One of the goals of tissue engineering is to generate tissues that can be used as alternatives for donor material to repair or replace damaged tissues or organs. Tissues generated for this purpose will generally be of a size larger than the diffusional limit for nutrients and oxygen. This means that after implantation, the tissue will require a vascular network to supply nutrients to all cells within the tissue.
As part of the foreign body response, a vascular network will generally invade implanted engineered tissues. However, this is a process that takes days or weeks, resulting in suboptimal tissue integration or cell death. The main goal of the Vascularization Lab is to include a vascular network during culture in the lab, which can connect to the vascular network of the patient after implantation. In order to achieve optimal tissue integration, the lab focuses on the organization and maturation of the vascular network, but also of the tissue in which the vascular network is present.
Engineered tissues offer a great promise to the field of medicine as an alternative for donor tissues for which the supply is not meeting the demands. However, the clinical application of engineered tissues is hampered. The integration of engineered tissues after implantation is limited due to the lack of a vascular network. Currently, strategies to include vascular networks rely on the spontaneous organization of vascular cells, or on the patterning of these cells. However, this results in either vascular networks that are not organized, or networks that lose their initial organization fast. This project will use a unique and novel approach to control vascular development and will therefore result in a vascular network with a controllable long-term organization. By allowing for anastomosis, and increasing nutrient delivery, this project will tackle an essential problem and will greatly enhance the clinical applicability of engineered tissues.
Within VascArbor, fluid flows through engineered tissues will be designed and controlled to guide vascular organization. Apart from that, growth factors will be patterned in space and time to further direct the formation of a vascular network with a controlled organization. In parallel, computational models will be developed that can predict vascular organization and development based on processing parameters. This will be a breakthrough in vascularized tissue engineering by enabling a direct link between a desired vascular organization and the tissue construct geometry and processing conditions that are needed to acquire this organization.
To maximize the impact of VascArbor on the field of tissue engineering and medicine, the principles that will guide vascular organization are compatible with multiple current and future tissue fabrication technologies. Within VascArbor, tissue building blocks and bio-printing will be used to engineer vascularized cardiac muscle tissue based on the principles developed in this project.
The current ageing population is faced with an ever increasing shortage of donor tissue for the repair or replacement of
damaged tissues. Tissue engineering is a promising strategy to cope with this shortage. However, even though tissue
engineering has been an active field of research for several decades, the number of clinical successes is limited. This is
often due to a lack of integration and survival of the engineered tissue after implantation.
PreVascIn proposes that the integration and survival of an engineered tissue can be significantly improved by including both a vascular and a neural network. Adding both a vascular and a neural network to an engineered tissue greatly enhances the complexity of this tissue. The cells forming the different tissue components each require different local environments to develop properly. This means that the local environment of individual structures need to be controlled and that current standard approaches, such as simply seeding cells within a scaffold material, are insufficient. As a better alternative approach, in this project I propose to use the ‘Living Legos’ building block system, combined with the use of matrix elasticity and local growth factor delivery to control formation of the separate tissue components. This provides a strong control of tissue development, potentially allowing for the engineering of the vascularized and innervated tissue from a single cell source, as will be investigated in this project. The development of the modular and self-assembly properties of the proposed approach has great potential to result in a highly flexible system, easily translatable to other applications and engineered tissues.
The translation of bone tissue engineering to clinical applications is limited by several factors: the size of engineered tissues that survive after implantation is limited to the millimetre scale by a lack of vascularization, and the integration of engineered tissues is limited by a mechanical mismatch of these tissues after implantation. A vascular network can be included before implantation (prevascularization) to cope with the lack of vascularization. Preconditioning a tissue to the in vivo mechanical environment before implantation can enhance the integration of the tissue. However, in order to mechanically precondition a tissue, one needs to understand the effects of mechanical signals on cells and tissues.
Currently, insufficient experimental data are available on the effect of mechanical signals on cells and tissues. This is especially the case when multiple cell types are present as is the case in vascularized bone. Apart from that, current experimental equipment is unfit to screen for the multitude of variables (different surface strains, different shear stresses and co-cultures of multiple cell types at different ratios) that are present in such systems.
This project will result in a novel lab-on-a-chip based semi-high throughput system to screen for the combined effect of surface strains and shear stresses on multiple cell populations. This system will then be used to characterize the effect of these signals on the cell populations that are needed for prevascularized bone tissue engineering.
As such, this project will result in a vast refinement of mechanobiological modeling. This allows for a better preconditioning of engineered tissues to the mechanical environment after implantation. It will also advance the complementation of bone tissue engineering with vascular tissue engineering, which is a promising strategy to increase the size of engineered tissues that survive after implantation to a clinically relevant scale.
Mechano-regulation theories have been applied to help predict tissue development in tissue engineering scaffolds in the past. For this, Finite Element Models (FEMs) were used to predict the distribution of strains within a scaffold. However, the strains reported in these studies are volumetric strains of the material or strains developed in the extracellular matrix occupying the pore space. The initial phase of cell attachment and growth on the biomaterial surface has thus far been neglected.
This project focusses on the development of a model that determines the magnitude of biomechanical signals on the biomaterial surface, enabling the prediction of cell differentiation stimulus values at this initial stage. Results showed that magnitudes of the 2D strain - termed surface strain - were lower when compared to the 3D volumetric strain or the conventional octahedral shear strain as used in current mechano-regulation theories. When comparing both micro computed tomography (μCT) and computer aided design (CAD) derived FEMs from the same scaffold, strain and fluid shear stress distributions, and subsequently the cell differentiation stimulus, were highly dependent on the pore shape. CAD models were not able to capture the distributions seen in the μCT FEM. The calculated mechanical stimuli can be combined with current mechanobiological models resulting in a tool to predict cell differentiation in the initial phase of tissue engineering. Although experimental data is still necessary to properly link mechanical signals to cell behaviour in this specific setting, this model is an important step towards optimizing scaffold architecture and/or stimulation regimes.