Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining.
ACS Chem Biol
; 17(3): 654-660, 2022 03 18.
Article
en En
| MEDLINE
| ID: mdl-35230809
ABSTRACT
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Receptores de Transferrina
/
Ferroptosis
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
ACS Chem Biol
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos