed. In this study, the computational efficiency and accuracy of TFM, referred to traction reconstruction from synthetically generated (control) ground truth solutions, are assessed from four different perspectives: magnitude of cellular pulling force (and hence strain level achieved in the hydrogel), effect of the complexity of the cellular morphology, accuracy and computational efficiency of forward vs inverse traction recovery methods, and the effect of incorrectly selecting a constitutive model that describes the behavior of the ECM (i.e. linear/nonlinear). The main results showed: (i) traction reconstruction is more challenging for complex cell morphologies, (ii) there is no significant impact of the magnitude of cellular pulling force on the overall reconstruction accuracy, and (iii) modeling a nonlinear hydrogel with a linear constitutive model leads to non-negligible errors (up to 80% and 30% for forward and inverse methodologies, respectively) in traction reconstruction. This study expands the characterization of the accuracy and efficiency of 3D TFM, highlighting important factors to be considered in future 3D TFM in vitro applications.

cell biology↗

bioRxiv2021

A mechanobiological model for tumor spheroids evolution: application to glioblastoma

Spheroids are in vitro spherical structures of cell aggregates, eventually cultured within a hydrogel matrix, that are used, among other applications, as a technological platform to investigate tumor formation and evolution. Several interesting features can be replicated using this methodology, such as cell communication mechanisms, the effect of gradients of nutrients, or the creation of realistic 3D biological structures. In this paper, we propose a continuum mechanobiological model which accounts for the most relevant phenomena that take place in tumor spheroids evolution under in vitro suspension, namely, nutrients diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. The model is qualitatively validated, after calibration of the model parameters, versus in vitro experiments of spheroids of different glioblastoma cell lines. This preliminary validation allowed us to conclude that glioblastoma tumor spheroids evolution is mainly driven by mechanical interactions of the cell aggregate and the dynamical evolution of the cell population. In particular, it is concluded that our model is able to explain quite different setups, such as spheroids growth (up to six times the initial configuration for U-87 MG cell line) or shrinking (almost half of the initial configuration for U-251 MG cell line); as the result of the mechanical interplay of cells driven by cellular evolution. Indeed, the main contribution of this work is to link the spheroid evolution with the mechanical activity of cells, coupled with nutrient consumption and the subsequent cell dynamics. All this information can be used to further investigate mechanistic effects in the evolution of tumors and their role in cancer disease. Author summarySpheroids structures of cell aggregates are an available experimental platform to analyze the evolution and drug response of solid tumors. In particular, the dynamics of different glioblastoma cell lines have been studied in this work using spheroids. Interestingly, very different behaviors were observed, from a half of the initial configuration shrinking for U-251 MG cell line to six times the initial configuration growth for U-87 MG cell line. These results were replicated by means of a coupled mathematical model which accounts for nutrients diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. Tumor growth or shrinkage can be explained from a continuum mechanics view driven by cell activity and nutrients availability. This modeling put the focus on mechanistic effects and is aligned with novel experimental techniques to quantify the mechanical microenvironment in tumors. These techniques may be combined with the approach presented in this work to further investigate the role of mechanics in cancer disease.

biophysics↗

bioRxiv2020

Advanced in silico validation framework forthree-dimensional Traction Force Microscopy andapplication to an in vitro model of sproutingangiogenesis

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.

bioengineering↗