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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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~3 min • Beginner • English
Abstract
Morphological profiling is a combination of established optical microscopes and cutting-edge machine vision technologies, which stacks up successful applications in high-throughput phenotyping. One major question is how much information can be extracted from an image to identify genetic differences between cells. While fluorescent microscopy images of specific organelles have been broadly used for single-cell profiling, the potential ability of bright-field (BF) microscopy images of label-free cells remains to be tested. Here, we examine whether single-gene perturbation can be discriminated based on BF images of label-free cells using a machine learning approach. We acquired hundreds of BF images of single-gene mutant cells, quantified single-cell profiles consisting of texture features of cellular regions, and constructed a machine learning model to discriminate mutant cells from wild-type cells. Interestingly, the mutants were successfully discriminated from the wild type (area under the receiver operating characteristic curve = 0.773). The features that contributed to the discrimination were identified, and they included those related to the morphology of structures that appeared within cellular regions. Furthermore, functionally close gene pairs showed similar feature profiles of the mutant cells. Our study reveals that single-gene mutant cells can be discriminated from wild-type cells based on BF images, suggesting the potential as a useful 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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