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Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics

Quantitative Biology volume 8, pages 228–237 (2020)Cite this article

Abstract

Background

ATP is the major energy source for myotube contraction, and is quickly produced to compensate ATP consumption and to maintain sufficient ATP level. ATP is consumed mainly in cytoplasm and produced in mitochondria during myotube contraction. To understand the mechanism of ATP homeostasis during myotube contraction, it is essential to monitor mitochondrial ATP at single-cell level, and examine how ATP is produced and consumed in mitochondria.

Methods

We established C2C12 cell line stably expressing fluorescent probe of mitochondrial ATP, and induced differentiation into myotubes. We gave electric pulse stimulation to the differentiated myotubes, and measured mitochondrial ATP. We constructed mathematical model of mitochondrial ATP at single-cell level, and analyzed kinetic parameters of ATP production and consumption.

Results

We performed hierarchical clustering analysis of time course of mitochondrial ATP, which resulted in two clusters. Cluster 1 showed strong transient increase, whereas cluster 2 showed weak transient increase. Mathematical modeling at single-cell level revealed that the ATP production rate of cluster 1 was larger than that of cluster 2, and that both regulatory pathways of ATP production and consumption of cluster 1 were faster than those of cluster 2. Cluster 1 showed larger mitochondrial mass than cluster 2, suggesting that cluster 1 shows the similar property of slow muscle fibers, and cluster 2 shows the similar property of fast muscle fibers.

Conclusions

Cluster 1 showed the stronger mitochondrial ATP increase by larger ATP production rate, but not smaller consumption. Cluster 1 might reflect the larger oxidative capacity of slow muscle fiber.

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Acknowledgements

We thank laboratory members for critical reading of the manuscript and for technical assistance with the analysis. The computations for this work were performed in part on the NIG supercomputer system at ROIS National Institute of Genetics. This work was supported by the Creation of Fundamental Technologies for Understanding and Control of Biosystem Dynamics, CREST, of the Japan Science and Technology Agency (JST). S. K. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number (17H06300, 17H6299, 18H03979, 19K22860), and M.F. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number (16K12508, 19K20382).

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Authors and Affiliations

  1. Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan

    Naoki Matsuda, Ken-ichi Hironaka, Masashi Fujii, Takumi Wada, Katsuyuki Kunida, Miki Eto, Daisuke Hoshino & Shinya Kuroda

  2. Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan

    Masashi Fujii & Shinya Kuroda

  3. Department of Mathematical and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, 739-8526, Japan

    Masashi Fujii

  4. Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, 630-0192, Japan

    Katsuyuki Kunida

  5. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, 113-0033, Japan

    Haruki Inoue

  6. Department of Engineering Science, Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, 182-8585, Japan

    Daisuke Hoshino

  7. Department of Health Promotion Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, 192-0397, Japan

    Yasuro Furuichi, Yasuko Manabe & Nobuharu L. Fujii

  8. Department of Applied Chemistry, Graduate School of Engineering, University of Tokyo, Tokyo, 113-8656, Japan

    Hiroyuki Noji

  9. Department of Functional Biology, Graduate School of Biostudies, Kyoto University, Kyoto, 606-8501, Japan

    Hiromi Imamura

  10. CREST, Japan Science and Technology Corporation, Tokyo, 113-0033, Japan

    Shinya Kuroda

Authors
  1. Naoki Matsuda
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  2. Ken-ichi Hironaka
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  3. Masashi Fujii
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  4. Takumi Wada
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  8. Daisuke Hoshino
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  9. Yasuro Furuichi
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  11. Nobuharu L. Fujii
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  12. Hiroyuki Noji
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Contributions

N.M., K.K., D.H., and M.E. carried out the experiments; Y.F., Y.M., N.L.F., H.N., and H.I contributed to establishment of C2C12 cells stably expressing mitAT1.03 ATP probe; N.M. and H.I. carried out image analysis; N.M., T. W., and M.F. carried out computational analysis; writing group consisted of N.M., M.F., M.E., K.H and S.K.; and the study was conceived and supervised by N.M. and S.K.

Corresponding author

Correspondence to Shinya Kuroda.

Additional information

Compliance with Ethics Guidelines

The authors Naoki Matsuda, Ken-ichi Hironaka, Masashi Fujii, Takumi Wada, Katsuyuki Kunida, Haruki Inoue, Miki Eto, Daisuke Hoshino, Yasuro Furuichi, Yasuko Manabe, Nobuharu L. Fujii, Hiroyuki Noji, Hiromi Imamura and Shinya Kuroda declare that they have no conflict of interests. All procedures performed in this study were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional Information

Data and model parameters are available online at http://kurodalab.bs.s.u-tokyo.ac.jp/ja/publication/info/matsuda/.

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Matsuda, N., Hironaka, Ki., Fujii, M. et al. Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics. Quant Biol 8, 228–237 (2020). https://doi.org/10.1007/s40484-020-0211-8

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  • DOI: https://doi.org/10.1007/s40484-020-0211-8

Keywords

  • mitocondrial ATP production
  • ATP consumption
  • single-cell analysis
  • mathematical modeling