Abstract
Myofibril arrangement is critical to cardiac muscle function in health and disease. Historically, analysis of the impact of myofibril organisation on force and cell contraction has relied on the assumption of uniaxial arrays. However, improvements in imaging indicate that myofibrils form complex networks, though how these networks modulate force has yet to be explored. Here, morphological analysis of sheep left-ventricular cardiomyocytes is utilised to inform a non-linear finite element model of cell contraction. Analysis of deep learning segmentations of z-discs demonstrate that myofibrils are oriented about the contraction axis (mean \(=0.03^{\circ}\)) but deviate locally by up to \(30^{\circ}\) (standard deviation \(=6.56^{\circ}\)). Simulations produce unique deformations for geometries informed by myofibril orientations, displaying internal rotation and off-axis deformations. Moreover, anisotropy generates shear stresses distinct from the uniaxial case, demonstrating spatial relationships that balance shear across the cell and a correlation between shear stress and z-disc orientation. These findings highlight the impact of myofibril networks on forces during cell contraction.
Data availability
Raw microscopy data used in this study are from a prior publication and are available upon request. Segmentation outputs and codes for simulation and analysis are available on our publicly accessible GitHub repository at https://github.com/CellSMB/CardiacMyofibrilsInduceShearStress.git.
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Acknowledgements
The authors thank Rishek Singh for contributions to manual annotations of Z-Discs. This research was supported by The University of Melbourne’s Research Computing Services and the Petascale Campus Initiative. The authors thank the staff in the EM Core Facility in the Faculty of Biology, Medicine and Health, University of Manchester for their assistance and the Wellcome Trust for equipment grant support to the EM Core facility. L.A.M was supported by the University of Melbourne Ingenium Scholarship and Research Training Stipend. A.P.Q was supported by the University of Melbourne Research Training Stipend. We also thank the Welcome Trust for equipment grant support to the EM Core Facility. C.P. was supported by research grants from the British Heart Foundation FS/18/4/33310. D.J.C is the recipient of an Australian Research Council Discovery Project (DP230102550) and a National Health and Medical Research Council Ideas grant (APP2003446). Work in the laboratory of V.R. relevant to this study was in part supported by the Australian Research Council Discovery Projects funding scheme (DP170101358), and the Royal Society International Exchange Award.
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L.A.M. wrote the manuscript, code, created figures, and performed analysis. L.A.M. and V.R. investigated scope and planned investigation. C.P. performed and provided EM imaging. A. P. Q. created machine learning model and applied model to segmentation. L.A.M. segmented raw EM data. V.R., D.J.C., C.P., A.Q. contributed to editing of manuscript.
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Murray, L.A., Quinn, A.P., Pinali, C. et al. Cardiac myofibril networks induce shear stress. npj Syst Biol Appl (2026). https://doi.org/10.1038/s41540-026-00696-1
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DOI: https://doi.org/10.1038/s41540-026-00696-1
