Enhanced piezoelectric fibered extracellular matrix to promote cardiomyocyte maturation and tissue formation: a 3d computational model

Mechanical and electrical stimuli play a key role in tissue formation, guiding cell processes such as cell migration, differentiation, maturation, and apoptosis. Monitoring and controlling these stimuli on in vitro experiments is not straightforward due to the coupling of these different stimuli. In addition, active and reciprocal cell–cell and cell–extracellular matrix interactions are essential to be considered during formation of complex tissue such as myocardial tissue. In this sense, computational models can offer new perspectives and key information on the cell microenvironment. Thus, we present a new computational 3D model, based on the Finite Element Method, where a complex extracellular matrix with piezoelectric properties interacts with cardiac muscle cells during the first steps of tissue formation. This model includes collective behavior and cell processes such as cell migration, maturation, differentiation, proliferation, and apoptosis. The model has employed to study the initial stages of in vitro cardiac aggregate formation, considering cell–cell junctions, under different extracellular matrix configurations. Three different cases have been purposed to evaluate cell behavior in fibered, mechanically stimulated fibered, and mechanically stimulated piezoelectric fibered extra-cellular matrix. In this last case, the cells are guided by the coupling of mechanical and electrical stimuli. Accordingly, the obtained results show the formation of more elongated groups and enhancement in cell proliferation

A computational model for cardiomyocytes mechano-electric stimulation to enhance cardiac tissue regeneration

Electrical and mechanical stimulations play a key role in cell biological processes, being essential in processes such as cardiac cell maturation, proliferation, migration, alignment, attachment, and organization of the contractile machinery. However, the mechanisms that trigger these processes are still elusive. The coupling of mechanical and electrical stimuli makes it difficult to abstract conclusions. In this sense, computational models can establish parametric assays with a low economic and time cost to determine the optimal conditions of in-vitro experiments. Here, a computational model has been developed, using the finite element method, to study cardiac cell maturation, proliferation, migration, alignment, and organization in 3D matrices, under mechano-electric stimulation. Different types of electric fields (continuous, pulsating, and alternating) in an intensity range of 50–350 Vm−1, and extracellular matrix with stiffnesses in the range of 10–40 kPa, are studied. In these experiments, the group’s morphology and cell orientation are compared to define the best conditions for cell culture. The obtained results are qualitatively consistent with the bibliography. The electric field orientates the cells and stimulates the formation of elongated groups. Group lengthening is observed when applying higher electric fields in lower stiffness extracellular matrix. Groups with higher aspect ratios can be obtained by electrical stimulation, with better results for alternating electric fields

Mechanical stimulation of cell microenvironment for cardiac muscle tissue regeneration: a 3D in-silico model

The processes in which cardiac cells are reorganized for tissue regeneration is still unclear. It is a complicated process that is orchestrated by many factors such as mechanical, chemical, thermal, and/or electrical cues. Studying and optimizing these conditions in-vitro is complicated and time costly. In such cases, in-silico numerical simulations can offer a reliable solution to predict and optimize the considered conditions for the cell culture process. For this aim, a 3D novel and enhanced numerical model has been developed to study the effect of the mechanical properties of the extracellular matrix (ECM) as well as the applied external forces in the process of the cell differentiation and proliferation for cardiac muscle tissue regeneration. The model has into account the essential cellular processes such as migration, cell–cell interaction, cell–ECM interaction, differentiation, proliferation and/or apoptosis. It has employed to study the initial stages of cardiac muscle tissue formation within a wide range of ECM stiffness (8–50 kPa). The results show that, after cell culture within a free surface ECM, cells tend to form elongated aggregations in the ECM center. The formation rate, as well as the aggregation morphology, have been found to be a function of the ECM stiffness and the applied external force. Besides, it has been found that the optimum ECM stiffness for cardiovascular tissue regeneration is in the range of 29–39 kPa, combined with the application of a mechanical stimulus equivalent to deformations of 20–25%

In-vivo cell remote control: a mecano-electro computational study

Abstract ICBB17-12: Nowadays, due to its importance for human survival, tissue andorgans regeneration have received a high attention of the scientificcommunity. For the development of this field, understanding the cellbehavior is essential forin-vivotissue and organs regeneration. Cellbehavior can be controlledin-vitro, among other stimuli, by changingthe extracellular matrix mechanical properties, applying externalforces and/or electrical field. Controlling these stimuliin-vivois nottrivial. Therefore, in this work, to remotely control the local cellenvironment, we consider a microsphere of cell size that is fabricatedfrom a piezoelectric material and charged with nanomagnetic parti-cles. This microsphere is integrated within an extracellular matrix, insuch a way, through an external magnetic field, internal forces canbe generated within the microsphere. As a result, a stiffness gradientas well as an electric field are generated around the microsphere.These cues can be controlled externally by changing the magneticfield intensity..

