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Schorpp, K. ; Bessadok, A. ; Biibosunov, A. ; Rothenaigner, I. ; Strasser, S. ; Peng, T. ; Hadian, K.

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

Cell Death Discov. 9:277 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Mechanisms; Death; Assay
ISSN (print) / ISBN 2058-7716
e-ISSN 2058-7716
Zeitschrift Cell Death Discovery
Quellenangaben Band: 9, Heft: 1, Seiten: , Artikelnummer: 277 Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Signaling and Translation (SAT)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen Projekt DEAL
Helmholtz Munich
Helmholtz Association's Initiative and Networking Fund