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Related Experiment Video

Updated: Jun 19, 2026

Quantification of Subcellular Glycogen Distribution in Skeletal Muscle Fibers using Transmission Electron Microscopy
08:32

Quantification of Subcellular Glycogen Distribution in Skeletal Muscle Fibers using Transmission Electron Microscopy

Published on: February 7, 2022

A semantic segmentation model to predict subcellular glycogen localization using transmission electron microscopy

Anders A Hansen1, Jacob M Egebjerg2, Kristian Solem3

  • 1University of Southern Denmark, Department of Sports Science and Clinical Biomechanics, Odense M, Denmark.

Plos One
|June 17, 2026
PubMed
Summary

A new deep learning method automates glycogen quantification in skeletal muscle using transmission electron microscopy (TEM). This approach significantly reduces analysis time, enabling efficient study of muscle glycogen metabolism.

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Area of Science:

  • Cellular Biology
  • Biophysics
  • Computational Biology

Background:

  • Transmission electron microscopy (TEM) is crucial for analyzing subcellular glycogen in skeletal muscle.
  • Manual analysis of TEM images is labor-intensive and hinders large-scale research.

Purpose of the Study:

  • To develop and validate a deep learning-based semantic segmentation approach for automated glycogen quantification in human skeletal muscle.
  • To enable efficient, high-throughput investigation of compartmentalized glycogen metabolism.

Main Methods:

  • Utilized deep learning (attention U-Net models) to segment subcellular structures and detect glycogen particles in TEM images.
  • Trained models on manually annotated skeletal muscle biopsies from healthy men with varying glycogen levels.
  • Combined region and glycogen models to estimate compartment-specific glycogen areal densities.

Main Results:

  • The deep learning model accurately quantified subcellular glycogen distribution, achieving biases below 15% and coefficients of variation below 26%.
  • Model-derived glycogen density strongly correlated with biochemical measurements of muscle glycogen content.
  • The automated workflow demonstrated high time efficiency compared to manual analysis.

Conclusions:

  • The validated semantic segmentation workflow offers an objective and time-efficient tool for quantifying skeletal muscle glycogen.
  • This approach facilitates high-throughput studies of glycogen metabolism and localization.
  • Openly available model weights and code support broader research applications.