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Jin, J.* ; Schorpp, K.K. ; Samaga, D. ; Unger, K. ; Hadian, K. ; Stockwell, B.R.*

Machine learning classifies ferroptosis and apoptosis cell death modalities with TfR1 immunostaining.

ACS Chem. Biol. 17, 654-660 (2022)
Postprint DOI PMC
Open Access Green
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 1554-8929
e-ISSN 1554-8937
Zeitschrift ACS Chemical Biology
Quellenangaben Band: 17, Heft: 3, Seiten: 654-660 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort Washington
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Molecular Toxicology and Pharmacology (TOXI)
Research Unit Radiation Cytogenetics (ZYTO)
Förderungen National Institute of Neurological Disorders and Stroke
National Cancer Institute