Comparison of spectral FRET microscopy approaches for single-cell analysis.

Förster resonance energy transfer (FRET) is a valuable tool for measuring molecular distances and the effects of biological processes such as cyclic nucleotide messenger signaling and protein localization. Most FRET techniques require two fluorescent proteins with overlapping excitation/emission spectral pairing to maximize detection sensitivity and FRET efficiency. FRET microscopy often utilizes differing peak intensities of the selected fluorophores measured through different optical filter sets to estimate the FRET index or efficiency. Microscopy platforms used to make these measurements include wide-field, laser scanning confocal, and fluorescence lifetime imaging. Each platform has associated advantages and disadvantages, such as speed, sensitivity, specificity, out-of-focus fluorescence, and Z-resolution. In this study, we report comparisons among multiple microscopy and spectral filtering platforms such as standard 2-filter FRET, emission-scanning hyperspectral imaging, and excitation-scanning hyperspectral imaging. Samples of human embryonic kidney (HEK293) cells were grown on laminin-coated 28 mm round gridded glass coverslips (10816, Ibidi, Fitchburg, Wisconsin) and transfected with adenovirus encoding a cAMP-sensing FRET probe composed of a FRET donor (Turquoise) and acceptor (Venus). Additionally, 3 FRET "controls" with fixed linker lengths between Turquoise and Venus proteins were used for inter-platform validation. Grid locations were logged, recorded with light micrographs, and used to ensure that whole-cell FRET was compared on a cell-by-cell basis among the different microscopy platforms. FRET efficiencies were also calculated and compared for each method. Preliminary results indicate that hyperspectral methods increase the signal-to-noise ratio compared to a standard 2-filter approach.

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning.

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

Demystifying autofluorescence with excitation-scanning hyperspectral imaging.

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states.

Excitation-Scanning Hyperspectral Imaging as a Means to Discriminate Various Tissues Types.

Little is currently known about the fluorescence excitation spectra of disparate tissues and how these spectra change with pathological state. Current imaging diagnostic techniques have limited capacity to investigate fluorescence excitation spectral characteristics. This study utilized excitation-scanning hyperspectral imaging to perform a comprehensive assessment of fluorescence spectral signatures of various tissues. Immediately following tissue harvest, a custom inverted microscope (TE-2000, Nikon Instruments) with Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) were used to acquire hyperspectral image data from each sample. Scans utilized excitation wavelengths from 340 nm to 550 nm in 5 nm increments. Hyperspectral images were analyzed with custom Matlab scripts including linear spectral unmixing (LSU), principal component analysis (PCA), and Gaussian mixture modeling (GMM). Spectra were examined for potential characteristic features such as consistent intensity peaks at specific wavelengths or intensity ratios among significant wavelengths. The resultant spectral features were conserved among tissues of similar molecular composition. Additionally, excitation spectra appear to be a mixture of pure endmembers with commonalities across tissues of varied molecular composition, potentially identifiable through GMM. These results suggest the presence of common autofluorescent molecules in most tissues and that excitation-scanning hyperspectral imaging may serve as an approach for characterizing tissue composition as well as pathologic state. Future work will test the feasibility of excitation-scanning hyperspectral imaging as a contrast mode for discriminating normal and pathological tissues.