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文献精选 > 近年高分"类器官"研究（IF≥30，近5年）

Automated high-speed 3D imaging of organoid cultures with multi-scale phenotypic quantification. ​

文献信息 ​

DOI10.1038/s41592-022-01508-0
PMID35697835
期刊Nature methods
影响因子32.1
JCR 分区Q1
发表年份2022
被引次数50
关键词自动化, 三维成像, 类器官, 多尺度表征, 深度学习
文献类型Journal Article, Research Support, Non-U.S. Gov't
ISSN1548-7091
页码881-892
期号19(7)
作者Anne Beghin, Gianluca Grenci, Geetika Sahni, Su Guo, Harini Rajendiran, Tom Delaire, Saburnisha Binte Mohamad Raffi, Damien Blanc, Richard de Mets, Hui Ting Ong, Xareni Galindo, Anais Monet, Vidhyalakshmi Acharya, Victor Racine, Florian Levet, Remi Galland, Jean-Baptiste Sibarita, Virgile Viasnoff

一句话小结 ​

本研究提出了一种自动化的多尺度3D成像平台，结合了高密度类器官培养与快速实时成像，显著提升了对三维器官样本的表征能力。该平台能够有效简化类器官的培养和成像过程，且通过深度学习算法分析大量数据，实现了对类器官形态发生的多尺度量化，具有广泛的应用潜力。

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自动化 · 三维成像 · 类器官 · 多尺度表征 · 深度学习

摘要 ​

当前的成像方法限制了对大规模三维（3D）器官样本（类器官）进行多尺度表征的能力。在此，我们提出了一种自动化的多尺度3D成像平台，结合了高密度类器官培养与快速的实时3D单透镜光片成像。该平台由可一次性使用的微加工类器官培养芯片（称为JeWells）组成，这些芯片内嵌光学元件，并配备激光束引导单元，连接至一台商业化的倒置显微镜。它使得在一台用户友好的仪器上简化类器官培养和高含量3D成像成为可能，所需操作最小，每小时可处理300个类器官。我们证明，通过我们的平台收集的大量3D堆栈能够训练基于深度学习的算法，从而量化类器官在多尺度下的形态发生组织，从亚细胞尺度到整个类器官水平。我们在肠道、肝脏、神经外胚层类器官及肿瘤球体上验证了我们方法的多功能性和稳健性。

英文摘要 ​

Current imaging approaches limit the ability to perform multi-scale characterization of three-dimensional (3D) organotypic cultures (organoids) in large numbers. Here, we present an automated multi-scale 3D imaging platform synergizing high-density organoid cultures with rapid and live 3D single-objective light-sheet imaging. It is composed of disposable microfabricated organoid culture chips, termed JeWells, with embedded optical components and a laser beam-steering unit coupled to a commercial inverted microscope. It permits streamlining organoid culture and high-content 3D imaging on a single user-friendly instrument with minimal manipulations and a throughput of 300 organoids per hour. We demonstrate that the large number of 3D stacks that can be collected via our platform allows training deep learning-based algorithms to quantify morphogenetic organizations of organoids at multi-scales, ranging from the subcellular scale to the whole organoid level. We validated the versatility and robustness of our approach on intestine, hepatic, neuroectoderm organoids and oncospheres.

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主要研究问题 ​

  1. 在多尺度表型量化方面，如何确保深度学习算法的准确性和可靠性？
  2. 除了肠道和肝脏类器官外，是否有其他类型的类器官可以应用这种高速度3D成像技术？
  3. 该平台在处理大规模样本时的效率如何影响最终的实验结果和数据分析？
  4. 使用这种自动化成像平台时，是否存在对样本质量或处理方式的特定要求？
  5. 在未来的研究中，如何将该技术与其他生物技术相结合以进一步提高类器官研究的深度和广度？

