Integrating structural heterogeneity, fiber orientation, and recruitment in multiscale ECM mechanics

Multimodal experimental studies of the passive mechanical behavior of human aortas: Current approaches and future directions

Cardiovascular diseases are the leading cause of death worldwide and include, among others, critical conditions of the aortic wall. Importantly, such critical conditions require effective diagnosis and treatment, which are not yet accurate enough. However, they could be significantly strengthened with predictive material models of the aortic wall. In particular, such predictive models could support surgical decisions, preoperative planning, and estimation of postoperative tissue remodeling. However, developing a predictive model requires experimental data showing both structural parameters and mechanical behavior. Such experimental data can be obtained using multimodal experiments. This review therefore discusses the current approaches to multimodal experiments. Importantly, the strength of the aortic wall is determined primarily by its passive components, i.e., mainly collagen, elastin, and proteoglycans. Therefore, this review focuses on multimodal experiments that relate the passive mechanical behavior of the human aortic wall to the structure and organization of its passive components. In particular, the multimodal experiments are classified according to the expected results. Multiple examples are provided for each experimental class and summarized with highlighted advantages and disadvantages of the method. Finally, future directions of multimodal experiments are envisioned and evaluated. STATEMENT OF SIGNIFICANCE: Multimodal experiments are innovative approaches that have gained interest very quickly, but also recently. This review presents therefore a first clear summary of groundbreaking research in the field of multimodal experiments. The benefits and limitations of various types of multimodal experiments are thoroughly discussed, and a comprehensive overview of possible results is provided. Although this review focuses on multimodal experiments performed on human aortic tissues, the methods used and described are not limited to human aortic tissues but can be extended to other soft materials.

The effects of axial twisting and material non-symmetry on arterial bent buckling

Artery buckling occurs due to hypertensive lumen pressure or reduced axial tension and other pathological conditions. Since arteries in vivo often experience axial twisting and the collagen fiber alignment in the arterial wall may become nonsymmetric, it is imperative to know how axial twisting and nonsymmetric collagen alignment would affect the buckling behavior of arteries. To this end, the objective of this study was to determine the effect of axial twisting and nonsymmetric collagen fiber distribution on the critical pressure of arterial bent buckling. The buckling model analysis was generalized to incorporate an axial twist angle and nonsymmetric fiber alignment. The effect of axial twisting on the critical pressure was simulated and experimentally tested in a group of porcine carotid arteries. Our results showed that axial twisting tends to reduce the critical pressure depending on the axial stretch ratio and twist angle. In addition, nonsymmetric fiber alignment reduces the critical pressure. Experimental results confirmed that a twist angle of 90° reduces the critical pressure significantly (p < 0.05). It was concluded that axial twisting and non-axisymmetric collagen fibers distribution could make arteries prone to bent buckling. These results enrich our understanding of artery buckling and vessel tortuosity. The model analysis and results could also be applicable to other fiber reinforced tubes under lumen pressure and axial twisting.

Changes in the microstructure of the human aortic adventitia under biaxial loading investigated by multi-photon microscopy

Among the three layers of the aortic wall, the media is primarily responsible for its mechanical properties, but the adventitia prevents the aorta from overstretching and rupturing. The role of the adventitia is therefore crucial with regard to aortic wall failure, and understanding the load-induced changes in tissue microstructure is of high importance. Specifically, the focus of this study is on the changes in collagen and elastin microstructure in response to macroscopic equibiaxial loading applied to the aortic adventitia. To observe these changes, multi-photon microscopy imaging and biaxial extension tests were performed simultaneously. In particular, microscopy images were recorded at 0.02 stretch intervals. The microstructural changes of collagen fiber bundles and elastin fibers were quantified with the parameters of orientation, dispersion, diameter, and waviness. The results showed that the adventitial collagen was divided from one into two fiber families under equibiaxial loading conditions. The almost diagonal orientation of the adventitial collagen fiber bundles remained unchanged, but the dispersion was substantially reduced. No clear orientation of the adventitial elastin fibers was observed at any stretch level. The waviness of the adventitial collagen fiber bundles decreased under stretch, but the adventitial elastin fibers showed no change. These original findings highlight differences between the medial and adventitial layers and provide insight into the stretching process of the aortic wall. STATEMENT OF SIGNIFICANCE: To provide accurate and reliable material models, it is essential to understand the mechanical behavior of the material and its microstructure. Such understanding can be enhanced with tracking of the microstructural changes caused by mechanical loading of the tissue. This study provides therefore a unique dataset of structural parameters of the human aortic adventitia obtained under equibiaxial loading. The structural parameters describe orientation, dispersion, diameter, and waviness of collagen fiber bundles and elastin fibers. Eventually, the microstructural changes in the human aortic adventitia are compared with the microstructural changes in the human aortic media from a previous study. This comparison reveals the cutting-edge findings on the differences in the response to the loading between these two human aortic layers.

An ultrastructural 3D reconstruction method for observing the arrangement of collagen fibrils and proteoglycans in the human aortic wall under mechanical load

An insight into changes of soft biological tissue ultrastructures under loading conditions is essential to understand their response to mechanical stimuli. Therefore, this study offers an approach to investigate the arrangement of collagen fibrils and proteoglycans (PGs), which are located within the mechanically loaded aortic wall. The human aortic samples were either fixed directly with glutaraldehyde in the load-free state or subjected to a planar biaxial extension test prior to fixation. The aortic ultrastructure was recorded using electron tomography. Collagen fibrils and PGs were segmented using convolutional neural networks, particularly the ESPNet model. The 3D ultrastructural reconstructions revealed a complex organization of collagen fibrils and PGs. In particular, we observed that not all PGs are attached to the collagen fibrils, but some fill the spaces between the fibrils with a clear distance to the collagen. The complex organization cannot be fully captured or can be severely misinterpreted in 2D. The approach developed opens up practical possibilities, including the quantification of the spatial relationship between collagen fibrils and PGs as a function of the mechanical load. Such quantification can also be used to compare tissues under different conditions, e.g., healthy and diseased, to improve or develop new material models. STATEMENT OF SIGNIFICANCE: The developed approach enables the 3D reconstruction of collagen fibrils and proteoglycans as they are embedded in the loaded human aortic wall. This methodological pipeline comprises the knowledge of arterial mechanics, imaging with transmission electron microscopy and electron tomography, segmentation of 3D image data sets with convolutional neural networks and finally offers a unique insight into the ultrastructural changes in the aortic tissue caused by mechanical stimuli.

