High-throughput “read-on-ski” automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids
Author(s) -
Qizheng Wang,
Jun Lu,
Ke Fan,
Yiwei Xu,
Yucui Xiong,
Zhiyong Sun,
Man Zhai,
Zhizhong Zhang,
Sheng Zhang,
Yan Song,
Jianzhong Luo,
Ming You,
Meijin Guo,
Xiao Zhang
Publication year - 2022
Publication title -
journal of zhejiang university science b
Language(s) - English
Resource type - Journals
eISSN - 1862-1783
pISSN - 1673-1581
DOI - 10.1631/jzus.b2100701
Subject(s) - organoid , computer science , nephrotoxicity , kidney , toxicity , artificial intelligence , medicine , biology , neuroscience
Organoid models are used to study kidney physiology, such as the assessment of nephrotoxicity and underlying disease processes. Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies, but there is a need to accelerate basic and translational research in the field. Here, we developed an automated continuous imaging setup with the "read-on-ski" law of control to maximize temporal resolution with minimum culture plate vibration. High-accuracy performance was achieved: organoid screening and imaging were performed at a spatial resolution of 1.1 μm for the entire multi-well plate under 3 min. We used the in-house developed multi-well spinning device and cisplatin-induced nephrotoxicity model to evaluate the toxicity in kidney organoids using this system. The acquired images were processed via machine learning-based classification and segmentation algorithms, and the toxicity in kidney organoids was determined with 95% accuracy. The results obtained by the automated "read-on-ski" imaging device, combined with label-free and non-invasive algorithms for detection, were verified using conventional biological procedures. Taking advantage of the close-to-in vivo-kidney organoid model, this new development opens the door for further application of scaled-up screening using organoids in basic research and drug discovery.
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