AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network. (23rd July 2022)

• Record Type: Journal Article
• Authors: Tang, Meng; Su, Yan; Zhao, Wei; Niu, Zubiao; Ruan, Banzhan; Li, Qinqin; Zheng, You; Wang, Chenxi; Zhang, Bo; Zhou, Fuxiang; Wang, Xiaoning; Huang, Hongyan; Shi, Hanping; Sun, Qiang
• Editors: Chen, Luonan
• Is Part Of: Journal of molecular cell biology. Volume 14:Number 6(2022)
• Publication Date: 2022-07-23
• Subjects: cell-in-cell structure -- artificial intelligence -- AIM-CICs -- cell death -- entosis -- convolutional neural network; Molecular biology -- Periodicals; 571.605
• DOI: 10.1093/jmcb/mjac044
• Languages: English
• ISSNs: 1674-2788

Abstract

ABSTRACT: Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readout that can only be quantified manually at present. Moreover, the manual CIC quantification is generally over-simplified as CIC counts, which represents a major hurdle against profound mechanistic investigations. In this study, we take advantage of artificial intelligence technology to develop an automatic identification method for CICs (AIM-CICs), which performs comprehensive CIC analysis in an automated and efficient way. The AIM-CICs, developed on the algorithm of convolutional neural network, can not only differentiate between CICs and non-CICs (the area under the receiver operating characteristic curve (AUC) > 0.99), but also accurately categorize CICs into five subclasses based on CIC stages and cell number involved (AUC > 0.97 for all subclasses). The application of AIM-CICs would systemically fuel research on CIC-mediated cell death, such as high-throughput screening.