| Literature DB >> 35794686 |
Qizheng Wang1, Jun Lu2,3, Ke Fan2, Yiwei Xu2, Yucui Xiong2, Zhiyong Sun2,3, Man Zhai2, Zhizhong Zhang2,3, Sheng Zhang2, Yan Song2, Jianzhong Luo2, Mingliang You4, Meijin Guo5, Xiao Zhang6,7.
Abstract
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.Entities:
Keywords: High-throughput microscopy; Kidney organoid; Machine learning; Nephrotoxicity
Mesh:
Year: 2022 PMID: 35794686 PMCID: PMC9264113 DOI: 10.1631/jzus.B2100701
Source DB: PubMed Journal: J Zhejiang Univ Sci B ISSN: 1673-1581 Impact factor: 5.552