Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress

doi:10.3389/fcell.2021.767866

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 计算生物学与机器智能

	Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress
	Guo, Yuanhao 1,2; Shen, Di 3; Zhou, Yanfeng 1,2; Yang, Yutong 1; Liang, Jinzhao 1; Zhou, Yating 1,2; Li, Ningning 4; Liu, Yu3 ; Yang, Ge 1,2; Li, Wenjing1,2
发表期刊	FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
ISSN	2296-634X
	2022-01-21
卷号	9 页码:13
通讯作者	Yang, Ge(ge.yang@ia.ac.cn) ; Li, Wenjing(wenjing.li@ia.ac.cn)
摘要	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.
关键词	ER stress morphological classification image biomarker deep learning homeostasis
DOI	10.3389/fcell.2021.767866
关键词[WOS]	UNFOLDED PROTEIN RESPONSE ; MODULATES ER STRESS ; MICROSCOPY IMAGES ; IRE1-ALPHA ; AUTOPHAGY ; TRANSLATION ; COMBINATION ; EXPRESSION ; DISCOVERY ; APOPTOSIS
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[91954201] ; Natural Science Foundation of China[31971289] ; Natural Science Foundation of China[32101216] ; Chinese Academy of Sciences[292019000056] ; University of Chinese Academy of Sciences[115200M001] ; Beijing Municipal Science & Technology Commission[5202022]
项目资助者	Natural Science Foundation of China ; Chinese Academy of Sciences ; University of Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission
WOS研究方向	Cell Biology ; Developmental Biology
WOS类目	Cell Biology ; Developmental Biology
WOS记录号	WOS:000751283800001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	人工智能+科学
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47371
专题	多模态人工智能系统全国重点实验室_计算生物学与机器智能
通讯作者	Yang, Ge; Li, Wenjing
作者单位	1.Chinese Acad Sci, Inst Automat, Lab Computat Biol & Machine Intelligence, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian, Peoples R China 4.Sun Yat Sen Univ, Affiliated Hosp 7, Tomas Lindahl Lab, Shenzhen, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Guo, Yuanhao,Shen, Di,Zhou, Yanfeng,et al. Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress[J]. FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY,2022,9:13.
APA	Guo, Yuanhao.,Shen, Di.,Zhou, Yanfeng.,Yang, Yutong.,Liang, Jinzhao.,...&Li, Wenjing.(2022).Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress.FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY,9,13.
MLA	Guo, Yuanhao,et al."Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress".FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY 9(2022):13.