Abstract
HIV-1 enters the nucleus of non-dividing cells through the nuclear pore complex where it integrates into the host genome. The mechanism of HIV-1 nuclear import remains poorly understood. A powerful means to investigate the docking of HIV-1 at the nuclear pore and nuclear import of viral complexes is through single virus tracking in live cells. This approach necessitates fluorescence labeling of HIV-1 particles and the nuclear envelope, which may be challenging, especially in the context of primary cells. Here, we leveraged a deep neural network model for label-free visualization of the nuclear envelope using transmitted light microscopy. A training image set of cells with fluorescently labeled nuclear Lamin B1 (ground truth), along with the corresponding transmitted light images, was acquired and used to train our model to predict the morphology of the nuclear envelope in fixed cells. This protocol yielded accurate predictions of the nuclear membrane and was used in conjunction with virus infection to examine the nuclear entry of fluorescently labeled HIV-1 complexes. Analyses of HIV-1 nuclear import as a function of virus input yielded identical numbers of fluorescent viral complexes per nucleus using the ground truth and predicted nuclear membrane images. We also demonstrate the utility of predicting the nuclear envelope based on transmitted light images for multicolor fluorescence microscopy of infected cells. Importantly, we show that our model can be adapted to predict the nuclear membrane of live cells imaged at 37 °C, making this approach compatible with single virus tracking. Collectively, these findings demonstrate the utility of deep learning approaches for label-free imaging of cellular structures during early stages of virus infection.