核心洞察 ​

1. 研究背景和目的 ​

在生物医学研究中，三维（3D）类器官培养物的表征是理解细胞行为和组织发育的重要手段。然而，现有的成像技术通常无法高效地对这些复杂的3D结构进行多尺度表征，限制了其在大规模研究中的应用。因此，本研究的目的是开发一种自动化的高速度3D成像平台，以便于对大规模类器官培养物进行快速、准确的多尺度表征。

2. 主要方法和发现 ​

研究团队提出了一种自动化的多尺度3D成像平台，该平台结合了高密度类器官培养和快速的实时3D单目标光片成像技术。该系统包含一种名为JeWells的微制造类器官培养芯片，芯片内嵌光学元件，并配备激光束转向单元与商业反相显微镜相连接。通过这一创新平台，研究人员能够在用户友好的设备上以每小时300个类器官的速度进行高内容3D成像，显著提高了数据采集的效率。此外，平台收集的大量3D图像堆栈使得训练基于深度学习的算法成为可能，从而实现了对类器官的亚细胞尺度到整个类器官水平的形态发生组织的量化。

3. 核心结论 ​

本研究成功开发了一种高效的自动化3D成像平台，能够快速、准确地对类器官进行多尺度的表征。通过该平台，研究人员能够在不同类型的类器官（如肠道、肝脏、神经外胚层类器官及肿瘤球体）中验证其通用性和稳健性。这一方法不仅提高了研究效率，还有助于深入理解类器官的发育和功能，推动了生物医学研究的进展。

4. 研究意义和影响 ​

该研究的意义在于突破了传统成像技术的限制，实现了对大规模类器官的高效、高度自动化的观察和分析。这一平台的开发为药物筛选、疾病模型研究及组织工程等领域提供了新的工具和方法，具有广泛的应用前景。同时，基于深度学习的图像分析方法的引入，为未来的生物医学研究提供了新的思路，可能推动精准医学的发展。

参考文献 ​

  1. Human organoids: model systems for human biology and medicine. - Jihoon Kim;Bon-Kyoung Koo;Juergen A Knoblich - Nature reviews. Molecular cell biology (2020)
  2. The challenge of developing human 3D organoids into medicines. - Joaquim Vives;Laura Batlle-Morera - Stem cell research & therapy (2020)
  3. The potential and challenges of patient-derived organoids in guiding the multimodality treatment of upper gastrointestinal malignancies. - Georg A Busslinger;Fianne Lissendorp;Ingrid A Franken;Richard van Hillegersberg;Jelle P Ruurda;Hans Clevers;Michiel F G de Maat - Open biology (2020)
  4. Phenotypic landscape of intestinal organoid regeneration. - Ilya Lukonin;Denise Serra;Ludivine Challet Meylan;Katrin Volkmann;Janine Baaten;Rui Zhao;Shelly Meeusen;Karyn Colman;Francisca Maurer;Michael B Stadler;Jeremy Jenkins;Prisca Liberali - Nature (2020)
  5. A fully automated high-throughput workflow for 3D-based chemical screening in human midbrain organoids. - Henrik Renner;Martha Grabos;Katharina J Becker;Theresa E Kagermeier;Jie Wu;Mandy Otto;Stefan Peischard;Dagmar Zeuschner;Yaroslav TsyTsyura;Paul Disse;Jürgen Klingauf;Sebastian A Leidel;Guiscard Seebohm;Hans R Schöler;Jan M Bruder - eLife (2020)
  6. The Organoid Cell Atlas. - Christoph Bock;Michael Boutros;J Gray Camp;Laura Clarke;Hans Clevers;Juergen A Knoblich;Prisca Liberali;Aviv Regev;Anne C Rios;Oliver Stegle;Hendrik G Stunnenberg;Sarah A Teichmann;Barbara Treutlein;Robert G J Vries; - Nature biotechnology (2021)
  7. Imaging organoids: a bright future ahead. - Anne C Rios;Hans Clevers - Nature methods (2018)
  8. Organoids in image-based phenotypic chemical screens. - Ilya Lukonin;Marietta Zinner;Prisca Liberali - Experimental & molecular medicine (2021)
  9. Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes. - Yinan Wan;Katie McDole;Philipp J Keller - Annual review of cell and developmental biology (2019)
  10. A single-cell transcriptomic landscape of the lungs of patients with COVID-19. - Si Wang;Xiaohong Yao;Shuai Ma;Yifang Ping;Yanling Fan;Shuhui Sun;Zhicheng He;Yu Shi;Liang Sun;Shiqi Xiao;Moshi Song;Jun Cai;Jiaming Li;Rui Tang;Liyun Zhao;Chaofu Wang;Qiaoran Wang;Lei Zhao;Huifang Hu;Xindong Liu;Guoqiang Sun;Lu Chen;Guoqing Pan;Huaiyong Chen;Qingrui Li;Peipei Zhang;Yuanyuan Xu;Huyi Feng;Guo-Guang Zhao;Tianzi Wen;Yungui Yang;Xuequan Huang;Wei Li;Zhenhua Liu;Hongmei Wang;Haibo Wu;Baoyang Hu;Yong Ren;Qi Zhou;Jing Qu;Weiqi Zhang;Guang-Hui Liu;Xiu-Wu Bian - Nature cell biology (2021)

