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Machine learning classifies ferroptosis and apoptosis cell death modalities with TfR1 immunostaining.
ACS Chem. Biol. 17, 654-660 (2022)
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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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
1554-8929
e-ISSN
1554-8937
Journal
ACS Chemical Biology
Quellenangaben
Volume: 17,
Issue: 3,
Pages: 654-660
Publisher
American Chemical Society (ACS)
Publishing Place
Washington
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Molecular Toxicology and Pharmacology (TOXI)
Translational Metabolic Oncology (TMO)
Translational Metabolic Oncology (TMO)
Grants
National Institute of Neurological Disorders and Stroke
National Cancer Institute
National Cancer Institute