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CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting

Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has rais...

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Autores principales: Schorpp, Kenji, Bessadok, Alaa, Biibosunov, Aidin, Rothenaigner, Ina, Strasser, Stefanie, Peng, Tingying, Hadian, Kamyar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390533/
https://www.ncbi.nlm.nih.gov/pubmed/37524741
http://dx.doi.org/10.1038/s41420-023-01559-y
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author Schorpp, Kenji
Bessadok, Alaa
Biibosunov, Aidin
Rothenaigner, Ina
Strasser, Stefanie
Peng, Tingying
Hadian, Kamyar
author_facet Schorpp, Kenji
Bessadok, Alaa
Biibosunov, Aidin
Rothenaigner, Ina
Strasser, Stefanie
Peng, Tingying
Hadian, Kamyar
author_sort Schorpp, Kenji
collection PubMed
description Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep-learning framework that uses high-content imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities, and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy data sets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an average accuracy of 95% on non-confocal data sets, supporting the capacity of the CellDeathPred framework for cell death discovery.
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spelling pubmed-103905332023-08-02 CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting Schorpp, Kenji Bessadok, Alaa Biibosunov, Aidin Rothenaigner, Ina Strasser, Stefanie Peng, Tingying Hadian, Kamyar Cell Death Discov Article Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep-learning framework that uses high-content imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities, and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy data sets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an average accuracy of 95% on non-confocal data sets, supporting the capacity of the CellDeathPred framework for cell death discovery. Nature Publishing Group UK 2023-07-31 /pmc/articles/PMC10390533/ /pubmed/37524741 http://dx.doi.org/10.1038/s41420-023-01559-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Schorpp, Kenji
Bessadok, Alaa
Biibosunov, Aidin
Rothenaigner, Ina
Strasser, Stefanie
Peng, Tingying
Hadian, Kamyar
CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title_full CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title_fullStr CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title_full_unstemmed CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title_short CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
title_sort celldeathpred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390533/
https://www.ncbi.nlm.nih.gov/pubmed/37524741
http://dx.doi.org/10.1038/s41420-023-01559-y
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