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

ABSTRACT: 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

SUBMITTER: Cheng S 

PROVIDER: S-EPMC7810377 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature