A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis

Drug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phosphol...

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Detalles Bibliográficos
Autores: Hu, Huabin, Tjaden, Amelie, Knapp, Stefan, Antolin, Albert A., Müller, Susanne
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/207922
Acceso en línea:https://hdl.handle.net/2445/207922
Access Level:acceso abierto
Palabra clave:Lipoïdosi
Aprenentatge automàtic
Lipidoses
Machine learning
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repository_id_str
spelling A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosisHu, HuabinTjaden, AmelieKnapp, StefanAntolin, Albert A.Müller, SusanneLipoïdosiAprenentatge automàticLipidosesMachine learningDrug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology.Elsevier BV2024202420232024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion65 p.application/pdfapplication/pdfhttps://hdl.handle.net/2445/207922Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.chembiol.2023.09.003Cell Chemical Biology, 2023, vol. 30, num. 12, p. 1634-1651https://doi.org/10.1016/j.chembiol.2023.09.003ccby-nc-nd (c) Elsevierhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2079222026-05-29T05:05:01Z
dc.title.none.fl_str_mv A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
title A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
spellingShingle A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
Hu, Huabin
Lipoïdosi
Aprenentatge automàtic
Lipidoses
Machine learning
title_short A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
title_full A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
title_fullStr A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
title_full_unstemmed A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
title_sort A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
dc.creator.none.fl_str_mv Hu, Huabin
Tjaden, Amelie
Knapp, Stefan
Antolin, Albert A.
Müller, Susanne
author Hu, Huabin
author_facet Hu, Huabin
Tjaden, Amelie
Knapp, Stefan
Antolin, Albert A.
Müller, Susanne
author_role author
author2 Tjaden, Amelie
Knapp, Stefan
Antolin, Albert A.
Müller, Susanne
author2_role author
author
author
author
dc.subject.none.fl_str_mv Lipoïdosi
Aprenentatge automàtic
Lipidoses
Machine learning
topic Lipoïdosi
Aprenentatge automàtic
Lipidoses
Machine learning
description Drug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/207922
url https://hdl.handle.net/2445/207922
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.chembiol.2023.09.003
Cell Chemical Biology, 2023, vol. 30, num. 12, p. 1634-1651
https://doi.org/10.1016/j.chembiol.2023.09.003
dc.rights.none.fl_str_mv ccby-nc-nd (c) Elsevier
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv ccby-nc-nd (c) Elsevier
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 65 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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