Visible Light Optical Coherence Tomography of Peripapillary Retinal Nerve Fiber Layer Reflectivity in Glaucoma.

Purpose: To evaluate the clinical utility of visible light optical coherence tomography (VIS-OCT) and to test whether VIS-OCT reflectivity and spectroscopy of peripapillary retinal nerve fiber layer (pRNFL) are correlated with severity of glaucoma, compared with standard-of-care OCT thickness measurements. Methods: In total 54 eyes (20 normal, 17 suspect/preperimetric glaucoma [GS/PPG], 17 perimetric glaucoma [PG]) were successfully imaged with complete datasets. All the eyes were scanned by a custom-designed dual-channel device that simultaneously acquired VIS-OCT and near-infrared OCT (NIR-OCT) images. A 5 × 5 mm2 scan was taken of the pRNFL. The pRNFL reflectivity was calculated for both channels and the spectroscopic marker was quantified by pVN, defined as the ratio of VIS-OCT to NIR-OCT pRNFL reflectivity. The results were compared with ophthalmic examinations and Zeiss Cirrus OCT. Results: VIS-OCT pRNFL reflectivity significantly, sequentially decreased from normal to GS/PPG to PG, as did NIR-OCT pRNFL reflectivity. The pVN had the same decreasing trend among three groups. Normal and GS/PPG eyes were significantly different in VIS-OCT pRNFL reflectivity (P = 0.002) and pVN (P < 0.001), but were not in NIR-OCT pRNFL reflectivity (P = 0.14), circumpapillary RNFL thickness (P = 0.17), or macular ganglion cell layer and inner plexiform layer thickness (P = 0.07) in a mixed linear regression model. Conclusions: VIS-OCT pRNFL reflectivity and pVN better distinguished GS/PPG from normal eyes than Cirrus OCT thickness measurements. Translational Relevance: VIS-OCT pRNFL reflectivity and pVN could be useful metrics in the early detection of glaucoma upon further longitudinal validation.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy.

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.