Multiplexed virtual fluorescence labeling from multi-contrast microscopy by deep learning

Abstract

Standard immunofluorescence (IF) staining is labor-intensive, time-consuming and suffers from inflexibility and poor multiplicity. To overcome these limitations, we proposed a deep learning (DL) approach for virtual IF staining with high multiplicity and specificity from label-free reflectance microscopy. Our results show that DL-enabled label-free IF microscopy can predict characteristic subcellular features during different cell cycles and reveal cellular phenotypes with high accuracy.