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson''s ¿ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach. © 2021 The Author(s

A multiscale data-driven approach for bone tissue biomechanics

The data-driven methodology with application to continuum mechanics relies upon two main pillars: (i) experimental characterization of stress–strain pairs associated to different loading states, and (ii) numerical elaboration of the elasticity equations as an optimization (searching) algorithm using compatibility and equilibrium as constraints. The purpose of this work is to implement a multiscale data-driven approach using experimental data of cortical bone tissue at different scales. First, horse cortical bone samples are biaxially loaded and the strain fields are recorded over a region of interest using a digital image correlation technique. As a result, both microscopic strain fields and macroscopic strain states are obtained by a homogenization procedure, associated to macroscopic stress loading states which are considered uniform along the sample. This experimental outcome is here referred as a multiscale dataset. Second, the proposed multiscale data-driven methodology is implemented and analyzed in an example of application. Results are presented both in the macroscopic and microscopic scales, with the latter considered just as a post-process step in the formulation. The macroscopic results show non-smooth strain and stress patterns as a consequence of the tissue heterogeneity which suggest that a preassumed linear homogeneous orthotropic model may be inaccurate for bone tissue. Microscopic results show fluctuating strain fields – as a consequence of the interaction and evolution of the microconstituents – an order of magnitude higher than the averaged macroscopic solution, which evidences the need of a multiscale approach for the mechanical analysis of cortical bone, since the driving force of many biological bone processes is local at the microstructural level. Finally, the proposed multiscale data-driven technique may also be an adequate strategy for the solution of intractable large size multiscale FE2 computational approaches since the solution at the microscale is obtained as a postprocessing. As a main conclusion, the proposed multiscale data-driven methodology is a useful alternative to overcome limitations in the continuum mechanical study of the bone tissue. This methodology may also be considered as a useful strategy for the analysis of additional biological or technological hierarchical multiscale materials

Multiscale multi-dimensional explicit a-posteriori error estimation for fluid dynamics

A-posteriori error estimation of convection-dominated and hyperbolic flow problems remains one of the largest challenges in computational mechanics. The available techniques are either non-robust (i.e. the predicted error differs by orders of magnitude from the exact error) or computationally involved (i.e. requiring the solution of additional partial differential equations). The variational multiscale approach [1,2] offers a fresh point of departure to set up new strategies for the development of efficient and accurate a-posteriori error estimators [3]. This approach has been shown exact for the class of edge-exact solutions [4] and delivers the error in any norm of choice. It is therefore very well suited for hyperbolic solutions computed with stabilized methods. In this paper the previous work is extended to multi-dimensional flows. In particular, adequate norms are proposed for the computation of the error and the proper error intrinsic scales are calculated for the bilinear quad and the linear triangle. Furthermore, the element-interface error along the element edges is considered in the model. Although in multi-dimensions the proposed explicit a-posteriori error estimator is not exact nor a strict upper bound, it will be shown that, when computing flows with stabilized methods, it is very accurate and economical, leading to efficiencies close to one

Computational modelling and analysis of mechanical conditions on cell locomotion and cell–cell interaction

<div><p>Between other parameters, cell migration is partially guided by the mechanical properties of its substrate. Although many experimental works have been developed to understand the effect of substrate mechanical properties on cell migration, accurate 3D cell locomotion models have not been presented yet. In this paper, we present a novel 3D model for cells migration. In the presented model, we assume that a cell follows two main processes: in the first process, it senses its interface with the substrate to determine the migration direction and in the second process, it exerts subsequent forces to move. In the presented model, cell traction forces are considered to depend on cell internal deformation during the sensing step. A random protrusion force is also considered which may change cell migration direction and/or speed. The presented model was applied for many cases of migration of the cells. The obtained results show high agreement with the available experimental and numerical data.</p></div