Journal article 654 views 70 downloads
Imaging flow cytometry
Nature Reviews Methods Primers, Volume: 2, Issue: 1
Swansea University Authors: Paul Rees , Huw Summers
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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...
Published in: | Nature Reviews Methods Primers |
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ISSN: | 2662-8449 |
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Springer Science and Business Media LLC
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa61960 |
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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 EAAS 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 Engineering and Applied Sciences School COLLEGE CODE EAAS 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. 2024-07-10T12:15:06.8230415 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 61960__26026__67c7ca58ae0840ddb4cf90c04a08e8b4.pdf 61960.pdf 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 |
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537a2fe031a796a3bde99679ee8c24f5 a61c15e220837ebfa52648c143769427 |
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537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees a61c15e220837ebfa52648c143769427_***_Huw Summers |
author |
Paul Rees Huw Summers |
author2 |
Paul Rees Huw Summers Andrew Filby Anne E. Carpenter Minh Doan |
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Nature Reviews Methods Primers |
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2022 |
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Swansea University |
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10.1038/s43586-022-00167-x |
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Springer Science and Business Media LLC |
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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 |
2022-11-03T12:15:06Z |
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1804190462807900160 |
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11.031242 |