Sarcomere dynamic instability and stochastic heterogeneity drive robust cardiomyocyte contraction
- Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), partner site Lower Saxony, Germany
- CIDAS (Campus Institute Data Science), University of Göttingen, Germany
- Department of Physics and Soft Matter Center, Duke University, United States
- Third Institute of Physics, Faculty for Physics, University of Göttingen, Germany
- Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology, Germany
Figures
Figure 1 with 2 supplements
Sarcomere tracking in genetically engineered Z-line labeled induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs) on micropatterned soft gels.
(A) Sketch of a human CM adhering to a micropatterned gel (top) and sarcomere structure in relaxed and contracted state (bottom). (B) ACTN2-Citrine CMs (culture day 20) on a polyacrylamide gel substrate (Young’s modulus: 15 kPa), patterned with rectangular stripes of Synthemax (70 × 10 µm). More than 50% of the stripes were typically occupied by single CMs. Inset: zoomed-in view. (C) Tracking of sarcomere motion: (Top) Blend of high-speed confocal movie of a CM adherent to a 15-kPa substrate (left) and deep-learning (3D U-Net)-based segmentation of sarcomere Z-bands (right) with line of interest (LOI, yellow). (Bottom) Intensity extracted along LOI. Inset shows representative section of the Z-band intensity profile. (D, E) Confocal images of representative ACTN2-Citrine-labeled CMs with corresponding Z-band segmentation and LOIs (red lines). (G–I) Z-band trajectories tracked along the LOI. (J–O) Overlay plots of single-sarcomere length change ∆SL(t) for all tracked sarcomeres in the marked LOIs (first 5 s in J–L, zoomed in M–O). Colored lines are individual sarcomere length changes; black lines display average length changes. Contraction intervals are marked by a blue background. Sarcomere popping events are marked with asterisks. Conditions (substrate stiffness): 5 kPa (D, G, J, M); 15 kPa (physiological) (E, H, K, N); 85 kPa (F, I, L, O).
Figure 1—figure supplement 1
Representative cardiomyocyte (CM) selection with automatically and individually detected ROIs.
CMs are randomly selected from the data sets for three substrate stiffnesses (9, 15, and 85 kPa); automatically determined ROIs depicted as red lines.
Figure 1—figure supplement 2
Effects of substrate elasticity on spontaneous beating of cardiomyocytes.
(A) Spontaneous beating frequencies. (B) Beating irregularity: relative standard deviation of beating periods. (C) Average length of contraction cycles Tc. (A–C) include data from 5085 lines of interest (LOIs). Boxes show quartiles, red lines the median, green triangles the mean, and whiskers the 5th and 95th percentiles of the distributions. Blue shaded intervals show included data.
Figure 2
Analysis of sarcomere length change dynamics.
(A–C) Phase-space plots of sarcomere length change ∆SL versus velocity V for three representative lines of interest (LOIs) from cardiomyocytes (CMs) on substrates of increasing stiffness (same LOIs as in Figure 1). Gray lines show individual sarcomere dynamics, black lines average dynamics. Colored dots mark maximal and minimal values of ∆SL and V for individual sarcomere trajectories, with colors corresponding to data in E and G. (D) Box plot of maximal average contractions as a function of substrate stiffness. Maximal average extensions are always close to 0 and not shown. (E) Box plot of maximal shortening and lengthening amplitudes of individual sarcomeres quantified in each contraction cycle. (F) Box plot of maximal average sarcomere lengthening and shortening velocities . (G) Box plot of maximal individual sarcomere lengthening and shortening velocities . Boxes show quartiles, lines the median, triangles the mean, and whiskers the 5th and 95th percentiles of the distribution per condition. Each data point corresponds to the extremal value within one contraction cycle. To weigh each LOI equally, only the first 10 contraction cycles in each recording were considered. (D–G) combine data of 2321 LOIs from 1362 cells (5 kPa: 184, 9 kPa: 580, 15 kPa: 392, 29 kPa: 319, 49 kPa: 503, 85 kPa: 343). Statistical analysis was performed using the Kruskal–Wallis and Dunn’s post hoc tests, with significance set at p < 0.05. All differences were significant. (H, I) Timeseries and Morlet wavelet scalogram of average and single-sarcomere length changes . The top plot displays average (H) and representative single (I) sarcomere length changes over time, with blue background marking contraction periods. The bottom plot presents the wavelet scalograms, depicting the evolution of frequency content in the signal over time, with the blue dashed line signifying the cell’s beating rate. (J) Comparison of time-averaged oscillation frequencies of average (red) and single (black) sarcomere length changes of one representative LOI, showing high-frequency intrinsic oscillatory motion of individual sarcomeres with frequencies of 3–4 Hz, which cancel out on the myofibril scale. For time-averaging of the frequency spectra, only the contraction intervals were included. The black curve shows mean ± SD of 16 sarcomeres in one representative LOI; the black dashed line shows the beating rate and the blue dashed line the peak of the oscillation frequency distribution.
