Deep-learning-based removal of autofluorescence and fluorescence quantification in plant-colonizing bacteria in vivo

Fluorescence microscopy is common in bacteria-plant interaction studies. However, strong autofluorescence from plant tissues impedes in vivo studies on endophytes tagged with fluorescent proteins. To solve this problem, we developed a deep-learning-based approach to eliminate plant autofluorescence from fluorescence microscopy images, tested for the model endophyte Azoarcus olearius BH72 colonizing Oryza sativa roots. Micrographs from three channels (tdTomato for gene expression, green fluorescent protein (GFP) and AutoFluorescence (AF)) were processed by a neural network based approach, generating images that simulate the background autofluorescence in the tdTomato channel. After subtracting the model-generated signals from each pixel in the genuine channel, the autofluorescence in the tdTomato channel was greatly reduced or even removed. The deep-learning-based approach can be applied for fluorescence detection and quantification, exemplified by a weakly expressed, a cell-density modulated and a nitrogen-fixation gene in A. olearius. A transcriptional nifH::tdTomato fusion demonstrated stronger induction of nif genes inside roots than outside, suggesting extension of the rhizosphere effect for diazotrophs into the endorhizosphere. The pre-trained convolutional neural network model is easily applied to process other images of the same plant tissues with the same settings. This study showed the high potential of deep-learning-based approaches in image processing. With proper training data and strategies, autofluorescence in other tissues or materials can be removed for broad applications.