In Flow Cytometry, Image Is Everything (2019)

With the combined advantages of optical microscopy and flow cytometry, imaging flow cytometry (IFC) has quickly become an established tool for performing cytometric analysis in diverse areas of biology including microbiology, immunology, and stem cell biology1-14. IFC provides quantitative, spatially registered image data for every event analyzed, which allows the morphometric characterization of single cells in large heterogeneous populations, further advancing our understanding of cell-to-cell differences in physiological settings. The availability of image data produced by IFC is well aligned with the pressing need for progressively larger biomedical datasets for efficient and accurate data analysis with the help of computational tools (e.g., compressive imaging, data mining, machine/deep learning) to make better decisions in biomedical research and clinical settings. Recent studies 4, 5, 10, 12 show that IFC is highly effective for the characterization of DNA damage and repair, the accurate assessment of cell death and autophagy, fluorescence in situ hybridization (FISH), the localization and enumeration of specific molecules such as proteins, nucleic acids, and peptides, and the analysis of cell–cell interaction and cell cycle. Furthermore, a number of potential clinical applications of IFC are conceivable, such as liquid biopsy, leukemia detection, and identification of infectious diseases in which the availability of high-content single-cell images plays an important role. Building on the excellent capabilities of IFC, the objective of this Special Issue is to provide a valuable forum where scientists, engineers, and users in the field share their most recent findings and understanding of basic principles, advanced techniques, and applications of IFC. Topics covered can be broadly categorized into (1) advanced imaging methods for IFC, (2) biological and clinical applications of IFC, and (3) computational techniques for IFC. Specifically, the first part of the Special Issue consists of nine papers (while the second part follows in a subsequent issue). Dey et al. 15 report the use of IFC to accurately differentiate between active and viable but not-culturable forms of Psedomonas aeruginosa (a Gram-negative bacterium abundant in the environment and water systems). Kim et al. 16 present their investigation of the physical and magnetic properties of monocytes in human blood together with plasma platelets, oxyhemoglobin red blood cells, and methemoglobin red blood cells by using IFC. Lelliott et al. 17 describe their development of an automated analysis method to rapidly evaluate cells that form neutrophil extracellular traps or precursors to neutrophil extracellular traps and differentiate them from cells that undergo other forms of cell death. Zhang et al. 18 report their development of a computational method for intelligent de-blurring of out-of-focus cell images in IFC by deep learning. Gu et al. 19 present their demonstration of image-guided cell sorting together with a cell classification system and real-time isolation of single cells based on nuclear localization of glucocorticoid receptor, particle binding to the cell membrane, and DNA damage induced γ-H2AX foci using the instrument. Goktas et al. 20 report a combination of IFC and angle-resolved light scattering measurements to study the morphological features of red blood cells in sickle cell patients. Hui et al. 21 describe their development of a method, which they refer to as immuno-flowFISH, for conducting FISH on immunophenotyped whole cells in suspension to detect chromosomal abnormalities in chronic lymphocytic leukemia. Lee et al. 22 report their development of a quantitative phase IFC system to conduct large-scale biophysical phenotyping of numerous single cells (>1,000,000 cells) in a label-free manner. Yaakov et al. 23 report their quantitative characterization of the kinetics of Mimivirus infection stages by IFC. The field of IFC dates back to 1979 when Kay et al. 1 and Kachel et al. 2 first introduced and experimentally demonstrated imaging in flow. Since then, the field of IFC has slowly gained interest from the research community over the past two decades (Fig. 1). Before the year 2002, only a handful of researchers with interdisciplinary expertise in optics (in particular, optical imaging), fluidics, electronics, and cell biology were capable of building IFC-capable systems and conducting experiments while many other researchers could appreciate the great potential of the technology. This situation dramatically changed around 2008 when the Amnis Corporation (now Luminex Corporation) successfully launched the first commercial IFC system. This has helped to democratize IFC from only a few specialist research groups at academic institutions to place IFC in the hands of many researchers worldwide, as shown by the sharp rise in the number of publications in Figure 1. As of today, IFC systems have been installed in research laboratories and flow cytometry core facilities at a number of universities and other research institutions to tackle problems unanswered by conventional (non-imaging) flow cytometry. The field of IFC is expected to grow further in the next decade or so (Fig. 2). There are several driving factors that push the frontier of IFC and expand its utility in diverse areas of biology and medicine. The first driving factor is the emergence of advanced microfluidics and its integration into IFC platforms 7, 8, 12-14. Microfluidics provides higher degrees of freedom in multiplexing, automation, and parallelization than traditional capillary-based fluidics in flow cytometry, thereby adding more functionalities for preparation, analysis, and manipulation of cells. The second driving factor is the advent of various optical imaging modalities other than traditional image acquisition based on CCD image sensors with time-delay integration 10. They include time-stretch imaging 8, 13, frequency-division-multiplexed imaging 9, 24, spatial–temporal transformation 14, temporally coded excitation 25, and stroboscopic fluorescence imaging 6, which are designed to handle sensitive image acquisition of fast-flowing cells. The third driving factor is the availability of sophisticated digital image processing techniques, such as compressive imaging, data mining, and deep learning, powered by the rapidly evolving smartphone industry 4, 5, 7, 18, 19. The big data brought by IFC systems acts as a fuel for artificial neural networks that can characterize and classify cells with higher accuracy than classical image analysis algorithms. The fourth driving factor is the advent of image-activated cell sorting (IACS) (also called image-guided cell sorting) 7, 19, a technology that achieves the best of both worlds between IFC and fluorescence-activated cell sorting (FACS). IACS extends the capabilities of FACS from one-dimensional signal intensities to multidimensional information-rich images, which can be used to analyze the spatial architecture of single cells. IACS offers the opportunity to study and exploit sorted cells via single-cell analysis tools such as genomics, proteomics, and proteomics. With these driving factors, the number of researchers, applications, discoveries, and publications in the field of IFC is expected to go up in the next decade or so (Fig. 2). The future of IFC is bright.