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It is not currently possible to reliably visualise and track immune cells in the human central nervous system or eye. Previous work demonstrated that indocyanine green (ICG) dye could label immune cells and be imaged after a delay during disease in the mouse retina. We report a pilot study investigating if ICG can similarly label immune cells within the human retina. Twelve adult participants receiving ICG angiography as part of routine standard of care were recruited. Baseline retinal images were obtained prior to ICG administration then repeated over a period ranging from 2 hours to 9 days. Matched peripheral blood samples were obtained to examine systemic immune cell labelling and activation from ICG by flow cytometry with human macrophage cultures as positive controls. Differences between the delayed near infrared ICG imaging and 488 nm autofluorescence was observed across pathologies, likely arising from the retinal pigment epithelium (RPE). Only one subject demonstrated ICG signal on peripheral blood myeloid cells and only three distinct cell-sized signals appeared over time within the retina of three participants. No significant increase in immune cell activation markers were detected after ICG administration. ICG accumulated in the endosomes of macrophage cultures and was detectable above a minimum concentration, suggesting cell labelling is possible. ICG can label RPE and may be used as an additional biomarker for RPE health across a range of retinal disorders. Standard clinical doses of intravenous ICG do not lead to robust immune cell labelling in human blood or retina and further optimisation in dose and route are required. [ABSTRACT FROM AUTHOR]

New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses. [ABSTRACT FROM AUTHOR]