Network Architecture and Transport Properties of ECMs

How nutrients and signalling molecules are transported through the ECM is critical to tissue health and effective signalling and communication between cells, which ultimately determines the growth, function, and regeneration of biological tissues. Yet, the transport properties of the ECM remain largely unknown. These properties are often poorly characterized, due to the experimental and theoretical challenges of elucidating how transport within the ECM depends on the interaction between the anisotropic structure of the ECM and its mechanical properties. ECMs are very variable depending on the types of tissues they reside in, for instance epithelial, nervous or connective tissue. Each of these tissue types is characterized by different transport properties, which allow them to achieve effective tissue function. Some ECMs possess highly organized communication channels forming signalling network structures, such as the osteocyte lacunocanalicular network (LCN) in bone. Other ECMs are formed by fibrous meshes with strong structural anisotropies, such as cartilage, wound healing tissue, or pathological tissue, which affect the transport of molecules of different sizes differentially. Transport through the ECM may be a passive, diffusion-dominated process, or it may be strongly driven by external mechanical stimulations (pressure gradients, walking, heartbeat, breathing) and be dominated by fluid flow induced by the deformation of the tissue.

Left: Work flow from an image stack obtained by confocal laser scanning microscopy of the lacunocanalicular network (LCN) (gray, left) to a binarised image of the LCN (red, middle) to a mathematical network consisting of edges (i.e., canaliculi) and nodes (i.e., lacunae and meeting points of canaliculi) (blue, right). From Weinkamer et al. Curr Osteoporos Rep. 2019; 17(4). Right: Pattern of fluid flow velocities through the LCN in diaphyseal region of a mouse tibia. From van Tol et al., PNAS 2020; 117(51).

Through the collaborative strengths of QUT and MPIKG, we are using a combination of experiments, imaging, data analysis, and mathematical modelling to characterise the network architecture and transport properties of the osteocyte LCN in bone. We are using native animal, human and bioengineered tissues of different types to allow variations in structural anisotropies and mechanical deformations. Tissue-engineered models of the osteocyte LCN are produced by the additive manufacturing of soft network composites with various types of architectures, and using primary human cells, to provide a physiologically relevant model platform with a hierarchy of permeabilities as a model of transport within human LCN under various conditions. This manufacturing strategy allows a high degree of freedom and control over structural properties of the composites to investigate key influences on transport properties through the osteocyte LCN, as a function of biochemical interactions, anisotropic structures, mechanical properties, and external stimuli in the context of the tissue function and to validate the results from the computational modelling arising from the study of native tissues.

Left, Middle: Confocal imaging of the lacunocanalicular network (LCN) in a whole mouse tibia cross-section (From van Tol et al., PNAS 2020; 117(51)). Right: Coloured SEM image of human osteocyte organization (in cyan) in cortical human bone.

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