ArticleNature reviews. Methods primers2022
Imaging flow cytometry: a primer.
Article in Nature reviews. Methods primers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
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- Malignant Transformation of Human Uroepithelial Cells Induced by Long-Term Exposure to a Disinfection Byproduct2,6-Dichlorobenzoquinone.Environment & health (Washington, D.C.) · 2025Article
- Single-pixel imaging flow cytometry for biomedical research.Inflammation and regeneration · 2025Review
- Three-dimensional forward-scattering imaging flow cytometry system for single-cell analysis.APL photonics · 2025Article
- Integrative Biosensing Nanoplasmonic Array for Real-Time Spatiotemporal Imaging of Protein Secretion in Cell-to-Cell Communication.Small science · 2025Article
- Real-time, high-throughput super-resolution microscopy via panoramic integration.Nature communications · 2025Article
- Application of process analytical technology for real-time monitoring of synthetic co-culture bioprocesses.Analytical and bioanalytical chemistry · 2025Review
- Toxicity of Magnetic Nanoparticles in Medicine: Contributing Factors and Modern Assessment Methods.International journal of molecular sciences · 2025Review
9 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
Imaging flow cytometry combines the high throughput 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. Using examples from the literature we discuss the progression of the analysis methods that have been applied to imaging flow cytometry data. 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 which are addressing these challenges.
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Registered trials
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 OpenQuestion graph.