Mapping Pixel Dissimilarity in Wide-Field Super-Resolution Fluorescence Microscopy

Recent advances in fluorescence bioimaging with single-molecule sensitivity have relied on the analysis and visualization of single-molecule data obtained on smart fluorophores. We describe an alternative method to enhance the information content of densely labeled fluorescence images. Visualization is improved by representing pixels as the dissimilarities of the fluctuations of the fluorescence signals, with the dissimilarity being taken to the mean of the signals over all the pixels. Mapping pixel dissimilarity (Mappix) results in signal and information enhancement of the output images. In addition, the spatial distribution of the fluorescence brightness of the original image is not skewed. This allows large differences of molecular brightness to be handled which turns out to be critical to the fidelity of the final image. In this work, we provide testing of the Mappix approach with both simulated and real data. The results obtained on HEK cells expressing Dronpa photoswitchable fluorescent protein show that, for densely labeled samples, improvement can be obtained on fluorescence images allowing the observation of structural information. Despite some limitations, comparison to state of art methods reveals that Mappix can be very useful for biological imaging applications.