Dataset Information

Image-based multiscale modeling predicts tissue-level and network-level fiber reorganization in stretched cell-compacted collagen gels.

ABSTRACT: The mechanical environment plays an important role in cell signaling and tissue homeostasis. Unraveling connections between externally applied loads and the cellular response is often confounded by extracellular matrix (ECM) heterogeneity. Image-based multiscale models provide a foundation for examining the fine details of tissue behavior, but they require validation at multiple scales. In this study, we developed a multiscale model that captured the anisotropy and heterogeneity of a cell-compacted collagen gel subjected to an off-axis hold mechanical test and subsequently to biaxial extension. In both the model and experiments, the ECM reorganized in a nonaffine and heterogeneous manner that depended on multiscale interactions between the fiber networks. Simulations predicted that tensile and compressive fiber forces were produced to accommodate macroscopic displacements. Fiber forces in the simulation ranged from -11.3 to 437.7 nN, with a significant fraction of fibers under compression (12.1% during off-axis stretch). The heterogeneous network restructuring predicted by the model serves as an example of how multiscale modeling techniques provide a theoretical framework for understanding relationships between ECM structure and tissue-level mechanical properties and how microscopic fiber rearrangements could lead to mechanotransductive cell signaling.

SUBMITTER: Sander EA

PROVIDER: S-EPMC2764876 | biostudies-other | 2009 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Image-based multiscale modeling predicts tissue-level and network-level fiber reorganization in stretched cell-compacted collagen gels.

Sander Edward A EA Stylianopoulos Triantafyllos T Tranquillo Robert T RT Barocas Victor H VH

Proceedings of the National Academy of Sciences of the United States of America 20091001 42

The mechanical environment plays an important role in cell signaling and tissue homeostasis. Unraveling connections between externally applied loads and the cellular response is often confounded by extracellular matrix (ECM) heterogeneity. Image-based multiscale models provide a foundation for examining the fine details of tissue behavior, but they require validation at multiple scales. In this study, we developed a multiscale model that captured the anisotropy and heterogeneity of a cell-compac ...[more]

PMID: 19805118

Similar Datasets

Project description:Collagen is the most prominent protein of human tissues. Its content and organization define to a large extent the mechanical properties of tissue as well as its function. Methods that have been used traditionally to visualize and analyze collagen are invasive, provide only qualitative or indirect information, and have limited use in studies that aim to understand the dynamic nature of collagen remodeling and its interactions with the surrounding cells and other matrix components. Second harmonic generation (SHG) imaging emerged as a promising noninvasive modality for providing high-resolution images of collagen fibers within thick specimens, such as tissues. In this article, we present a fully automated procedure to acquire quantitative information on the content, orientation, and organization of collagen fibers. We use this procedure to monitor the dynamic remodeling of collagen gels in the absence or presence of fibroblasts over periods of 12 or 14 days. We find that an adaptive thresholding and stretching approach provides great insight to the content of collagen fibers within SHG images without the need for user input. An additional feature-erosion and feature-dilation step is useful for preserving structure and noise removal in images with low signal. To quantitatively assess the orientation of collagen fibers, we extract the orientation index (OI), a parameter based on the power distribution of the spatial-frequency-averaged, two-dimensional Fourier transform of the SHG images. To measure the local organization of the collagen fibers, we access the Hough transform of small tiles of the image and compute the entropy distribution, which represents the probability of finding the direction of fibers along a dominant direction. Using these methods we observed that the presence and number of fibroblasts within the collagen gel significantly affects the remodeling of the collagen matrix. In the absence of fibroblasts, gels contract, especially during the first few days, in a manner that allows the fibers to remain mostly disoriented, as indicated by small OI values. Subtle changes in the local organization of fibers may be taking place as the corresponding entropy values of these gels show a small decrease. The presence of fibroblasts affects the collagen matrix in a manner that is highly dependent on their number. A low density of fibroblasts enhances the rate of initial gel contraction, but ultimately leads to degradation of collagen fibers, which start to organize in localized clumps. This degradation and reorganization is seen within the first days of incubation with fibroblasts at a high density and is followed by de novo collagen fiber deposition by the fibroblasts. These collagen fibers are more highly oriented and organized than the fibers of the original collagen gel. These initial studies demonstrate that SHG imaging in combination with automated image analysis approaches offer a noninvasive and easily implementable method for characterizing important features of the content and organization of collagen in tissuelike specimens. Therefore, these studies could offer important insights for improving tissue engineering and disease diagnostic efforts.

Dataset Information

Image-based multiscale modeling predicts tissue-level and network-level fiber reorganization in stretched cell-compacted collagen gels.

Publications

Image-based multiscale modeling predicts tissue-level and network-level fiber reorganization in stretched cell-compacted collagen gels.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets