Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging. Issue 2 (15th October 2019) - Favreau, Peter F.; Deal, Joshua A.; Harris, Bradley; Weber, David S.; Rich, Thomas C.; Leavesley, Silas J.
Abstract: Spectral imaging approaches provide new possibilities for measuring and discriminating fluorescent molecules in living cells and tissues. These approaches often employ tunable filters and robust image processing algorithms to identify many fluorescent labels in a single image set. Here, we present results from a novel spectral imaging technology that scans the fluorescence excitation spectrum, demonstrating that excitation‐scanning hyperspectral image data can discriminate among tissue types and estimate the molecular composition of tissues. This approach allows fast, accurate quantification of many fluorescent species from multivariate image data without the need of exogenous labels or dyes. We evaluated the ability of the excitation‐scanning approach to identify endogenous fluorescence signatures in multiple unlabeled tissue types. Signatures were screened using multi‐pass principal component analysis. Endmember extraction techniques revealed conserved autofluorescent signatures across multiple tissue types. We further examined the ability to detect known molecular signatures by constructing spectral libraries of common endogenous fluorophores and applying multiple spectral analysis techniques on test images from lung, liver and kidney. Spectral deconvolution revealed structure‐specific morphologic contrast generated from pure molecule signatures. These results demonstrate that excitation‐scanning spectral imaging, coupled with spectral imaging processing techniques, provides an approach for discriminating among tissue types and assessing the molecular composition of tissues. Additionally, excitation scanning offers the ability to rapidly screen molecular markers across a range of tissues without using fluorescent labels. This approach lays the groundwork for translation of excitation‐scanning technologies to clinical imaging platforms. Abstract : Changes in tissue fluorescence can be a sensitive cancer marker but identifying the molecular origins of these changes remains challenging. This study identified unique tissue spectra corresponding to well‐known autofluorescent components using a novel label‐free excitation‐scanning spectral imaging platform. A suite of four image processing algorithms revealed morphologic contrast generated from autofluorescent components. This approach lays the groundwork for translation of excitation‐scanning technologies to clinical imaging platforms.