A comprehensive dataset of image sequences covering 20 fluorescent protein labels and 12 imaging conditions for use...

Super-resolution fluorescence microscopy techniques allow imaging fluorescently labelled structures with a resolution that surpasses the diffraction limit of light (approx. 200nm). The quality and, thus, reliability of each of these techniques is strongly dependent on (1) the quality of the optics, (2) the fitness of the specific fluorescent label for the given technique and (3) the algorithms being used. Of these, the fitness of the labels is most subjective, as fitness metrics are scarce, and generating samples with different labels and imaging them is laborious. This prevent rigorous fitness assessment of fluorescent labels. We have developed a mathematical framework for assessing the quality of SOFI data [1], [2], which we used to assess the fitness of 20 different fluorescent protein labels for SOFI imaging. Here, we report this dataset of 2240 image sequences, representing 10 fields of view each of transfected Cos7 cells expressing each of the 20 different fluorescent proteins under 4-12 imaging conditions. The labels span the visible spectrum and include non-photo-transforming and photo-transforming fluorescent proteins. The imaging conditions consist of 4 different excitation powers, each with three different powers of 405 nm light added (except for the blue labels that are excited with 405 nm light). Though this data was in essence generated to assess which labels are best suited for SOFI imaging, it can be used as a benchmark for further development of the SOFI algorithm, or for the development of other super-resolution imaging modalities that benefit from similar input data.