Imaging flow cytometry

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