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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
Förderungen National Institute of Neurological Disorders and Stroke
National Cancer Institute