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Imaging flow cytometry
Nature Reviews Methods Primers, Volume: 2, Issue: 1
Swansea University Authors: Paul Rees , Huw Summers
Accepted Manuscript under embargo until: 3rd May 2023
DOI (Published version): 10.1038/s43586-022-00167-x
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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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.
Faculty of Science and Engineering
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.