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.

LED array microscopy is an emerging platform for computational imaging with significant utility for biological imaging. Existing LED array systems often exploit transmission imaging geometries of standard brightfield microscopes that leave the rich backscattered field undetected. This backscattered signal contains high-resolution sample information with superb sensitivity to subtle structural features that make it ideal for biological sensing and detection. Here, we develop an LED array reflectance microscope capturing the sample's backscattered signal. In particular, we demonstrate multimodal brightfield, darkfield, and differential phase contrast imaging on fixed and living biological specimens including Caenorhabditis elegans (C. elegans), zebrafish embryos, and live cell cultures. Video-rate multi-modal imaging at 20 Hz records real time features of freely moving C. elegans and the fast beating heart of zebrafish embryos. Our new reflectance mode is a valuable addition to the LED array microscopic toolbox.

The retina, as part of the central nervous system, has distinct anatomical and structural properties for its visual function. Light scattering spectroscopy, while widely used for tissue structural characterization and disease diagnosis, has been relatively unexplored in the living retina. Recently, we have developed a fiber-based visible and near-infrared optical coherence tomography system (vnOCT) for in vivo retinal imaging, to uniquely measure a spectroscopic marker (VN ratio) sensitive to nanoscale pathological changes. In the present study, we applied vnOCT in an animal model of glaucoma (dexamethasone-induced ocular hypertension mouse) and tested the capabilities of four optical markers, VN ratio, peripapillary retinal nerve fiber layer (RNFL) thickness, total retinal blood flow, and hemoglobin oxygen saturation (sO 2), for the detection of retinal ganglion cell (RGC) damage in association with ocular hypertension. We found that RNFL-RGC VN ratio and arteriovenous (A-V) sO 2 are capable of detecting early retinal alteration in ocular hypertensive eyes, preceding measurable change of RNFL thickness. This study suggests a potential clinical application of vnOCT in early detection of glaucoma.

Traditional imaging cytometry uses fluorescence markers to identify specific structures, but is limited in throughput by the labeling process. Here we develop a label-free technique that alleviates the physical staining and provides highly multiplexed readout 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 highly accurate subcellular features after training on immunofluorescence images. We demonstrate up to 3× 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 readout enables accurate multi-parametric single-cell profiling across a large cell population. Our method can dramatically improve the throughput for imaging cytometry towards applications for phenotyping, pathology, and high-content screening.