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Machine learning approach for discrimination of genotypes based on bright-field cellular images

Biology

Machine learning approach for discrimination of genotypes based on bright-field cellular images

G. Suzuki, Y. Saito, et al.

This study showcases the groundbreaking potential of bright-field microscopy images in distinguishing single-gene mutant cells from wild-type cells through a machine learning approach, conducted by leading researchers including Godai Suzuki and Yutaka Saito. Discover how texture features and morphology inference can revolutionize mutant cell profiling!

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Playback language: English
Abstract
This study investigates the potential of bright-field (BF) microscopy images for discriminating single-gene mutant cells from wild-type cells using a machine learning approach. Hundreds of BF images of single-gene mutant cells were acquired, and single-cell profiles consisting of texture features were quantified. A machine learning model successfully discriminated mutants from wild-type cells (AUC = 0.773). The features contributing to this discrimination were identified and related to the morphology of intracellular structures. Functionally close gene pairs showed similar feature profiles in mutant cells, suggesting that BF image-based profiling holds potential as a tool for mutant cell profiling.
Publisher
npj Systems Biology and Applications
Published On
Jul 21, 2021
Authors
Godai Suzuki, Yutaka Saito, Motoaki Seki, Daniel Evans-Yamamoto, Mikiko Negishi, Kentaro Kakoi, Hiroki Kawai, Christian R. Landry, Nozomu Yachie, Toutai Mitsuyama
Tags
bright-field microscopy
single-gene mutation
machine learning
textural analysis
cell profiling
intracellular structure
feature discrimination

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