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Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining

[Image: see text] 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 machi...

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Autores principales: Jin, Jenny, Schorpp, Kenji, Samaga, Daniel, Unger, Kristian, Hadian, Kamyar, Stockwell, Brent R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938922/
https://www.ncbi.nlm.nih.gov/pubmed/35230809
http://dx.doi.org/10.1021/acschembio.1c00953
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author Jin, Jenny
Schorpp, Kenji
Samaga, Daniel
Unger, Kristian
Hadian, Kamyar
Stockwell, Brent R.
author_facet Jin, Jenny
Schorpp, Kenji
Samaga, Daniel
Unger, Kristian
Hadian, Kamyar
Stockwell, Brent R.
author_sort Jin, Jenny
collection PubMed
description [Image: see text] 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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spelling pubmed-89389222022-03-28 Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining Jin, Jenny Schorpp, Kenji Samaga, Daniel Unger, Kristian Hadian, Kamyar Stockwell, Brent R. ACS Chem Biol [Image: see text] 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. American Chemical Society 2022-03-01 2022-03-18 /pmc/articles/PMC8938922/ /pubmed/35230809 http://dx.doi.org/10.1021/acschembio.1c00953 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Jin, Jenny
Schorpp, Kenji
Samaga, Daniel
Unger, Kristian
Hadian, Kamyar
Stockwell, Brent R.
Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title_full Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title_fullStr Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title_full_unstemmed Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title_short Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining
title_sort machine learning classifies ferroptosis and apoptosis cell death modalities with tfr1 immunostaining
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938922/
https://www.ncbi.nlm.nih.gov/pubmed/35230809
http://dx.doi.org/10.1021/acschembio.1c00953
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