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In the past two decades, spectral imaging technologies have expanded the capacity of fluorescence microscopy for accurate detection of multiple labels, separation of labels from cellular and tissue autofluorescence, and analysis of autofluorescence signatures. These technologies have been implemented using a range of optical techniques, such as tunable filters, diffraction gratings, prisms, interferometry, and custom Bayer filters. Each of these techniques has associated strengths and weaknesses with regard to spectral resolution, spatial resolution, temporal resolution, and signal-to-noise characteristics. We have previously shown that spectral scanning of the fluorescence excitation spectrum can provide greatly increased signal strength compared to traditional emission-scanning approaches. Here, we present results from utilizing a Hyperspectral Imaging Fluorescence Excitation Scanning (HIFEX) microscope system for live cell imaging. Live cell signaling studies were performed using HEK 293 and rat pulmonary microvascular endothelial cells (PMVECs), transfected with either a cAMP FRET reporter or a Ca2+ reporter. Cells were further labeled to visualize subcellular structures (nuclei, membrane, mitochondria, etc.). Spectral images were acquired using a custom inverted microscope (TE2000, Nikon Instruments) equipped with a 300W Xe arc lamp and tunable excitation filter (VF- 5, Sutter Instrument Co., equipped with VersaChrome filters, Semrock), and run through MicroManager. Timelapse spectral images were acquired from 350-550 nm, in 5 nm increments. Spectral image data were linearly unmixed using custom MATLAB scripts. Results indicate that the HIFEX microscope system can acquire live cell image data at acquisition speeds of 8 ms/wavelength band with minimal photobleaching, sufficient for studying moderate speed cAMP and Ca2+ events. 

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.