Longevity of Cardiac and Skeletal Muscle Proteins: The Role of SIMS in Revealing Tissue and Subcellular Compartmentation Patterns

Introduction

Striated muscle depends on long-term protein maintenance to preserve function. That challenge looks different in cardiac and skeletal muscle, where patterns of activity, metabolic demand, and structural organization are not the same. In a recent study, Gugel et al. examined how protein turnover varies across these tissues and where older proteins remain within muscle cells and tissue architecture.

To answer that, the authors combined stable isotope labeling, proteomics, and multi-isotope imaging mass spectrometry.

SIMS was especially important in this study because it allowed the researchers to visualize spatial patterns of isotope retention within muscle tissue, revealing where older proteins persisted at the muscle, myofiber, and sarcomere levels.

Their work produced a broad dataset of protein half-lives in cardiac muscle, slow-twitch skeletal muscle, and fast-twitch skeletal muscle, while also revealing age heterogeneity at the muscle, myofiber, and sarcomere levels. Together, these approaches showed that protein longevity is shaped not only by tissue type, but also by subcellular compartmentation.

• (A) Schematic showing pulse-chase labeling method for mice.
• (B–D) Representative MIMS images from the (B) EDL (scale bars: 10, 5, and 0.5 μm left to right), (C) soleus (scale bars: 10, 5, and 0.5 left to right), and (D) heart (scale bars, 10, 5, and 2.5 μm left to right). Color gradient represents 15N/14N ratio with blue indicating 1× background 15N/14N (37 × 10−4) and magenta indicating 20× background 15N/14N (740 × 10−4).
• (E) Quantification of 15N/14N ratio in the heart, EDL, and soleus. Each point represents one skeletal myofiber or cardiomyocyte from a separate image. Statistical analysis was performed with an ordinary one-way ANOVA.
• (F) Classification of fibers into “fast turnover” and “slow turnover” subpopulations using k-means clustering based on 15N/14N ratio. Statistical analyses were performed by t test with Welch’s correction. Center and error bars represent mean ± SEM.

Methods

The study used two complementary labeling strategies in mice. In one arm of the work, the researchers labeled mice with deuterium oxide and then used protein mass spectrometry to measure deuterium enrichment and incorporation in individual proteins over time. Samples were collected from the left atrium, left ventricle, soleus, and extensor digitorum longus, or EDL. This allowed the authors to calculate protein half-lives across several muscle types and compare their turnover behavior directly.

The second arm used a ^15N pulse-labeling strategy followed by a ^14N chase. Those samples were then analyzed with correlated electron microscopy and multi-isotope imaging mass spectrometry-electron microscopy, or MIMS-EM. This made it possible to visualize isotope retention within tissue architecture and connect protein age to specific cells and structural regions.

For the SIMS measurements, tissue sections were mapped by scanning electron microscopy and then transferred to a CAMECA 50L SIMS. Stable isotope maps were acquired using a cesium beam to ionize surface ions, while multiple isotope signals were collected to reconstruct spatial distributions. In the sarcomere analysis, the images were acquired by the CAMECA NanoSIMS-HR at 60-nm resolution.

Results

A consistent hierarchy emerged across the muscle types examined. Cardiac muscle showed the fastest protein turnover, slow-twitch skeletal muscle was intermediate, and fast-twitch skeletal muscle was the slowest. The median protein half-life was 7.2 days in the left atrium, 9.6 days in the left ventricle, 13.3 days in the soleus, and 18.8 days in the EDL.

The imaging data supported that same pattern. Cardiac myocytes retained relatively little ^15N after the chase period, while soleus and especially EDL retained substantially more. These results indicated that EDL fibers and subcellular compartments had the slowest turnover, whereas cardiac myocytes turned over most of their cellular biomass within 6 months. MIMS-EM also showed that adjacent fibers within EDL and soleus could differ markedly in isotope retention, pointing to heterogeneity within the same tissue.

The authors then examined what might account for these differences. They tested whether myosin isoform composition might be a driving factor by analyzing a transgenic mouse model with increased MYH7 expression in fast-twitch skeletal muscle. They did not observe a difference in average protein half-life in the EDL or left ventricle, leading them to conclude that myosin isoform composition was not the main driver of turnover differences between muscle types.

They next considered muscle use. Using voluntary wheel running, they increased muscle use in mice and measured calpain and proteasome activity to assess protein degradation as a proxy for protein turnover. In EDL, soleus, and left ventricle, exercise increased both calpain I/II and 20S proteasome activity relative to sedentary controls. Taken together, these results support the authors’ conclusion that differences in protein turnover are linked more closely to muscle use than to myosin isoform composition.

The study also found that not all protein classes behaved in the same way. Sarcomeric proteins showed the strongest tissue dependence, with faster turnover in more oxidative and more frequently used muscles, while proteins involved in aerobic metabolism were more tissue independent.

Role of SIMS in the Study

SIMS was central to the spatial dimension of the work. Proteomics provided half-life values for individual proteins, but MIMS-EM showed where older proteins were retained within muscle tissue. That was important because bulk proteomics does not resolve age heterogeneity across individual fibers or subcellular structures.

One of the clearest examples came from the EDL. The study found that the I-band retained higher ^15N levels than the A-band after a 6-month ^14N chase, with the I-band retaining about 50% more ^15N signal. Signal was highest at the Z-line and then declined with distance from it, while the M- and A-bands showed lower signal overall.

The proteomic results helped explain that pattern. The authors identified skeletal muscle alpha actin, encoded by ACTA, as the longest-lived sarcomeric protein in the EDL, with an estimated half-life of 82 days. In a separate EDL sample from a mouse chased with ^14N for 12 months, MIMS-EM showed high ^15N in the I-bands of the sarcomere, indicating that macromolecular complexes inside sarcomeres can persist for up to 12 months.

Implications

The study provides a detailed view of how protein maintenance differs across striated muscle. Cardiac muscle replaces proteins more rapidly than skeletal muscle, and within skeletal muscle, slow- and fast-twitch tissues maintain proteins on different timescales. The work also shows that protein age is not uniform within a tissue or even within a single sarcomere.

Another important finding is that long-lived proteins were not distributed in the same way across muscle types. In EDL, long-lived proteins were found in the nucleus, sarcomeres, mitochondria, cytoplasm, extracellular matrix, and cell membrane. In cardiac muscle, long-lived proteins were confined to the nucleus, mitochondria, and cytoplasm.

The authors note that these findings have broad implications for understanding cellular aging, muscle disease, and therapeutic strategies targeting muscle protein turnover.

Conclusion

Gugel et al. show that muscle protein longevity depends on both tissue type and subcellular compartmentation. By combining proteomics with SIMS-based imaging, the study connected protein half-life measurements with spatially resolved patterns of protein age across cardiac muscle, slow-twitch skeletal muscle, and fast-twitch skeletal muscle.

In this study, SIMS was essential for revealing where older proteins persist within muscle structure. That spatial context helped identify age heterogeneity at the muscle, myofiber, and sarcomere levels and provided a clearer view of how protein maintenance differs across striated muscle.