JZUS - Journal of Zhejiang University SCIENCE

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

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

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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

Reference

[1]BorettoM,MaenhoudtN,LuoXL,et al.,2019.Patient-derived organoids from endometrial disease capture clinical heterogeneity and are amenable to drug screening.Nat Cell Biol,21(8):1041-1051.

[2]BracewellRN,1986.The Fourier Transform and its Applications.McGraw-hill,New York, USA.

[3]CarpenterMK,Frey-VasconcellsJ,RaoMS,2009.Developing safe therapies from human pluripotent stem cells.Nat Biotechnol,27(7):606-613.

[4]ChenCL,MahjoubfarA,TaiLC,et al.,2016.Deep learning in label-free cell classification.Sci Rep,6:21471.

[5]ChougradH,ZouakiH,AlheyaneO,2018.Deep convolutional neural networks for breast cancer screening.Comput Methods Programs Biomed,157:19-30.

[6]CiampiO,IaconeR,LongarettiL,et al.,2016.Generation of functional podocytes from human induced pluripotent stem cells.Stem Cell Res,17(1):‍130-139.

[7]DaviesJA,2015.Biological techniques: kidney tissue grown from induced stem cells.Nature,526(7574):512-513.

[8]DigbyJLM,VanichapolT,PrzepiorskiA,et al.,2020.Evaluation of cisplatin-induced injury in human kidney organoids.Am J Physiol Renal Physiol,318(4):F971-F978.

[9]DriehuisE,KretzschmarK,CleversH,2020.Establishment of patient-derived cancer organoids for drug-screening applications.Nat Protoc,15(10):3380-3409.

[10]EbertAD,YuJY,RoseFF,et al.,2009.Induced pluripotent stem cells from a spinal muscular atrophy patient.Nature,457(7227):277-280.

[11]FanK,ZhangS,ZhangY,et al.,2017.A machine learning assisted, label-free, non-invasive approach for somatic reprogramming in induced pluripotent stem cell colony formation detection and prediction.Sci Rep,7:13496.

[12]HinchcliffeEH,2005.Using long-term time-lapse imaging of mammalian cell cycle progression for laboratory instruction and analysis.Cell Biol Educ,4(4):284-290.

[13]HosmerDW,LemesbowS,1980.Goodness of fit tests for the multiple logistic regression model.Commun Stat Theory Methods,9(10):‍1043-1069.

[14]IchimuraH,ShibaY,2017.Recent progress using pluripotent stem cells for cardiac regenerative therapy.Circ J,81(7):929-935.

[15]ImigJD,RyanMJ,2013.Immune and inflammatory role in renal disease.Compr Physiol,3(2):957-976.

[16]JansenJ,SchophuizenCMS,WilmerMJ,et al.,2014.A morphological and functional comparison of proximal tubule cell lines established from human urine and kidney tissue.Exp Cell Res,323(1):87-99.

[17]JonesSA,ShimSH,HeJ,et al.,2011.Fast, three-dimensional super-resolution imaging of live cells.Nat Methods,8(6):499-505.

[18]KoningM,van den BergCW,RabelinkTJ,2020.Stem cell-derived kidney organoids: engineering the vasculature.Cell Mol Life Sci,77(12):2257-2273.

[19]KrizhevskyA,SutskeverI,HintonGE,2012.ImageNet classification with deep convolutional neural networks.Proceedings of the 25th International Conference on Neural Information Processing Systems,Lake Tahoe, p.‍1097-1105.

[20]LeCun Y, Bengio Y,1995.Convolutional networks for images,speech, andtime-series. In: Arbib MA (Ed.), The Handbook of Brain Theory and Neural Networks.MIT Press,Cambridge, p.‍1995.

[21]LiyanageT,NinomiyaT,JhaV,et al.,2015.Worldwide access to treatment for end-stage kidney disease: a systematic review.Lancet,385(9981):1975-1982.

[22]LuJ,FanWH,HuangZH,et al.,2022.Automatic system for high-throughput and high-sensitivity diagnosis of SARS-CoV-2.Bioprocess Biosyst Eng,45(3):503-514.

[23]PrzepiorskiA,SanderV,TranT,et al.,2018.A simple bioreactor-based method to generate kidney organoids from pluripotent stem cells.Stem Cell Reports,11(2):470-484.

[24]QianXY,NguyenHN,SongMM,et al.,2016.Brain-region-specific organoids using mini-bioreactors for modeling ZIKV exposure.Cell,165(5):1238-1254.

[25]RonnebergerO,FischerP,BroxT,2015.U-Net: convolutional networks for biomedical image segmentation.International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer,Cham, p.234-241.

[26]SchmuckMR,TemmeT,DachK,et al.,2017.Omnisphero: a high-content image analysis (HCA) approach for phenotypic developmental neurotoxicity (DNT) screenings of organoid neurosphere cultures in vitro.Arch Toxicol,91(4):2017-2028.

[27]ShiYH,InoueH,WuJC,et al.,2017.Induced pluripotent stem cell technology: a decade of progress.Nat Rev Drug Discov,16(2):‍115-130.

[28]SirenkoO,MitloT,HesleyJ,et al.,2015.High-content assays for characterizing the viability and morphology of 3D cancer spheroid cultures.Assay Drug Dev Technol,13(7):402-414.

[29]StephensDJ,AllanVJ,2003.Light microscopy techniques for live cell imaging.Science,300(5616):82-86.

[30]SuR,XiongSJ,ZinkD,et al.,2016.High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures.Arch Toxicol,90(11):‍2793-2808.

[31]SunW,ZhangS,ZhouTC,et al.,2020.Human urinal cell reprogramming: synthetic 3D peptide hydrogels enhance induced pluripotent stem cell population homogeneity.ACS Biomater Sci Eng,6(11):6263-6275.

[32]TasnimF,DengRS,HuM,et al.,2010.Achievements and challenges in bioartificial kidney development.Fibrogenesis Tissue Repair,3:14.

[33]TiongHY,HuangP,XiongSJ,et al.,2014.Drug-induced nephrotoxicity: clinical impact and preclinicalin vitro models.Mol Pharm,11(7):1933-1948.

[34]WangQZ,XiongYC,ZhangS,et al.,2021.The dynamics of metabolic characterization in iPSC-derived kidney organoid differentiationvia a comparative omics approach.Front Genet,12:632810.

[35]WijnenB,PetersenEE,HuntEJ,et al.,2016.Free and open-source automated 3-D microscope.J Microsc,264(2):238-246.

[36]Zuiderveld K,1994.VIII.‍5.‍—Contrast limited adaptive histogram equalization. In: Heckbert PS (Ed.), Graphics Gems IV.Academic Press,Boston, p.474-485.

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