Unsupervised learning enables multicolor synchronous fluorescence microscopy without cytoarchitecture crosstalk

Abstract

In multicolor fluorescence microscopy, it is crucial to orient biological structures at a single-cell resolution based on precise anatomical annotations of cytoarchitecture images. However, during synchronous multicolor imaging, due to spectral mixing, the crosstalk from the blue signals of 4′,6-diamidino-2-phenylindole (DAPI)-stained cytoarchitecture images to the green waveband hinders the visualization and identification of green signals. Here, we proposed a deep learning-based framework named the crosstalk elimination and cytoarchitecture enhancement pipeline (CECEP) to simultaneously acquire crosstalk-free signals in the green channel and high-contrast DAPI-stained cytoarchitecture images during multicolor fluorescence imaging. For the CECEP network, we proposed an unsupervised learning algorithm named the cytoarchitecture enhancement network (CENet), which increased the signal-to-background ratio (SBR) of the cytoarchitecture images from 1.5 to 15.0 at a reconstruction speed of 25 Hz for 1800 × 1800 pixel images. The CECEP network is widely applicable to images of different quality, different types of tissues, and different multicolor fluorescence microscopy. In addition, the CECEP network can also facilitate various downstream analysis tasks, such as cell recognition, structure tensor calculation, and brain region segmentation. With the CECEP network, we simultaneously acquired two specific fluorescence-labeled neuronal distributions and their colocated high-SBR cytoarchitecture images without crosstalk throughout the brain. Experimental results demonstrate that our method could potentially facilitate multicolor fluorescence imaging applications in biology, such as revealing and visualizing different types of biological structures with precise locations and orientations.