Poster N1 - Automated and Scalable Muscle Tissue System with Magnetic-based Functional Evaluation

Introduction: In vitro muscle tissue chips are widely used as models for pre-clinical disease modeling and drug testing attributed to their physiological relevance, especially when integrated with functional measurements. However, their application in large-scale pharmaceutical contexts is limited by issues related to cost-effectiveness. These issues primarily stem from the labor intensive, low-throughput nature of current systems, which often leads to inconsistency across samples and heavy reliance on the experimenter’s skill level. To address these challenges, we have developed an automated, scalable muscle tissue chip system that enables high-throughput, real-time functional measurements.

Materials and

Methods: To improve the efficiency of large-scale muscle chip fabrication and functional evaluation, we have designed a 96-well plate human striated muscle chip device that can be automatically fabricated. This device employs a programmable micro-pipetting robot to perform essential fabrication steps, including cell resuspension, hydrogel premixing, mold casting, and media refreshing. The muscle tissues are constructed on pairs of elastic polydimethylsiloxane posts, each featuring one rigid and one flexible post with a permanent magnet embedded on the tip. For functional measurement, 96 anisotropic magneto-resistance sensors are strategically aligned with the magnets to monitor muscle contractile kinetics. These sensors measure parameters such as muscle twitch force, speed, and power in real-time by detecting changes in magnetic flux resulting from the displacement of the magnets during muscle contractions. We characterized the development of muscle tissue over time to demonstrate its physiological relevance and potential utility in pharmaceutical contexts using human-sourced myocytes. This longitudinal analysis ensures that the tissue models accurately mimic human muscle physiology, which is critical for their application in drug testing and disease modeling.

Results, Conclusions, and Discussions: Our automated fabrication process significantly improved the efficiency by 92.5% in terms of time, allowing to produce 96 muscle tissues in six minutes with a success rate exceeding 97%, compared to the conventional 24-well format manual fabrication. The magnetic-based functional measurements demonstrated a signal-to-noise ratio over 60 dBf. Sensor readouts, calibrated against the golden standards with optical tracking of tissue contractions, show a linear correlation that facilitates direct translation between these two measurement modalities.
This system effectively produces three-dimensional cardiac and skeletal muscle tissues from human induced pluripotent stem cells (hiPSCs) and human primary cells, exhibiting robust physiological functions and gene expression profiles. Over four weeks, hiPSC-derived cardiac tissues significantly increase in spontaneous twitch strength from 62.848 ± 7.647 μN (n = 7) on day 7 to 146.855 ± 6.919 μN (n = 7) on day 21, then stabilize. Human primary skeletal muscles respond to electrical stimuli with twitch forces stabilizing from day 7 to 28 post-biofabrication, with forces increasing from 1 Hz single twitches at 50.62 ± 6.28 μN (n = 7) to 30 Hz sustained tetanic forces at 126.17 ± 11.12 μN (n = 7). Stable expressions of myosin heavy chain and ion-regulating proteins of cardiac and skeletal muscle tissues suggest maturation of both muscle types at the transcriptional level. We are exploring potential pharmaceutical applications of the platform by testing its functional responses to 11 CiPA drugs for cardiac tissues and 6 hypertrophy-/atrophy-inducing drugs for skeletal muscle tissues.
The described system significantly enhances the throughput of muscle tissue fabrication and functional evaluation, reducing costs, time, and labor required for large-scale production. This advancement offers a promising approach to advance the use of muscle chip devices in large-scale drug screening and disease modeling.

Acknowledgements (Optional):