Literature DB >> 31278398

A practical guide to intelligent image-activated cell sorting.

Akihiro Isozaki1, Hideharu Mikami1, Kotaro Hiramatsu1, Shinya Sakuma2, Yusuke Kasai2, Takanori Iino3, Takashi Yamano4, Atsushi Yasumoto5, Yusuke Oguchi6, Nobutake Suzuki6, Yoshitaka Shirasaki6, Taichiro Endo7, Takuro Ito1,8, Kei Hiraki1, Makoto Yamada9, Satoshi Matsusaka10, Takeshi Hayakawa11, Hideya Fukuzawa4, Yutaka Yatomi5, Fumihito Arai2, Dino Di Carlo1,12,13,14, Atsuhiro Nakagawa15, Yu Hoshino16, Yoichiroh Hosokawa17, Sotaro Uemura6, Takeaki Sugimura1,8, Yasuyuki Ozeki3, Nao Nitta1,8, Keisuke Goda18,19,20.   

Abstract

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.

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Year:  2019        PMID: 31278398     DOI: 10.1038/s41596-019-0183-1

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  17 in total

Review 1.  Chemical Analysis of Single Cells and Organelles.

Authors:  Keke Hu; Tho D K Nguyen; Stefania Rabasco; Pieter E Oomen; Andrew G Ewing
Journal:  Anal Chem       Date:  2020-12-07       Impact factor: 6.986

2.  Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry.

Authors:  Kotaro Hiramatsu; Koji Yamada; Matthew Lindley; Kengo Suzuki; Keisuke Goda
Journal:  Biomed Opt Express       Date:  2020-03-03       Impact factor: 3.732

Review 3.  Cell Separations and Sorting.

Authors:  Malgorzata A Witek; Ian M Freed; Steven A Soper
Journal:  Anal Chem       Date:  2019-12-20       Impact factor: 6.986

4.  What is the future of electrical impedance spectroscopy in flow cytometry?

Authors:  Furkan Gökçe; Paolo S Ravaynia; Mario M Modena; Andreas Hierlemann
Journal:  Biomicrofluidics       Date:  2021-12-06       Impact factor: 2.800

5.  Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation.

Authors:  Eric Gioe; Mohammed Raihan Uddin; Jong-Hoon Kim; Xiaolin Chen
Journal:  Micromachines (Basel)       Date:  2022-04-23       Impact factor: 3.523

6.  Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing.

Authors:  Samannoy Ghosh; Marshall V Johnson; Rajan Neupane; James Hardin; John Daniel Berrigan; Surya R Kalidindi; Yong Lin Kong
Journal:  Flex Print Electron       Date:  2022-03-01

Review 7.  Image-Based Live Cell Sorting.

Authors:  Cody A LaBelle; Angelo Massaro; Belén Cortés-Llanos; Christopher E Sims; Nancy L Allbritton
Journal:  Trends Biotechnol       Date:  2020-11-13       Impact factor: 21.942

8.  Raman image-activated cell sorting.

Authors:  Takanori Iino; Akihiro Isozaki; Mai Yamagishi; Yasutaka Kitahama; Shinya Sakuma; Nao Nitta; Yuta Suzuki; Hiroshi Tezuka; Minoru Oikawa; Fumihito Arai; Takuya Asai; Dinghuan Deng; Hideya Fukuzawa; Misa Hase; Tomohisa Hasunuma; Takeshi Hayakawa; Kei Hiraki; Kotaro Hiramatsu; Yu Hoshino; Mary Inaba; Yuki Inoue; Takuro Ito; Masataka Kajikawa; Hiroshi Karakawa; Yusuke Kasai; Yuichi Kato; Hirofumi Kobayashi; Cheng Lei; Satoshi Matsusaka; Hideharu Mikami; Atsuhiro Nakagawa; Keiji Numata; Tadataka Ota; Takeichiro Sekiya; Kiyotaka Shiba; Yoshitaka Shirasaki; Nobutake Suzuki; Shunji Tanaka; Shunnosuke Ueno; Hiroshi Watarai; Takashi Yamano; Masayuki Yazawa; Yusuke Yonamine; Dino Di Carlo; Yoichiroh Hosokawa; Sotaro Uemura; Takeaki Sugimura; Yasuyuki Ozeki; Keisuke Goda
Journal:  Nat Commun       Date:  2020-07-10       Impact factor: 14.919

Review 9.  Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry.

Authors:  Kaiser Pärnamets; Tamas Pardy; Ants Koel; Toomas Rang; Ott Scheler; Yannick Le Moullec; Fariha Afrin
Journal:  Micromachines (Basel)       Date:  2021-03-23       Impact factor: 2.891

10.  Rapid counting and spectral sorting of live coral larvae using large-particle flow cytometry.

Authors:  Carly J Randall; Justin E Speaks; Claire Lager; Mary Hagedorn; Lyndon Llewellyn; Rock Pulak; Julia Thompson; Line K Bay; David Mead; Andrew J Heyward; Andrew P Negri
Journal:  Sci Rep       Date:  2020-07-31       Impact factor: 4.996

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