Understanding muscle tissue is key not only for advancing our knowledge in biophysical function of the human body, but can also open new paths to better description of pathological states, improve treatment and training and even provide insights for drug development. While using excised muscles is an excellent way to study muscular function, this limits access to non-human tissue, as human muscle tissue donation is very limited. The past decade has seen a rise in 3D in-vitro models of human skeletal muscle, however many current experimental approaches to create such models often struggle with the high variability of primary cells or the limited translational relevance in cases where murine lines are used. Furthermore, the state-of-the-art functional analysis is typically focusing at peak force, which overlooks critical kinetic information. In this study, we present an systematic approach to generate functional engineered skeletal muscle tissues (ESMs), measure the contraction force dynamics and model these with a new approach to extract the effect of a series of standard pharmacological modulators. Within 14 days, LHCN-M2 ESMs mature into aligned, multinucleated myofibers expressing key sarcomeric markers and exhibiting robust excitation-contraction coupling. To decode these dynamics, we introduce a novel mathematical model based on stretched exponential functions. This framework accurately captures the heterogeneous contraction and relaxation phases across diverse phenotypes using only six interpretable parameters. We validated the platforms sensitivity using a library of pharmacological modulators. Our kinetic modeling revealed a distinct parameter fingerprint for different drug classes such as the specific changes of contraction kinetics that peak force alone could not detect. Additionally the presence of the necessary drug targets as well as the state of maturation were investigated by proteomic profiling. Together, study provides a scalable, high-fidelity human platform for high-content pharmacological screening and muscle biophysics.
Luber, M., Schmelz, B., Lenz, C., Betz, T.
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