Figure 3
Static versus stochastic heterogeneity in the popping dynamics.
(A) Representative timeseries of sarcomere length changes ΔSL of a myofibril in one representative cardiomyocyte on a 30-kPa substrate. Popping events, defined as sarcomere elongations beyond 0.25 μm within one contraction cycle, are marked in red. Contraction intervals are marked with a blue background. (B) Correlation analysis showing mutual correlation () versus serial correlation (). The x-axis shows the mutual correlation of motion between different sarcomeres (i ≠ j), the y-axis shows the serial correlation of different cycles of one sarcomere (i = j, k ≠ l). Dashed lines delineate regions of static heterogeneity (left) and stochastic heterogeneity (right). Data points are colored by substrate stiffness (5–85 kPa). (C) Ratio R between average mutual and serial correlation of ΔSL, serving as a measure for the degree of stochasticity in motions, for different substrate stiffnesses. Illustrative computer-generated sketches of sarcomere length changes ΔSL in different contraction cycles for purely static heterogeneity (D) and purely stochastic heterogeneity (E). Color bar denotes sarcomere contractile strength. (F) Zoomed view of consecutive experimentally observed contraction cycles showing popping events (red shaded regions) where sarcomeres elongated beyond the threshold of 0.25 µm during contraction. (G) Overall popping frequencies (in units of 1/contraction cycle) for different substrate conditions. (H) Popping frequency as a function of sarcomere equilibrium length SL₀. Lines show averages for different substrate stiffnesses, with underlying distribution of equilibrium lengths shown below in gray. Probability density distributions of time lag (I) and distance (J) between popping events for single line of interest (LOI) compared with corresponding geometric distributions (red lines). Data and statistics: Panels B, C, G–J show data from 2321 LOIs (5 kPa: 184, 9 kPa: 580, 15 kPa: 392, 29 kPa: 319, 49 kPa: 503, 85 kPa: 343). Box plots show quartiles, mean (triangle) and median (line), with whiskers representing the 5th and 95th percentiles. Statistical analysis was performed using Kruskal–Wallis and Dunn’s post hoc tests, with significance set at p < 0.05. All differences were significant.
Figure 4
Mesoscopic model of coupled sarcomeres with non-monotonic force–velocity dynamics and comparison with data.
(A) Schematic of the myofibril model where each sarcomere comprises three parallel components: an active force generator, creating force , a viscous element creating force , and a passive elastic element, creating force . Sarcomeres are mechanically coupled in series; such that an external force generated by elastic substrate deformation is transmitted uniformly through all sarcomeres in the chain. (B) Activation imposes a time-dependent modulation of the active force throughout the contraction cycles, with a waveform . (C) Smooth curves: Activation-dependent force–velocity curves showing an S-shaped non-monotonic relationship with two stable branches and an unstable region with a negative-slope; the force–velocity relations are color-coded by activation level. Superimposed trajectories (gray) are trajectories of 20 individual sarcomeres, generated by Equation 2. The black trajectory is the myofibril average, and one sarcomere is highlighted in red. (D–F) Experimental recordings and corresponding model outputs for cells on soft (5 kPa), intermediate (15 kPa), and stiff (85 kPa) substrates showing timeseries of /length x with blue-shaded contraction intervals (experiment) or (model), together with the associated v–x trajectories in phase space; colored traces depict individual sarcomeres, and black traces denote cycle averages. (G, H) Extrema (maximum and minimum) of length x and velocity v per contraction cycle for individual sarcomeres and for the myofibril average as a function of substrate stiffness ; dashed lines indicate the average, solid lines show individual sarcomere values, colored shaded regions denote the standard deviation, and points with error bars display the experimental data median and inter-quantile range.
