Transforming tissue engineering through a deep dive into dynamic organ physiology
Description: Engineered living tissues, which aim to functionally replace human tissues damaged due to disease, injury or congenital defects, have gained significant prominence in modern medicine. These engineered tissues have also demonstrated promising results as models for disease modeling and drug testing. Current tissue engineering approaches use cells (animal or patient-derived) seeded or encapsulated within biomaterial scaffolds. During maturation (tissue growth), the scaffolds and the culture media provide the right topographical or chemotactic cues for the cells to proliferate, differentiate and deposit the tissue-specific extracellular matrix (ECM). A major challenge with current tissue engineering approaches is creating functional tissues for dynamic organs (e.g., lungs, heart, etc.), which undergo repetitive physiological motion with volumetric dilation-contraction. Dynamic tissues pose specific design challenges which are overlooked by conventional approaches: 1) Auxetic tissue characteristics, where the tissues expand across multiple directions simultaneously, and 2) Anisotropic cellular and ECM arrangement, which impart a directional variation in the tissue properties. Bridging these two design challenges requires a complete re-thinking of how we design and fabricate engineered tissues. In addition, it is equally important to use the right cells and biomaterials which mimic the composition of the native tissues, as well the tissue maturation conditions mimicking the growth and function in vivo. To be able to solve this multi-faceted challenge, we will develop new synergistic deep learning (DL)-based design and computational tools to analyze the magnetic resonance imaging (MRI) scans, and identify patient-specific conformant designs for the cardiac meshes which demonstrate Poisson’s ratios similar to the native tissue (Objective 1). Then, volumetric bioprinting (VBP) with or without tomographic projections will be used to rapidly fabricate the tissue constructs based on photo-clickable decellularized extracellular matrix (dECM) resin encapsulating human induced pluripotent stem cell (hiPSC)-derived cardiac cells (Objective 2). The cardiac tissues will then be maturated in custom dynamic culture bioreactors which provide electrical, mechanical and biochemical stimulation to achieve biomimetic tissues (Objective 3), which will be evaluated for their pre-clinical effectiveness in improving cardiac function post myocardial infarction in rat models (Objective 4).Given the established expertise in high-throughput volumetric printing and development of new photoclick biomaterials, the Zenobi-Wong group will be the ideal host lab for the Ambizione. To be able to achieve the challenging tasks in the Ambizione project, I have gathered a consortium of experts (see letters of collaborations in the Annexes) – 1) Prof. Fanny Yang (Department of Computer Science, ETHZ) – for helping develop the DL models, and 2) Prof. Maximillian Y. Emmert (Institute for Regenerative Medicine, University of Zurich), for providing the cells and performing the animal surgeries. By combining the otherwise distinct fields of deep learning, and tissue engineering and biofabrication, the project will provide a framework for optimizing the tissue design and fabrication for dynamic organs. A human cell-based biomimetic cardiac model could also reduce animals used in research or elucidate disease mechanisms. The new innovations in tissue design and materials, fabrication and maturation will transform conventional tissue engineering approaches and open-up new collaboration opportunities. The Ambizione project will be a steppingstone in my career as an independent researcher, with my lab investigating the frontiers of interdisciplinary science and creating impact by meaningfully engaging society.
Amount: CHF 902,200
Funding type: fellowship
Funder scheme: Ambizione
Start date: 2023-10-01
End date: 2027-09-30
Investigator: Chansoria, Parth
Institution: ETH Zurich – ETHZ
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Funded outputs count: 9
Funder award ID: 216356
OpenAlex ID: G715009347