Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784672650460659712 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8938922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT jinjenny machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining AT schorppkenji machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining AT samagadaniel machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining AT ungerkristian machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining AT hadiankamyar machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining AT stockwellbrentr machinelearningclassifiesferroptosisandapoptosiscelldeathmodalitieswithtfr1immunostaining |