Application of Temporary, Cell-Containing Alginate Microcarriers to Facilitate the Fabrication of Spatially Defined Cell Pockets in 3D Collagen Hydrogels

Mimicking the complexity of natural tissue is a major challenge in the field of tissue engineering. Here, a facile 2-step fabrication method to prepare 3D constructs with distinct regions of high cell concentrations and without the need for elaborate equipment is proposed. The initial incorporation of cells in a sacrificial alginate matrix allows the addition of other, cell relevant biopolymers, such as, collagen to form a spatially confined, interpenetrating network at the microscale. A layered structure at the macroscale can be achieved by incorporating these cell-containing microspheres in thin collagen layers. Cells are locally released by de-gelling the alginate matrix and their attachment to the collagen hydrogel layers has been studied. The use of the murine pre-osteoblast cell line MC3T3-E1 as an example cell line shows that the cells behave differently in their cell migration pattern based on the initial composition of the alginate microspheres.

Monitoring matrix remodeling in the cellular microenvironment using microrheology for complex cellular systems

Multiple particle tracking (MPT) microrheology was employed for monitoring the development of extracellular matrix (ECM) mechanical properties in the direct microenvironment of living cells. A customized setup enabled us to overcome current limitations: (i) Continuous measurements were enabled using a cell culture chamber, with this, matrix remodeling by fibroblasts in the heterogeneous environment of macroporous scaffolds was monitored continuously. (ii) Employing tracer laden porous scaffolds for seeding human mesenchymal stem cells (hMSCs), we followed conventional differentiation protocols. Thus, we were, for the first time able to study the massive alterations in ECM elasticity during hMSC differentiation. (iii) MPT measurements in 2D cell cultures were enabled using a long distance objective. Exemplarily, local mechanical properties of the ECM in human umbilical vein endothelial cell (HUVEC) cultures, that naturally form 2D layers, were investigated scaffold-free. Using our advanced setup, we measured local, apparent elastic moduli G_0,app in a range between 0.08 and 60 Pa. For fibroblasts grown in collagen-based scaffolds, a continuous decrease of local matrix elasticity resulted during the first 10 hours after seeding. The osteogenic differentiation of hMSC cells cultivated in similar scaffolds, led to an increase of G_0,app by 100 %, whereas after adipogenic differentiation it was reduced by 80 %. The local elasticity of ECM that was newly secreted by HUVECs increased significantly upon addition of protease inhibitor and in high glucose conditions even a twofold increase in G_0,app was observed. The combination of these advanced methods opens up new avenues for a broad range of investigations regarding cell-matrix interactions and the propagation of ECM mechanical properties in complex biological systems. STATEMENT OF SIGNIFICANCE: Cells sense the elasticity of their environment on a micrometer length scale. For studying the local elasticity of extracellular matrix (ECM) in the direct environment of living cells, we employed an advanced multipleparticle tracking microrheology setup. MPT is based on monitoring the Brownian motion oftracer particles, which is restricted by the surrounding network. Network elasticity can thusbe quantified. Overcoming current limitations, we realized continuous investigations of ECM elasticityduring fibroblast growth. Furthermore, MPT measurements of stem cell ECM showed ECMstiffening during osteogenic differentiation and softening during adipogenic differentiation.Finally, we characterized small amounts of delicate ECM newly secreted in scaffold-freecultures of endothelial cells, that naturally form 2D layers.

A Multiscale Model to Predict Neuronal Cell Deformation with Varying Extracellular Matrix Stiffness and Topography

Introduction

Neuronal cells are sensitive to mechanical properties of extracellular matrix (ECM) such as stiffness and topography. Cells contract and exert a force on ECM to detect the microenvironment, which activates the signaling pathway to influence the cell functions such as differentiation, migration, and proliferation. There are numerous transmembrane proteins that transmit signals; however, integrin and neural cellular adhesion molecules (NCAM) play an important role in sensing the ECM mechanical properties. Mechanotransduction of cell-ECM is the key to understand the influence of ECM stiffness and topography; therefore, in this study, we develop a multiscale computational model to investigate these properties.

Methods

This model couples the molecular behavior of integrin and NCAM to microscale interactions of neuronal cell and the ECM. We analyze the atomistic/molecular behavior of integrin and NCAM due to mechanical stimuli using steered molecular dynamics. The microscale properties of the neuronal cell and the ECM are simulated using non-linear finite element analysis by applying cell contractility.

Results

We predict that by increasing the ECM stiffness, a neuronal cell exerts greater stress on the ECM. However, this stress reaches a saturation value for a threshold stiffness of ECM, and the saturation value is affected by the ECM thickness, topography, and clustering of integrin and NCAMs. Further, the ECM topography leads to asymmetric stress and deformation in the neuronal cell. Predicted stress distribution in neuronal cell and ECM are consistent with experimental results from the literature.

Conclusion

The multiscale computational model will guide in selecting the optimal ECM stiffness and topography range for in vitro studies.