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On-line Access: 2022-07-06

Received: 2021-08-07

Revision Accepted: 2022-02-04

Crosschecked: 2022-07-06

Cited: 0

Clicked: 378

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Meijin GUO

https://orcid.org/0000-0002-3171-4802

Xiao ZHANG

https://orcid.org/0000-0002-3894-8513

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Journal of Zhejiang University SCIENCE B 2022 Vol.23 No.7 P.564-577

http://doi.org/10.1631/jzus.B2100701


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, Mingliang YOU, Meijin GUO, Xiao ZHANG

Affiliation(s):  State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology,Shanghai200237,China; more

Corresponding email(s):   zhang_xiao@gibh.ac.cn, guo_mj@ecust.edu.cn

Key Words:  Kidney organoid, High-throughput microscopy, Nephrotoxicity, Machine learning


Qizheng WANG, Jun LU, Ke FAN, Yiwei XU, Yucui XIONG, Zhiyong SUN, Man ZHAI, Zhizhong ZHANG, Sheng ZHANG, Yan SONG, Jianzhong LUO, Mingliang YOU, Meijin GUO, Xiao ZHANG. High-throughput “read-on-ski” automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids[J]. Journal of Zhejiang University Science B, 2022, 23(7): 564-577.

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author="Qizheng WANG, Jun LU, Ke FAN, Yiwei XU, Yucui XIONG, Zhiyong SUN, Man ZHAI, Zhizhong ZHANG, Sheng ZHANG, Yan SONG, Jianzhong LUO, Mingliang YOU, Meijin GUO, Xiao ZHANG",
journal="Journal of Zhejiang University Science B",
volume="23",
number="7",
pages="564-577",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2100701"
}

%0 Journal Article
%T High-throughput “read-on-ski” automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids
%A Qizheng WANG
%A Jun LU
%A Ke FAN
%A Yiwei XU
%A Yucui XIONG
%A Zhiyong SUN
%A Man ZHAI
%A Zhizhong ZHANG
%A Sheng ZHANG
%A Yan SONG
%A Jianzhong LUO
%A Mingliang YOU
%A Meijin GUO
%A Xiao ZHANG
%J Journal of Zhejiang University SCIENCE B
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%N 7
%P 564-577
%@ 1673-1581
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2100701

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A1 - Qizheng WANG
A1 - Jun LU
A1 - Ke FAN
A1 - Yiwei XU
A1 - Yucui XIONG
A1 - Zhiyong SUN
A1 - Man ZHAI
A1 - Zhizhong ZHANG
A1 - Sheng ZHANG
A1 - Yan SONG
A1 - Jianzhong LUO
A1 - Mingliang YOU
A1 - Meijin GUO
A1 - Xiao ZHANG
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B2100701


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.

一种可用于使用个性化的人肾类器官进行化合物毒性筛选的高通量"扫板时读取"自动成像和无标签检测系统

王起正1,卢俊2,3,樊科2,徐毅炜2,熊玉翠2,孙志勇2,3,翟曼2,张治中2,3,张晟2,宋研2,骆健忠2,游明亮4,郭美锦1,张骁2,3
1华东理工大学生物反应器工程国家重点实验室,中国上海,200237
2中国科学院,广州生物医药与健康研究院,中国广州,510530
3生物岛实验室(广州再生医学与健康广东省实验室),中国广州,510320
4浙江大学医学院附属杭州市肿瘤医院,浙江省临床肿瘤药理与毒理学研究重点实验室,杭州市肿瘤研究所,中国杭州,310002
目的:人诱导多能干细胞来源的肾类器官是药物肾毒性研究中的理想模型。然而受限于传统检测方式的局限,迫切需要一种高通量、高分辨率和高精确性的方法来满足这一领域的基础和转化研究需求。
创新点:该系统采用了最新的硬件和软件技术,并采用了新颖的自动对焦控制机制,满足了高速和亚微米分辨率的要求;该方法结合了无标签和非侵入性的检测算法;该方法在满足检测过程中培养板振动的最小化的同时能够最大化时间分辨率;该方法能够满足大规模化合物筛选的需求。
方法:我们开发了一种连续成像和检测识别的系统,对培养在多孔板内悬浮培养基中顺铂诱导的肾类器官进行检测识别。通过基于机器学习的分类和分割算法对获得的图像进行处理,并通过传统的生物学分析手段验证该系统检测识别的准确性。
结论:运用自主研发的"扫板时读取"系统,我们可以在3分钟内对一个多孔板内的肾类器官进行1.1 µm分辨率的成像,并结合无标签的检测识别算法,准确率可以达到95%。该系统允许我们使用全自动的方法对肾类器官进行批量的化合物筛选。

关键词:肾类器官;高通量显微成像;肾毒性;机器学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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