Figure 5
Simulation of static and stochastic heterogeneity in sarcomere dynamics.
(A–D) Top panels: Overlaid timeseries of individual sarcomere length changes (colored curves) and the ensemble average (thick black curve). Bottom panels: Trajectories in phase space (length change vs. velocity) of individual sarcomeres (gray) and ensemble average (black). (A) Uniform ensemble (no noise, no force variability). (B) Stochastic heterogeneity driven by noise only. (C) Static heterogeneity driven by intrinsic force variability () only. (D) Mixed, static and stochastic heterogeneity with both noise and force variability. (E) Serial correlation versus mutual correlation across load levels and force variability; marker size encodes the dimensionless simulated substrate stiffness , and color shade indicates the standard deviation of the multiplicative factor modulating each sarcomere’s active force. Dashed lines mark the transition from purely static heterogeneity to purely stochastic heterogeneity . Circles denote simulations with noise/stochastic fluctuations, and crosses denote deterministic simulations without noise/stochastic fluctuations.
Videos
Video 1
Representative cardiomyocytes showing the effect of substrate stiffness on sarcomere heterogeneity.
Real-time confocal movies of three representative ACTN2-Citrine labeled cardiomyocytes (16 ms frame time) on 5, 15, and 85 kPa substrates. Red lines show lines of interest (LOIs) for sarcomere motion tracking and analyses. Middle: Individual sarcomere length changes (colored lines) and average sarcomere length change (black line) of myofibril ROIs shown above. Contraction intervals are shown in light blue. Black vertical line indicates time progress synchronized with movies above. Bottom: Length-change velocity phase portraits showing individual sarcomere trajectories (red traces), the average trajectory (black trace), and individual sarcomere positions (colored dots).
Video 2
Computational model simulations showing the effect of substrate stiffness on sarcomere coordination.
Simulations of sarcomere dynamics under three substrate stiffness conditions (resembling 5, 15, and 85 kPa). Top: Individual sarcomere length changes over time (colored traces) and the ensemble average (black trace). The thin vertical line indicates time progress. Bottom: Phase portraits in velocity–force space showing individual sarcomere trajectories (red traces), the ensemble average trajectory (black trace), and instantaneous sarcomeres at each time point (colored dots). The blue curve represents the force–velocity relationship (nullcline).
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Cell line (Homo sapiens) | TC-1133 (parental hiPSC line) | Baghbaderani, B. A. et al. cGMP-Manufactured Human Induced Pluripotent Stem Cells Are Available for Pre-clinical and Clinical Applications. Stem Cell Reports 5, 647–59 (2015) | hPSCreg: RUCDRi002-A | https://hpscreg.eu/cell-line/RUCDRi002-A |
| Cell line (Homo sapiens) | ACTN2-Citrine reporter hiPSC, TC-1133-ACTN2-Citrine | Härtter et al., 2025 | hPSCreg: RUCDRi002-A-3 | Heterozygous C-terminal citrine knock-in at ACTN2 |
| Peptide, recombinant protein | Synthemax II-SC substrate | Corning | Cat 3535 | 0.1 mg/ml, micropattern transfer |
| Chemical compound, drug | RPMI 1640 with GlutaMAX | Invitrogen | Cat 61870 | Base of serum-free ‘cardio’ medium |
| Chemical compound, drug | B27 supplement | Invitrogen | Cat 17504-044 | 2% (vol/vol) |
| Chemical compound, drug | Penicillin/streptomycin | Invitrogen | Cat 15140 | 100 U/ml / 100 µg/m |
