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Cell Struct Funct. 2018 Aug 31;43(2):153-169. doi: 10.1247/csf.18012. Epub 2018 Jul 26.

Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation.

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Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo.
Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology.
Department of Biological Sciences, Graduate School of Science, University of Tokyo.
Department of Engineering Science, Graduate School of Informatics and Engineering, University of Electro-Communications.
Department of Functional Biology, Graduate School of Biostudies, Kyoto University.
Department of Applied Chemistry, Graduate School of Engineering, University of Tokyo.
CREST, Japan Science and Technology Corporation.


Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.


C2C12; cell segmentation; fluorescence resonance energy transfer; myotube; time lapse images

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