No abstract

Combining super-resolution localization microscopy with pathology creates new opportunities for biomedical researches. This combination requires a suitable image mosaic method for generating a panoramic image from many overlapping super-resolution images. However, current image mosaic methods are not suitable for this purpose. Here we proposed a computational framework and developed an image mosaic method called NanoStitcher. We generated ground truth datasets and defined criteria to evaluate this computational framework. We used both simulated and experimental datasets to prove that NanoStitcher exhibits better performance than two representative image mosaic methods. This study is helpful for the mature of super-resolution digital pathology.

We introduce a simplified labeling strategy based on biotin–streptavidin interactions for expansion single-molecule localization microscopy (Ex-SMLM) is proposed, which can effectively preserve fluorophores and improve the resolution of SMLM 2.6 times.