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Imaging flow cytometry

Nature Reviews Methods Primers volume 2, Article number: 86 (2022) Cite this article

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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 data sets that 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 these data. Focusing on the first commercially available imaging flow cytometer, the ImageStream (Luminex), we 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 analysing typical data sets and provide details of example applications. Finally, we discuss the current limitations of imaging flow cytometry and the innovations and new instruments that are addressing these challenges.

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Fig. 1: Overview of imaging flow cytometry and examples of images generated.
Fig. 2: Process flow used to select in-focus, single-cell images from an acquired event set.
Fig. 3: Data analysis based on spatial information.
Fig. 4: Spatial analysis based on area masking.
Fig. 5: Spatial analysis based on morphology.

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Acknowledgements

P.R. and H.D.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.E.C. acknowledges the National Science Foundation (DBI 1458626) and the NIH (R35 GM122547) for supporting this work.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Swansea University, Swansea, UK

    Paul Rees & Huw D. Summers

  2. Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Paul Rees & Anne E. Carpenter

  3. Flow Cytometry Core Facility and Innovation, Methodology and Application Research Theme, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

    Andrew Filby

  4. Bioimaging Analytics, GlaxoSmithKline, Collegeville, PA, USA

    Minh Doan

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Contributions

Introduction (H.D.S., A.E.C., M.D. and P.R.); Experimentation (H.D.S., A.E.C. and P.R.); Results (H.D.S., A.F., A.E.C. and P.R.); Applications (H.D.S., A.F., A.E.C. and P.R.); Reproducibility and data deposition (H.D.S., A.F., A.E.C. and P.R.); Limitations and optimizations (H.D.S., A.E.C., M.D. and P.R.); Outlook (H.D.S., A.E.C., M.D. and P.R.).

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Correspondence to Paul Rees.

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Nature Reviews Method Primers thanks Maik Herbig and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

FigShare: https://figshare.com/

FlowRepository: https://flowrepository.org/

GitHub: https://github.com/

Luminex: https://www.luminexcorp.com/eu/imaging-flow-cytometry/

Supplementary information

Glossary

Brightfield

The simplest form of microscopy, in which the image is formed by white light that is transmitted through the sample and then captured on a detector.

Darkfield

In the context of imaging flow cytometry, the darkfield image is formed when light scattered from the cell is collected on the detector perpendicular to the excitation direction.

Gating

A range of bins for the histogram or a polygon for the scatter plot. This process selects cells for further analysis. The gating process can be repeated to define phenotypes that require more than two markers for identification.

Multi-spectral images

An image data set in which the same field of view is imaged in different spectral bands.

Masks

A mask is a binary image that defines the extent of the object in an image; the pixel values in the image are 1 inside the object perimeter and 0 elsewhere to represent the background.

Raw maximum pixel

A feature in the ImageStream Data Exploration and Analysis Software that returns the maximum pixel value in an image acquired by the detector before any compensation. This is often used to set the laser excitation intensity to ensure that the pixel values are not saturated.

Building block

Suggested feature scatter plots and gating strategies to help the user with simple analysis and preprocessing tasks, such as determining in-focus cells in the ImageStream Data Exploration and Analysis software.

Aspect ratio

The ratio of the minor axis and the major axis. The major axis is the longest line that can be drawn through the shape, and the minor axis is the shortest line that can be drawn through the shape at right angles to the major axis.

AND mask operation

The AND operator applied to two masks delivers the overlapped shared area between the masks.

NOT mask operation

The NOT operator is a logic operator that delivers the inverse of a mask, that is, 0s become 1s.

Confusion matrix

A confusion matrix is used to compare the predicted outcome of a machine-learning algorithm with the known classes of the data. Diagonal elements represent the number of correct classifications. Off-diagonal elements can be used to assess misclassifications.

t-Distributed stochastic neighbour embedding

An algorithm used to visualize high-dimensional data sets in two or three dimensions. Nonlinear dimensional reduction of the data to the 2D–3D coordinate system is used to preserve the distances between similar and dissimilar data points.

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Rees, P., Summers, H.D., Filby, A. et al. Imaging flow cytometry. Nat Rev Methods Primers 2, 86 (2022). https://doi.org/10.1038/s43586-022-00167-x

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