Identifying molecular contributors to autofluorescence of neoplastic and normal colon sections using excitation-scanning hyperspectral imaging

Abstract

Autofluorescence, the endogenous fluorescence present in cells and tissues, has historically been considered a nuisance in biomedical imaging. Many endogenous fluorophores, specifically, collagen, elastin, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide (FAD), are found throughout the human body. In fluorescence imaging scenarios, these signals can be prohibitive as they can outcompete signals introduced for diagnostic purposes. However, autofluorescence also contains information that has diagnostic value. 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. We propose to utilize excitation-scanning hyperspectral imaging of autofluorescence to differentiate neoplastic lesions from surrounding non-neoplastic "normal" tissue. The spectra of isolated autofluorescent molecules are obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with an Xe arc lamp and thin-film tunable filter array (VersaChrome, Semrock, Inc.). Scans utilize excitation wavelengths from 360 to 550 nm in 5-nm increments. The resultant molecule-specific spectra are used to analyze hyperspectral image stacks from normal and neoplastic colorectal tissues. Due to a limited number of samples, neoplastic tissues examined here are a pool of both colorectal adenocarcinoma and adenomatous polyps. The hyperspectral images are analyzed with ENVI software and custom MATLAB scripts, including linear spectral unmixing. Initial results indicate the ability to separate signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states, in this case, normal colon versus neoplastic colon. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will focus on expanding the library of pure molecules, exploring histogram distance metrics as a means for identifying deviations in spectral signatures, and examining more defined disease states.

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

“…We have previously investigated the sensitivity of these algorithms for unmixing a mixture of fluorophores in a spectral imaging approach . Additionally, our group has recently shown the ability to correctly unmix known concentrations of autofluorescent molecules (NADH and FAD) in solution . As further validation for our methodology, we performed a similar experiment for this study with known concentrations of Rhodamine and Fluorescein, and showed a high correlation ( R 2 = 0.95) between measured and expected concentrations (Figure S4).…”

“…Excitation‐scanning approaches provide higher signal‐to‐noise ratios and thus, higher sensitivity and faster acquisition speeds than more traditional emission scanning approaches . We have previously shown the ability to differentiate cancerous and noncancerous colonic lesions with a band sequential excitation‐scanning approach . Previous studies by Grosberg et al and others used label‐free imaging with wavelength scanning in multiphoton microscopy to parse the autofluorescence of select tissues .…”

Advances in endoscopy for colorectal polyp detection and classification

“…The modification in biochemical composition of dysplastic tissue can be detected by these techniques. 48,49 A novel imaging technology called second harmonic generation has the ability to evaluate the amount of extracellular matrix collagen protein and its alignment. It can differentiate malignant from nonmalignant colonic polyp tissue with high sensitivity and specificity and can also distinguish high-grade dysplasia and malignant lesions in an objective, numeric fashion, which makes it a very promising technique to use for early CRC detection.…”

Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks

“…In the final section of the chapter, we discuss the distribution of the data that were collected. The collected colon tissue samples were imaged with QImaging Corporation's Rolera EM-C 2 camera at the laboratory, using the excitation wavelength tuning method described in Deal et al [24]. The camera has a 14-bit digital output, outputting a unitless spectral intensity at each pixel with a range between 0 and 16383 (2 14 As can be seen from the table, there is a lot of variation in the number of images provided for each patient, with some patients having as few as 5 images, and others as many as 21.…”

“…The colon cancer tissues samples that are used in this research were gathered as previously described in Deal et al[24] from the University of South Alabama Medical Center Department of Surgery. This dataset is not publicly available and is used as part of a collaboration with researchers at the University of South Alabama.…”