UCSF

Spectral imaging is a powerful technique used to simultaneously study multiple fluorophore labels with overlapping emissions. Here, we present a computational spectral imaging method, which uses sample spatial fluorescence information as a reconstruction constraint. Our method addresses both the under-sampling issue of compressive spectral imaging and the low throughput issue of scanning spectral imaging. With simulated and experimental data, we have demonstrated the reconstruction precision of our method in two and three-color imaging. We have experimentally validated this method for differentiating cellular structures labeled with two red-colored fluorescent proteins, tdTomato and mCherry, which have highly overlapping emission spectra. Our method has the advantage of totally free wavelength choice and can also be combined with conventional filter-based sequential multi-color imaging to further improve multiplexing capability.