引用本文的文献 ​

  1. Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics. - Peter R Corridon;Xinyu Wang;Adeeba Shakeel;Vincent Chan - Biomedicines (2022)
  2. Applications and Advances of Multicellular Tumor Spheroids: Challenges in Their Development and Analysis. - Achilleas G Mitrakas;Avgi Tsolou;Stylianos Didaskalou;Lito Karkaletsou;Christos Efstathiou;Evgenios Eftalitsidis;Konstantinos Marmanis;Maria Koffa - International journal of molecular sciences (2023)
  3. Middle-out methods for spatiotemporal tissue engineering of organoids. - Michael R Blatchley;Kristi S Anseth - Nature reviews bioengineering (2023)
  4. In-silico and in-vitro morphometric analysis of intestinal organoids. - Sandra Montes-Olivas;Danny Legge;Abbie Lund;Alexander G Fletcher;Ann C Williams;Lucia Marucci;Martin Homer - PLoS computational biology (2023)
  5. A Strainer-Based Platform for the Collection and Immunolabeling of Mouse Intestinal Organoids. - Jinlong Tan;Yinju Liu;Weike Li;Guohua Chen;Yongxiang Fang;Xiaobing He;Baoquan Fu;Zhizhong Jing - International journal of molecular sciences (2023)
  6. A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids. - Alexandra Sockell;Wing Wong;Scott Longwell;Thy Vu;Kasper Karlsson;Daniel Mokhtari;Julia Schaepe;Yuan-Hung Lo;Vincent Cornelius;Calvin Kuo;David Van Valen;Christina Curtis;Polly M Fordyce - Cell systems (2023)
  7. Cerebral Organoid Arrays for Batch Phenotypic Analysis in Sections and Three Dimensions. - Juan Chen;Haihua Ma;Zhiyu Deng;Qingming Luo;Hui Gong;Ben Long;Xiangning Li - International journal of molecular sciences (2023)
  8. Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet. - Nahima Saliba;Gabriella Gagliano;Anna-Karin Gustavsson - bioRxiv : the preprint server for biology (2024)
  9. Lung cancer organoids: models for preclinical research and precision medicine. - Yajing Liu;Yanbing Zhou;Pu Chen - Frontiers in oncology (2023)
  10. Automated, High-Throughput Phenotypic Screening and Analysis Platform to Study Pre- and Post-Implantation Morphogenesis in Stem Cell-Derived Embryo-Like Structures. - Vinidhra Shankar;Clemens van Blitterswijk;Erik Vrij;Stefan Giselbrecht - Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)

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