Research Area:  Machine Learning

Abstract:

Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.

Keywords:  
Image-Based Deep Learning
Cell Cycle Phase
Subcellular
Spatiotemporal
Convolutional Neural Network

Author(s) Name:  Yukiko Nagao , Mika Sakamoto , Takumi Chinen , Yasushi Okada , and Daisuke Takao

Publisher name:  The American Society for Cell Biology

DOI:  10.1091/mbc.E20-03-0187

Volume Information:  Volume 31

Paper Link:   https://www.molbiolcell.org/doi/full/10.1091/mbc.E20-03-0187