Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress

Yuanhao Guo; Di Shen; Yanfeng Zhou; Yutong Yang; Jinzhao Liang; Yating Zhou; Ningning Li; Yu Liu; Ge Yang; Wenjing Li

doi:10.3389/fcell.2021.767866

Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress

Front Cell Dev Biol. 2022 Jan 21:9:767866. doi: 10.3389/fcell.2021.767866. eCollection 2021.

Authors

Yuanhao Guo^{1

2}, Di Shen³, Yanfeng Zhou^{1

2}, Yutong Yang¹, Jinzhao Liang¹, Yating Zhou^{1

2}, Ningning Li⁴, Yu Liu³, Ge Yang^{1

2}, Wenjing Li^{1

2}

Affiliations

¹ Laboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
² School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
³ CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
⁴ Tomas Lindahl Laboratory, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.

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.

Keywords: ER stress; deep learning; homeostasis; image biomarker; morphological classification.