A practical guide to intelligent image-activated cell sorting,Nature Protocols

当前位置： X-MOL 学术 › Nat. Protoc. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A practical guide to intelligent image-activated cell sorting
Nature Protocols ( IF 13.1 ) Pub Date : 2019-07-05 , DOI: 10.1038/s41596-019-0183-1
Akihiro Isozaki ₁ , Hideharu Mikami ₁ , Kotaro Hiramatsu ₁ , Shinya Sakuma ₂ , Yusuke Kasai ₂ , Takanori Iino ₃ , Takashi Yamano ₄ , Atsushi Yasumoto ₅ , Yusuke Oguchi ₆ , Nobutake Suzuki ₆ , Yoshitaka Shirasaki ₆ , Taichiro Endo ₇ , Takuro Ito _{1,

8} , Kei Hiraki ₁ , Makoto Yamada ₉ , Satoshi Matsusaka ₁₀ , Takeshi Hayakawa ₁₁ , Hideya Fukuzawa ₄ , Yutaka Yatomi ₅ , Fumihito Arai ₂ , Dino Di Carlo _{1,

12,

13,

14} , Atsuhiro Nakagawa ₁₅ , Yu Hoshino ₁₆ , Yoichiroh Hosokawa ₁₇ , Sotaro Uemura ₆ , Takeaki Sugimura _{1,

8} , Yasuyuki Ozeki ₃ , Nao Nitta _{1,

8} , Keisuke Goda _{1,

8,

18}

Affiliation

Department of Chemistry, The University of Tokyo, Tokyo, Japan.
Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan.
Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan.
Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Department of Biological Sciences, The University of Tokyo, Tokyo, Japan.
ExaWizards Inc., Tokyo, Japan.
Japan Science and Technology Agency, Saitama, Japan.
Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan.
Department of Precision Mechanics, Chuo University, Tokyo, Japan.
Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA.
Department of Neurosurgery, Graduate School of Medicine, Tohoku University, Sendai, Japan.
Department of Chemical Engineering, Kyushu University, Fukuoka, Japan.
Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software–hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.

中文翻译：

智能图像激活细胞分选实用指南

智能图像激活细胞分选 (iIACS) 是一种机器智能技术，可对单个细胞进行实时智能图像分选，并具有高通量。iIACS 超越了荧光激活细胞分选 (FACS) 的能力，从细胞的荧光强度分布到多维图像，从而能够对具有独特空间化学和形态特征的细胞或细胞簇进行高内涵分选。因此，iIACS 通过实现群体水平分析（流式细胞术）、细胞水平分析（显微镜）和基因水平分析（测序）之间的直接联系，成为整体单细胞分析的一个组成部分。具体而言，iIACS 基于高通量细胞显微镜（例如，多色荧光成像、明场成像）、细胞聚焦、在混合软硬件数据管理基础设施上进行细胞分选和深度学习，实现数据采集、数据处理、智能决策和驱动的实时自动化操作。在这里，我们提供了 iIACS 实用指南，描述了如何设计、构建、表征和使用 iIACS 机器。该指南包括对几个重要设计参数的考虑，例如吞吐量、灵敏度、动态范围、图像质量、分类纯度和分类产量；光学、微流体、电气、计算和机械组件的开发和集成；以及集成系统的特性和实际用途。假设所有组件都是现成的，一个由多名研究人员组成的团队在光学、电子、数字信号处理、微流体、

更新日期：2019-11-18

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11