Imaging Flow Cytometry as a Molecular Biology Tool: From Cell Morphology to Molecular Mechanisms

Insights into the state of individual cells within a living organism are essential for identifying diseases and abnormalities. The internal state of a cell is reflected in its morphological features and changes in the localization of intracellular molecules. Using this information, it is possible to infer the state of the cells with high precision. In recent years, technological advancements and improvements in instrument specifications have made large-scale analyses, such as single-cell analysis, more widely accessible. Among these technologies, imaging flow cytometry (IFC) is a high-throughput imaging platform that can simultaneously acquire information from flow cytometry (FCM) and cellular images. While conventional FCM can only obtain fluorescence intensity information corresponding to each detector, IFC can acquire multidimensional information, including cellular morphology and the spatial arrangement of proteins, nucleic acids, and organelles for each imaging channel. This enables the discrimination of cell types and states based on the localization of proteins and organelles, which is difficult to assess accurately using conventional FCM. Because IFC can acquire a large number of single-cell morphological images in a short time, it is well suited for automated classification using machine learning. Furthermore, commercial instruments that combine integrated imaging and cell sorting capabilities have recently become available, enabling the sorting of cells based on their image information. In this review, we specifically highlight practical applications of IFC in four representative areas: cell cycle analysis, protein localization analysis, immunological synapse formation, and the detection of leukemic cells. In addition, particular emphasis is placed on applications that directly contribute to elucidating molecular mechanisms, thereby distinguishing this review from previous general overviews of IFC. IFC enables the estimation of cell cycle phases from large numbers of acquired cellular images using machine learning, thereby allowing more precise cell cycle analysis. Moreover, IFC has been applied to investigate intracellular survival and differentiation signals triggered by external stimuli, to monitor DNA damage responses such as γH2AX foci formation, and more recently, to detect immune synapse formation among interacting cells within large populations and to analyze these interactions at the molecular level. In hematological malignancies, IFC combined with fluorescence in situ hybridization (FISH) enables high-throughput detection of chromosomal abnormalities, such as BCR-ABL1 translocations. These advances demonstrate that IFC provides not only morphological and functional insights but also clinically relevant genomic information at the single-cell level. By summarizing these unique applications, this review aims to complement existing publications and provide researchers with practical insights into how IFC can be implemented in both basic and translational research.