Deep learning permits imaging of multiple structures with the same fluorophores.

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Abstract

Fluorescence microscopy is a powerful tool for life sciences, which employs fluorescent tags to label and observe cellular structures and their dynamics. However, due to the spectral overlap between different dyes, limited number of structures can be separately labeled and imaged for live cell applications. In addition, the conventional sequential channel imaging procedure is quite time-consuming as it needs to switch either different lasers or filters. Here we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.Copyright © 2024. Published by Elsevier Inc.