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A practical guide to intelligent image-activated cell sorting.
Isozaki, Akihiro; Mikami, Hideharu; Hiramatsu, Kotaro; Sakuma, Shinya; Kasai, Yusuke; Iino, Takanori; Yamano, Takashi; Yasumoto, Atsushi; Oguchi, Yusuke; Suzuki, Nobutake; Shirasaki, Yoshitaka; Endo, Taichiro; Ito, Takuro; Hiraki, Kei; Yamada, Makoto; Matsusaka, Satoshi; Hayakawa, Takeshi; Fukuzawa, Hideya; Yatomi, Yutaka; Arai, Fumihito; Di Carlo, Dino; Nakagawa, Atsuhiro; Hoshino, Yu; Hosokawa, Yoichiroh; Uemura, Sotaro; Sugimura, Takeaki; Ozeki, Yasuyuki; Nitta, Nao; Goda, Keisuke.
Afiliação
  • Isozaki A; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Mikami H; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Hiramatsu K; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Sakuma S; Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
  • Kasai Y; Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
  • Iino T; Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan.
  • Yamano T; Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan.
  • Yasumoto A; Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Oguchi Y; Department of Biological Sciences, The University of Tokyo, Tokyo, Japan.
  • Suzuki N; Department of Biological Sciences, The University of Tokyo, Tokyo, Japan.
  • Shirasaki Y; Department of Biological Sciences, The University of Tokyo, Tokyo, Japan.
  • Endo T; ExaWizards Inc., Tokyo, Japan.
  • Ito T; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Hiraki K; Japan Science and Technology Agency, Saitama, Japan.
  • Yamada M; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Matsusaka S; Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
  • Hayakawa T; Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan.
  • Fukuzawa H; Department of Precision Mechanics, Chuo University, Tokyo, Japan.
  • Yatomi Y; Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan.
  • Arai F; Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Di Carlo D; Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
  • Nakagawa A; Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Hoshino Y; Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
  • Hosokawa Y; Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
  • Uemura S; California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA.
  • Sugimura T; Department of Neurosurgery, Graduate School of Medicine, Tohoku University, Sendai, Japan.
  • Ozeki Y; Department of Chemical Engineering, Kyushu University, Fukuoka, Japan.
  • Nitta N; Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
  • Goda K; Department of Biological Sciences, The University of Tokyo, Tokyo, Japan.
Nat Protoc ; 14(8): 2370-2415, 2019 08.
Article em En | MEDLINE | ID: mdl-31278398
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação / Análise de Célula Única / Citometria de Fluxo Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação / Análise de Célula Única / Citometria de Fluxo Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article