Hyperspectral imaging system for quantitative identification and discrimination of fluorescent labels in the presence of autofluorescence

Multivariate data analysis applied to hyperspectral images offers the unique opportunity to dramatically increase the amount of information gained from a single biological sample. Numerous fluorescent tags can be used to perform multiple studies in parallel from a single hyperspectral image scan. Highly spatially and spectrally overlapping fluorophores can be separated even amidst a large autofluorescence background with the use of multivariate curve resolution methods. The results of two biological samples with multiple fluorescent labels are shown and compared to a traditional filter-based multispectral system. These examples illustrate the combined power of the hyperspectral microscope hardware and the multivariate image analysis software for biomedical imaging. This technique has the potential to be applied to a broad array of biological applications where fluorescent tags are a central and ubiquitous tool, and to biomedical areas that focus on the discovery and identification of weak, broad spectrum native fluorescence