Abstract

Hyperspectral imaging is a powerful technique to simultaneously study multiple fluorophore labels with overlapping emissions. Here we present a computational hyperspectral imaging method, which uses the sample spatial fluorescence information as a reconstruction constraint. Our method addresses both the under-sampling issue of compressive hyperspectral imaging and the low throughput issue of scanning hyperspectral imaging. With simulated and experimental data, we have demonstrated the superior reconstruction precision of our method in two and three-color imaging. We have experimentally validated this method in 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 expand the choices of probes.