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Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress

Authors :
Yuanhao Guo
Di Shen
Yanfeng Zhou
Yutong Yang
Jinzhao Liang
Yating Zhou
Ningning Li
Yu Liu
Ge Yang
Wenjing Li
Source :
Frontiers in Cell and Developmental Biology, Vol 9 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

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.

Details

Language :
English
ISSN :
2296634X
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Cell and Developmental Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.89819123a12345a9ba8b29d105ce2eda
Document Type :
article
Full Text :
https://doi.org/10.3389/fcell.2021.767866