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

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 dif...

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Autores principales:	Godai Suzuki, Yutaka Saito, Motoaki Seki, Daniel Evans-Yamamoto, Mikiko Negishi, Kentaro Kakoi, Hiroki Kawai, Christian R. Landry, Nozomu Yachie, Toutai Mitsuyama
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/66aefc2368b74bae83b5e15802ae8fec
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spelling	oai:doaj.org-article:66aefc2368b74bae83b5e15802ae8fec2021-12-02T17:55:12ZMachine learning approach for discrimination of genotypes based on bright-field cellular images10.1038/s41540-021-00190-w2056-7189https://doaj.org/article/66aefc2368b74bae83b5e15802ae8fec2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00190-whttps://doaj.org/toc/2056-7189Abstract 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.Godai SuzukiYutaka SaitoMotoaki SekiDaniel Evans-YamamotoMikiko NegishiKentaro KakoiHiroki KawaiChristian R. LandryNozomu YachieToutai MitsuyamaNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Godai Suzuki Yutaka Saito Motoaki Seki Daniel Evans-Yamamoto Mikiko Negishi Kentaro Kakoi Hiroki Kawai Christian R. Landry Nozomu Yachie Toutai Mitsuyama Machine learning approach for discrimination of genotypes based on bright-field cellular images
description	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.
format	article
author	Godai Suzuki Yutaka Saito Motoaki Seki Daniel Evans-Yamamoto Mikiko Negishi Kentaro Kakoi Hiroki Kawai Christian R. Landry Nozomu Yachie Toutai Mitsuyama
author_facet	Godai Suzuki Yutaka Saito Motoaki Seki Daniel Evans-Yamamoto Mikiko Negishi Kentaro Kakoi Hiroki Kawai Christian R. Landry Nozomu Yachie Toutai Mitsuyama
author_sort	Godai Suzuki
title	Machine learning approach for discrimination of genotypes based on bright-field cellular images
title_short	Machine learning approach for discrimination of genotypes based on bright-field cellular images
title_full	Machine learning approach for discrimination of genotypes based on bright-field cellular images
title_fullStr	Machine learning approach for discrimination of genotypes based on bright-field cellular images
title_full_unstemmed	Machine learning approach for discrimination of genotypes based on bright-field cellular images
title_sort	machine learning approach for discrimination of genotypes based on bright-field cellular images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/66aefc2368b74bae83b5e15802ae8fec
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_version_	1718379110633308160

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

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