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bioRxiv · 10.1101/2021.02.26.432996

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

Tang, M.·Su, Y.·Zhao, W.·Niu, Z.·Ruan, B.·Li, Q.·Zheng, Y.·Wang, C.·Zhou, Y.·Zhang, B.·Zhou, F.·Huang, H.·Shi, H.·Sun, Q.
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Abstract

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

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Tang, M., Su, Y., Zhao, W., Niu, Z., Ruan, B., Li, Q., Zheng, Y., Wang, C., Zhou, Y., Zhang, B., Zhou, F., Huang, H., Shi, H., Sun, Q.. 2021-02-26. AIM-CICs: automatic identification method for Cell-in-cell structures based on convolutional neural network. https://doi.org/10.1101/2021.02.26.432996

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