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Imaging flow cytometry

Paul Rees Orcid Logo, Huw Summers Orcid Logo, Andrew Filby, Anne E. Carpenter Orcid Logo, Minh Doan

Nature Reviews Methods Primers, Volume: 2, Issue: 1

Swansea University Authors: Paul Rees Orcid Logo, Huw Summers Orcid Logo

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DOI (Published version): 10.1038/s43586-022-00167-x

Abstract

Imaging flow cytometry combines the high event rate nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel bio...

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Published in: Nature Reviews Methods Primers
ISSN: 2662-8449
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61960
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first_indexed 2022-11-21T08:52:33Z
last_indexed 2023-01-13T19:23:05Z
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spelling 2022-12-16T16:20:24.6278483 v2 61960 2022-11-21 Imaging flow cytometry 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 2022-11-21 MEDE Imaging flow cytometry combines the high event rate nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel biomedical applications. In this primer we discuss the typical imaging flow instrumentation, the form of data acquired and the typical analysis tools that can be applied to this data. Focusing on the first commercially available Imaging flow cytometer, the ImageStream (Luminex) we will use examples from the literature to discuss the progression of the analysis methods used in imaging flow cytometry. These methods start from the use of simple single image features and multiple channel gating strategies, followed by the design and use of custom features for phenotype classification, through to powerful machine and deep learning methods. For each of these methods, we outline the processes involved in analyzing typical datasets and provide details of example applications. Finally, we discuss the current limitations of imaging flow cytometry and the innovations and new instruments which are addressing these challenges. Journal Article Nature Reviews Methods Primers 2 1 Springer Science and Business Media LLC 2662-8449 3 11 2022 2022-11-03 10.1038/s43586-022-00167-x COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University P.R. and H.S. acknowledge the UK Engineering and Physical Sciences Research Council (EP/N013506/1) and UK Biotechnology and Biological Sciences Research Council (BB/P026818/1) for supporting this work. A.C. acknowledges the National Science Foundation (DBI 1458626) and the National Institutes of Health (R35 GM122547) for supporting this work. 2022-12-16T16:20:24.6278483 2022-11-21T08:47:33.1006768 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Paul Rees 0000-0002-7715-6914 1 Huw Summers 0000-0002-0898-5612 2 Andrew Filby 3 Anne E. Carpenter 0000-0003-1555-8261 4 Minh Doan 5 Under embargo Under embargo 2022-12-07T11:46:27.7624857 Output 348478 application/pdf Accepted Manuscript true 2023-05-03T00:00:00.0000000 true eng
title Imaging flow cytometry
spellingShingle Imaging flow cytometry
Paul Rees
Huw Summers
title_short Imaging flow cytometry
title_full Imaging flow cytometry
title_fullStr Imaging flow cytometry
title_full_unstemmed Imaging flow cytometry
title_sort Imaging flow cytometry
author_id_str_mv 537a2fe031a796a3bde99679ee8c24f5
a61c15e220837ebfa52648c143769427
author_id_fullname_str_mv 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
a61c15e220837ebfa52648c143769427_***_Huw Summers
author Paul Rees
Huw Summers
author2 Paul Rees
Huw Summers
Andrew Filby
Anne E. Carpenter
Minh Doan
format Journal article
container_title Nature Reviews Methods Primers
container_volume 2
container_issue 1
publishDate 2022
institution Swansea University
issn 2662-8449
doi_str_mv 10.1038/s43586-022-00167-x
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
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description Imaging flow cytometry combines the high event rate nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel biomedical applications. In this primer we discuss the typical imaging flow instrumentation, the form of data acquired and the typical analysis tools that can be applied to this data. Focusing on the first commercially available Imaging flow cytometer, the ImageStream (Luminex) we will use examples from the literature to discuss the progression of the analysis methods used in imaging flow cytometry. These methods start from the use of simple single image features and multiple channel gating strategies, followed by the design and use of custom features for phenotype classification, through to powerful machine and deep learning methods. For each of these methods, we outline the processes involved in analyzing typical datasets and provide details of example applications. Finally, we discuss the current limitations of imaging flow cytometry and the innovations and new instruments which are addressing these challenges.
published_date 2022-11-03T04:21:12Z
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score 11.013148

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