AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network.

Journal: Journal of molecular cell biology
Published Date: Nov 26, 2022

Abstract

Edited by Luonan Chen 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.

Authors

  • Meng Tang
    Department of computer science, University of Waterloo, ON, Canada.
  • Yan Su
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Zubiao Niu
    Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China.
  • Banzhan Ruan
    Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China.
  • Qinqin Li
    Beijing Shijitan Hospital of Capital Medical University, Beijing 100038, China.
  • You Zheng
    Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China.
  • Chenxi Wang
    Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Fuxiang Zhou
    Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, Wuhan 430071, China.
  • Xiaoning Wang
    E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201204, China.
  • Hongyan Huang
    Beijing Shijitan Hospital of Capital Medical University, Beijing 100038, China.
  • Hanping Shi
    Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. Electronic address: shihp@ccmu.edu.cn.
  • Qiang Sun
    Research Center for Agricultural and Sideline Products Processing, Henan Academy of Agricultural Sciences, 116 Park Road, Zhengzhou 450002, PR China.

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