期刊
ACS CHEMICAL BIOLOGY
卷 17, 期 3, 页码 654-660出版社
AMER CHEMICAL SOC
DOI: 10.1021/acschembio.1c00953
关键词
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资金
- NCI [P01CA87497, R35CA209896]
- National Institute of Neurological Disorders and Stroke (NINDS) [R61NS109407, R33NS109407]
- NIH [UG3CA256962]
- Columbia College Science Scholars program
A machine learning approach combined with staining techniques was developed to unbiasedly classify different cell death mechanisms based on specific cell features, providing a novel analytic framework.
Determining cell death mechanisms occurring in patient and animal tissues is a longstanding goal that requires suitable biomarkers and accurate quantification. However, effective methods remain elusive. To develop more powerful and unbiased analytic frameworks, we developed a machine learning approach for automated cell death classification. Image sets were collected of HT-1080 fibrosarcoma cells undergoing ferroptosis or apoptosis and stained with an anti-transferrin receptor 1 (TfR1) antibody, together with nuclear and F-actin staining. Features were extracted using high-content-analysis software, and a classifier was constructed by fitting a multinomial logistic lasso regression model to the data. The prediction accuracy of the classifier within three classes (control, ferroptosis, apoptosis) was 93%. Thus, TfR1 staining, combined with nuclear and F-actin staining, can reliably detect both apoptotic and ferroptotis cells when cell features are analyzed in an unbiased manner using machine learning, providing a method for unbiased analysis of modes of cell death.
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