Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress.

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

doi:10.3389/fcell.2021.767866

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.

Highlights

The endoplasmic reticulum (ER) is a continuous membrane-bound organelle network that plays key roles in protein synthesis and modification, lipid biogenesis, and ionic homeostasis (Friedman and Voeltz, 2011)
By comparing ER morphology under normal conditions versus IRE1α activation, we found that the formation of ER whorls is initiated when unfolded protein response (UPR) pathways are activated under induced ER stress and that it is dependent on the duration and strength of the induced ER stress
Because Tg activation of ER stress increases the level of phosphorylated IRE1α (Han et al, 2009), we used the amount of phosphorylated IRE1α normalized by the total amount of IRE1α as an indicator of ER stress

Summary

Introduction

The endoplasmic reticulum (ER) is a continuous membrane-bound organelle network that plays key roles in protein synthesis and modification, lipid biogenesis, and ionic homeostasis (Friedman and Voeltz, 2011). ER stress is strongly implicated in the onset and progression of a wide range of human diseases, including neurodegenerative diseases, metabolic diseases, and cancer (Ozcan et al, 2004; Zhang et al, 2005; Wang and Kaufman, 2016) It can cause alterations of protein synthesis or folding and deleterious cellular responses including accumulation of lipids and activation of autophagy. In comparison to target-based screening, phenotypic screening utilizes readouts that are more observable and physiologically relevant for drug discoveries (Swinney and Anthony, 2011; Moffat et al, 2017) It can accelerate drug discoveries by using cell models of diseases with the support of high-throughput imaging (Zheng et al, 2013; Moffat et al, 2017). Such an assay has yet to be developed for ER stress

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Conclusion