Evidence map›Paper›PMID 31278398›Full record

ArticleNature protocols2019

A practical guide to intelligent image-activated cell sorting.

Akihiro Isozaki, Hideharu Mikami, Kotaro Hiramatsu, Shinya Sakuma, Yusuke Kasai, Takanori Iino, Takashi Yamano, Atsushi Yasumoto, Yusuke Oguchi, Nobutake Suzuki and 19 more

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Review
  2. Review
  3. Highly Efficient Calibration-Free Color Compensation Algorithm for Imaging Flow Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025
    Article
  4. Optical sorting: past, present and future.Light, science & applications · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Artificial intelligence in imaging flow cytometry.Frontiers in bioinformatics · 2023
    Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

29 authors.

Akihiro IsozakiDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Hideharu MikamiDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Kotaro HiramatsuDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Shinya SakumaDepartment of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.ORCID http://orcid.org/0000-0002-4145-7264
Yusuke KasaiDepartment of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.ORCID http://orcid.org/0000-0003-0637-4575
Takanori IinoDepartment of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan.
Takashi YamanoLaboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan.
Atsushi YasumotoDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yusuke OguchiDepartment of Biological Sciences, The University of Tokyo, Tokyo, Japan.
Nobutake SuzukiDepartment of Biological Sciences, The University of Tokyo, Tokyo, Japan.
Yoshitaka ShirasakiDepartment of Biological Sciences, The University of Tokyo, Tokyo, Japan.
Taichiro EndoExaWizards Inc., Tokyo, Japan.
Takuro ItoDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Kei HirakiDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Makoto YamadaDepartment of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
Satoshi MatsusakaClinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan.
Takeshi HayakawaDepartment of Precision Mechanics, Chuo University, Tokyo, Japan.
Hideya FukuzawaLaboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan.
Yutaka YatomiDepartment of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Fumihito AraiDepartment of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan.
Dino Di CarloDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Atsuhiro NakagawaDepartment of Neurosurgery, Graduate School of Medicine, Tohoku University, Sendai, Japan.
Yu HoshinoDepartment of Chemical Engineering, Kyushu University, Fukuoka, Japan.ORCID http://orcid.org/0000-0001-9628-6979
Yoichiroh HosokawaDivision of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
Sotaro UemuraDepartment of Biological Sciences, The University of Tokyo, Tokyo, Japan.
Takeaki SugimuraDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.
Yasuyuki OzekiDepartment of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-9004-0799
Nao NittaDepartment of Chemistry, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-9093-9016
Keisuke GodaDepartment of Chemistry, The University of Tokyo, Tokyo, Japan. goda@chem.s.u-tokyo.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Neural Networks, ComputerCells, CulturedFlow CytometryHumansImage Processing, Computer-AssistedLab-On-A-Chip DevicesMicroalgaeSignal Processing, Computer-AssistedSingle-Cell AnalysisSoftware

Identifiers

PMID31278398

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.