Machine learning approach for discrimination of genotypes based on bright-field cellular images.
Machine learning approach for discrimination of genotypes based on bright-field cellular images.
复制标题
DOI：
10.1038/s41540-021-00190-w
复制
发表时间：
2021-07-21
影响因子：
4
通讯作者：
Mitsuyama T
中科院分区：
文献类型：
--
作者：
Suzuki G;Saito Y;Seki M;Evans-Yamamoto D;Negishi M;Kakoi K;Kawai H;Landry CR;Yachie N;Mitsuyama T
形态学分析是成熟的光学显微镜和尖端机器视觉技术的结合，它在高通量表型分析中积累了成功的应用。一个主要的问题是可以从图像中提取多少信息来识别细胞之间的遗传差异。虽然特定细胞器的荧光显微镜图像已被广泛用于单细胞分析，但无标记细胞的明场（BF）显微镜图像的潜在能力仍有待测试。在这里，我们研究是否可以使用机器学习方法基于无标记细胞的BF图像来区分单基因扰动。我们获取了数百张单基因突变细胞的BF图像，量化了由细胞区域纹理特征组成的单细胞轮廓，并构建了一个机器学习模型来区分突变细胞和野生型细胞。有趣的是，突变体被成功地从野生型区分（在受试者工作特征曲线下的面积= 0.773）。识别了有助于区分的特征，包括与细胞区域内出现的结构形态相关的特征。此外，功能接近的基因对显示出突变细胞的相似特征谱。我们的研究表明，单基因突变细胞可以区分野生型细胞的基础上BF图像，这表明作为一个有用的工具突变细胞分析的潜力。
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.
登录
查看更多内容
影响因子：
3.2
作者：
Gomes AV
通讯作者：
Gomes AV
影响因子：
15
作者：
Arora, Pooja;Venkataswamy, Manjunatha M.;Baena, Andres;Bricard, Gabriel;Li, Qian;Veerapen, Natacha;Ndonye, Rachel;Park, Jeong Ju;Lee, Ji Hyung;Seo, Kyung-Chang;Howell, Amy R.;Chang, Young-Tae;Illarionov, Petr A.;Besra, Gurdyal S.;Chung, Sung-Kee;Porcelli, Steven A.
通讯作者：
Porcelli, Steven A.
影响因子：
9.9
作者：
Yachie N;Petsalaki E;Mellor JC;Weile J;Jacob Y;Verby M;Ozturk SB;Li S;Cote AG;Mosca R;Knapp JJ;Ko M;Yu A;Gebbia M;Sahni N;Yi S;Tyagi T;Sheykhkarimli D;Roth JF;Wong C;Musa L;Snider J;Liu YC;Yu H;Braun P;Stagljar I;Hao T;Calderwood MA;Pelletier L;Aloy P;Hill DE;Vidal M;Roth FP
通讯作者：
Roth FP
影响因子：
48
作者：
Ounkomol C;Seshamani S;Maleckar MM;Collman F;Johnson GR
通讯作者：
Johnson GR
DOI：
10.1083/jcb.201610026
发表时间：
2017-01-02
期刊：
The Journal of cell biology
影响因子：
--
作者：
Grys BT;Lo DS;Sahin N;Kraus OZ;Morris Q;Boone C;Andrews BJ
通讯作者：
Andrews BJ