Abstract
The visual classification of cell images according to differences in the spatial patterns of subcellular structure is an important methodology in cell and developmental biology. Experimental perturbation of cell function can induce changes in the spatial distribution of organelles and their associated markers or labels. Here, we demonstrate how to achieve accurate, unbiased, high-throughput image classification using an artificial intelligence (AI) algorithm. We show that a convolutional neural network (CNN) algorithm can classify distinct patterns of Golgi images after drug or siRNA treatments, and we review our methods from cell preparation to image acquisition and CNN analysis.
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Acknowledgments
We gratefully acknowledge Charles Yokoyama (IRCN, The University of Tokyo) for proofreading the manuscript.
Funding This work was supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT)/Japan Society for the Promotion of Science (JSPS) 18K06233 (to D.T.), 18K06133 (A.S.), 19H05974 (Y.O.), 19H05975 (Y.O.), 21K06163 (D.T.), 21H04708 and 21H05028 (A.S.), Japan Science and Technology Agency (JST) JPMJCR20E2 (Y.O.) and JPMJMS2025-14 (Y.O.), Sumitomo Foundation (D.T.), and Kato Memorial Bioscience Foundation (D.T.)
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Takao, D., Kyunai, Y.M., Okada, Y., Satoh, A. (2023). A Primer on Deep Learning-Based Cellular Image Classification of Changes in the Spatial Distribution of the Golgi Apparatus After Experimental Manipulation. In: Wang, Y., Lupashin, V.V., Graham, T.R. (eds) Golgi. Methods in Molecular Biology, vol 2557. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2639-9_18
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DOI: https://doi.org/10.1007/978-1-0716-2639-9_18
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