Cargando…
Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress
Endoplasmic reticulum stress (ER stress) is a condition that is defined by abnormal accumulation of unfolded proteins. It plays an important role in maintaining cellular protein, lipid, and ion homeostasis. By triggering the unfolded protein response (UPR) under ER stress, cells restore homeostasis...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8865080/ https://www.ncbi.nlm.nih.gov/pubmed/35223863 http://dx.doi.org/10.3389/fcell.2021.767866 |
_version_ | 1784655578332659712 |
---|---|
author | Guo, Yuanhao Shen, Di Zhou, Yanfeng Yang, Yutong Liang, Jinzhao Zhou, Yating Li, Ningning Liu, Yu Yang, Ge Li, Wenjing |
author_facet | Guo, Yuanhao Shen, Di Zhou, Yanfeng Yang, Yutong Liang, Jinzhao Zhou, Yating Li, Ningning Liu, Yu Yang, Ge Li, Wenjing |
author_sort | Guo, Yuanhao |
collection | PubMed |
description | Endoplasmic reticulum stress (ER stress) is a condition that is defined by abnormal accumulation of unfolded proteins. It plays an important role in maintaining cellular protein, lipid, and ion homeostasis. By triggering the unfolded protein response (UPR) under ER stress, cells restore homeostasis or undergo apoptosis. Chronic ER stress is implicated in many human diseases. Despite extensive studies on related signaling mechanisms, reliable image biomarkers for ER stress remain lacking. To address this deficiency, we have validated a morphological image biomarker for ER stress and have developed a deep learning-based assay to enable automated detection and analysis of this marker for screening studies. Specifically, ER under stress exhibits abnormal morphological patterns that feature ring-shaped structures called whorls (WHs). Using a highly specific chemical probe for unfolded and aggregated proteins, we find that formation of ER whorls is specifically associated with the accumulation of the unfolded and aggregated proteins. This confirms that ER whorls can be used as an image biomarker for ER stress. To this end, we have developed ER-WHs-Analyzer, a deep learning-based image analysis assay that automatically recognizes and localizes ER whorls similarly as human experts. It does not require laborious manual annotation of ER whorls for training of deep learning models. Importantly, it reliably classifies different patterns of ER whorls induced by different ER stress drugs. Overall, our study provides mechanistic insights into morphological patterns of ER under stress as well as an image biomarker assay for screening studies to dissect related disease mechanisms and to accelerate related drug discoveries. It demonstrates the effectiveness of deep learning in recognizing and understanding complex morphological phenotypes of ER. |
format | Online Article Text |
id | pubmed-8865080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88650802022-02-24 Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress Guo, Yuanhao Shen, Di Zhou, Yanfeng Yang, Yutong Liang, Jinzhao Zhou, Yating Li, Ningning Liu, Yu Yang, Ge Li, Wenjing Front Cell Dev Biol Cell and Developmental Biology Endoplasmic reticulum stress (ER stress) is a condition that is defined by abnormal accumulation of unfolded proteins. It plays an important role in maintaining cellular protein, lipid, and ion homeostasis. By triggering the unfolded protein response (UPR) under ER stress, cells restore homeostasis or undergo apoptosis. Chronic ER stress is implicated in many human diseases. Despite extensive studies on related signaling mechanisms, reliable image biomarkers for ER stress remain lacking. To address this deficiency, we have validated a morphological image biomarker for ER stress and have developed a deep learning-based assay to enable automated detection and analysis of this marker for screening studies. Specifically, ER under stress exhibits abnormal morphological patterns that feature ring-shaped structures called whorls (WHs). Using a highly specific chemical probe for unfolded and aggregated proteins, we find that formation of ER whorls is specifically associated with the accumulation of the unfolded and aggregated proteins. This confirms that ER whorls can be used as an image biomarker for ER stress. To this end, we have developed ER-WHs-Analyzer, a deep learning-based image analysis assay that automatically recognizes and localizes ER whorls similarly as human experts. It does not require laborious manual annotation of ER whorls for training of deep learning models. Importantly, it reliably classifies different patterns of ER whorls induced by different ER stress drugs. Overall, our study provides mechanistic insights into morphological patterns of ER under stress as well as an image biomarker assay for screening studies to dissect related disease mechanisms and to accelerate related drug discoveries. It demonstrates the effectiveness of deep learning in recognizing and understanding complex morphological phenotypes of ER. Frontiers Media S.A. 2022-01-21 /pmc/articles/PMC8865080/ /pubmed/35223863 http://dx.doi.org/10.3389/fcell.2021.767866 Text en Copyright © 2022 Guo, Shen, Zhou, Yang, Liang, Zhou, Li, Liu, Yang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Guo, Yuanhao Shen, Di Zhou, Yanfeng Yang, Yutong Liang, Jinzhao Zhou, Yating Li, Ningning Liu, Yu Yang, Ge Li, Wenjing Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title | Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title_full | Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title_fullStr | Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title_full_unstemmed | Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title_short | Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress |
title_sort | deep learning-based morphological classification of endoplasmic reticulum under stress |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8865080/ https://www.ncbi.nlm.nih.gov/pubmed/35223863 http://dx.doi.org/10.3389/fcell.2021.767866 |
work_keys_str_mv | AT guoyuanhao deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT shendi deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT zhouyanfeng deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT yangyutong deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT liangjinzhao deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT zhouyating deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT liningning deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT liuyu deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT yangge deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress AT liwenjing deeplearningbasedmorphologicalclassificationofendoplasmicreticulumunderstress |