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Sinergia: Machine-learned Design and Bioxolography of Functional 3D Skeletal Muscle Tissues

Research Project
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01.07.2023
 - 30.06.2027

Engineered 3D skeletal muscle tissue (SMT) is an important tool to study muscle physiology and disease. SMT engineering has applications in regenerative medicine, in vitro drug screening, bio-hybrid robotics, and cultured meat. However, state-of-the-art engineered muscle tissue does not effectively mimic the cellular heterogeneity, architecture, and performance of biological muscle. To address this, researchers are developing techniques to bioprint, differentiate, and mature functional muscle architectures. Machine Learning (ML) approaches could streamline SMT design by rapidly exploring the ideal conditions for muscle biofabrication, computationally capturing the complex interplay between biofabrication parameters (bioprinting, differentiation, and mechanical and electrical maturation), and the resulting functionality of the contractile SMT. This project consists of four key objectives: (1) Adaptation of xolography1 to muscle bioprinting. We will develop the bioxolography technique to fabricate highly aligned and anisotropic cell-laden hydrogels without constraints on the achievable shapes. (2) Biofabrication and differentiation of 3D heterocellular muscle constructs. We will develop a protocol to bioprint, culture, and differentiate arrays of muscle bundles which contain multiple cell types, thereby mimicking the natural muscle. (3) Maturation and characterization of engineered muscle tissues. We will mechanically stimulate our engineered muscle tissues for maturation and characterize their cellular morphology and contractile performance. (4) Development of an ML pipeline to guide the biofabrication and design of muscle actuators. Finally, we will develop an ML pipeline which maps biofabrication and tissue engineering parameters to performance metrics of engineered muscle (structure and function), leveraging a differentiable simulation approach originally developed to model soft material and actuator deformation in soft robotics. We will demonstrate proof-of-concept of this model by designing a centimeter-scale asymmetric, antagonistically actuated skeletal muscle construct. Methods. (1) We will print highly aligned, multinucleated and large (2 to 3-cm-long) bundles of muscle fibers by developing bioxolography: a linear volumetric bioprinting process (LVBP), realized by designing new photoresins and photoinitiators for previously established xolographic printing. (2) Primary murine myoblasts will be co-printed with fibro-adipogenic progenitors (FAPs), and cell culture will be optimized to promote myogenesis, myogenic cell differentiation, and tissue maturation in three dimensional tissue. (3) Muscle tissue will be mechanically stimulated under varying conditions, and characterized for structure and response to electrical stimulation. In a further iteration, we will also characterize muscle tissue co-cultured with optogenetically modified motor neurons to realize a light-controllable innervation system for remote neural actuation of muscle tissue. (4) We will create an in-silico-to-in-vitro platform to optimize muscle design by using ML and our differentiable finite element method (FEM) to map biofabrication parameters to SMT's contractility. This project will accelerate and improve the design and fabrication of functional SMTs by providing an in-silico-to-in-vitro platform for 3D muscle construct design. Our work will provide insights into biofabrication, soft materials, 3D cell culture techniques, hetero-cellular models, bio-hybrid technologies, robotic design, and biophysical cell stimulation. We anticipate multidisciplinary advancements in tissue engineering, muscle physiopathology, 3D bioprinting, the development of new therapeutics, biological machine learning, bio-hybrid robotics, and engineering with living materials.

Funding

Sinergia: Machine-learned Design and Bioxolography of Functional 3D Skeletal Muscle Tissues

SNF Projekt (GrantsTool), 07.2023-06.2027 (48)
PI : Rüegg, Markus A..

Members (1)

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Markus A. Rüegg

Principal Investigator