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.

Autofluorescence-Free In Vivo Imaging Using Polymer-Stabilized Nd3+-Doped YAG Nanocrystals.

Neodymium-doped yttrium aluminum garnet (YAG:Nd3+) has been widely developed during roughly the past 60 years and has been an outstanding fluorescent material. It has been considered as the gold standard among multipurpose solid-state lasers. Yet, the successful downsizing of this system into the nanoregimen has been elusive, so far. Indeed, the synthesis of a garnet structure at the nanoscale, with enough crystalline quality for optical applications, was found to be quite challenging. Here, we present an improved solvothermal synthesis method producing YAG:Nd3+ nanocrystals of remarkably good structural quality. Adequate surface functionalization using asymmetric double-hydrophilic block copolymers, constituted of a metal-binding block and a neutral water-soluble block, provides stabilized YAG:Nd3+ nanocrystals with long-term colloidal stability in aqueous suspensions. These newly stabilized nanoprobes offer spectroscopic quality (long lifetimes, narrow emission lines, and large Stokes shifts) close to that of bulk YAG:Nd3+. The narrow emission lines of YAG:Nd3+ nanocrystals are exploited by differential infrared fluorescence imaging, thus achieving an autofluorescence-free in vivo readout. In addition, nanothermometry measurements, based on the ratiometric fluorescence of the stabilized YAG:Nd3+ nanocrystals, are demonstrated. The progress here reported paves the way for the implementation of this new stabilized YAG:Nd3+ system in the preclinical arena.

Autofluorescence removal using a customized filter set.

Quantitative fluorescence microscopy is severely hindered by intrinsic autofluorescence (AF). Endogenous fluorescent molecules in tissue and cell samples emit fluorescence that often dominates signals from specific dyes. This makes AF removal critical to the development and practice of quantitative fluorescence microscopy. In this study, we showed that AF signal could be separated from specific signal using a customized filter set. The filter set used the same excitation and beam splitter as the standard filter set, but the emission filter was red-shifted 40-60 nm from the peak of the specific dye. This filter set configuration collected mostly AF with minimum contribution from the specific dye. A linear transformation of AF images was required to correct for the difference in exposure and filter configuration. The constants (slope and intercept) in linear transformation were obtained through a pixel to pixel comparison between AF images (no staining) obtained by the standard filter set and the customized AF filter set. After staining of specific dye, the standard filter collecting target dye spectra was used to capture both target signal and AF, whereas customized filter was used to capture only AF. AF removal was accomplished by subtracting the linear transformed AF image from the image obtained from the standard filter. To validate our approach, we examined weak staining of androgen receptor in an AF abundant prostate tissue sample. Our method revealed a similar but cleaner nuclear staining of androgen receptor in a specimen, when compared to a traditional autofluorescence removal method.