| Chemical compound, drug | StemPro Accutase cell dissociation reagent | Gibco | Cat A11105-01 | Digestion medium |
| Chemical compound, drug | Trypsin | Gibco | Cat 15090-046 | 0.025% in digestion medium |
| Chemical compound, drug | DNase I | Calbiochem | Cat 260913 | 20 µg/ml in digestion medium |
| Chemical compound, drug | Y-27632 (Stemolecule, ROCK inhibitor) | Reprocell | Cat 04-0012-10 | 5 µmol/l |
| Chemical compound, drug | Acrylamide | Sigma-Aldrich | N/A | Gel solutions; elastic moduli per Supplementary file 1A |
| Chemical compound, drug | N,N′-Methylenebis(acrylamide) | Sigma-Aldrich | N/A | Crosslinker in gel solutions |
| Chemical compound, drug | Ammonium persulfate (APS) | Vizag Chemicals | N/A | Polymerization initiator, 1:100 |
| Chemical compound, drug | Tetramethylethylenediamine (TEMED) | Muby Chemicals | N/A | Polymerization accelerator, 1:1000 |
| Chemical compound, drug | Glutaraldehyde | N/A | N/A | 0.5% (vol/vol) in ultrapure water; glass-slide coating |
| Software, algorithm | SarcAsM (Sarcomere Analysis Multitool) | Härtter et al., 2025 | https://github.com/danihae/SarcAsM | Sarcomere detection and tracking; sarc-asm ≥0.4.0 (PyPI: sarc-asm) |
| Software, algorithm | SarcomereModel | This paper | https://github.com/danihae/SarcomereModel | Mesoscopic model of coupled sarcomeres; sarcomere-model v0.2.0, Python ≥3.10, <3.14 |
| Other | 6-Well culture plates | Corning | Cat 3516 | Maintenance culture |
| Other | SU-8 photoresist, Series 3005 | MicroChem | Series 3005 | Photoresist masters for micropatterns |
| Other | PDMS (Sylgard 184 elastomer kit) | Dow Corning | Sylgard 184 | 10:1 base-to-curing-agent; stamps |
| Other | Silicon wafers | Microchemicals GmbH | N/A | Substrate for photoresist masters |
| Other | Custom photomasks | Compugraphics | N/A | Micropattern geometry |
| Other | Confocal microscope TCS SP5 II | Leica | N/A | 8000 Hz resonant scanner, 67 fps |
| Other | Physica MCR 501 rheometer | Anton Paar | N/A | Measurement of gel elastic moduli |
| Other | Experimental dataset and analysis code | This paper | https://doi.org/10.5281/zenodo.17564384 | Zenodo archive |
Additional files
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Supplementary file 1
Polyacrylamide gel compositions and model parameters.
(A) Composition of polyacrylamide soft gels used in the study. All gels used here were made from the same respective stock solution (10 ml). The Young’s modulus was measured for a gel sample from each prepared stock solution using a rheometer (Physica MCR 501 Rheometer, Anton Paar, Germany) using a 25 mm, 2° cone plate with a sample volume of 140 μl. A time sweep (1 hr, spacing 30 s, 1% strain, 1 Hz), a frequency sweep (3 measurements per decade, 1% strain, 0.01–100 Hz), and an amplitude sweep (3 measurements per decade, 0.01–100% strain, 1 Hz) were performed consecutively. (B) Model parameters obtained through differential evolution optimization. Parameters were optimized by minimizing the Kolmogorov–Smirnov statistic between model simulations and experimental data from ACTN2-Citrine hiPSC-derived cardiomyocytes. Search ranges were based on physical constraints and preliminary analyses. Parameter values were rounded.
- https://cdn.elifesciences.org/articles/97321/elife-97321-supp1-v1.pdf
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MDAR checklist
- https://cdn.elifesciences.org/articles/97321/elife-97321-mdarchecklist1-v1.docx
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Sarcomere dynamic instability and stochastic heterogeneity drive robust cardiomyocyte contraction
eLife 13:RP97321.
https://doi.org/10.7554/eLife.97321.4
