A real-time clinical endoscopic system for intraluminal, multiplexed imaging of surface-enhanced Raman scattering nanoparticles.

The detection of biomarker-targeting surface-enhanced Raman scattering (SERS) nanoparticles (NPs) in the human gastrointestinal tract has the potential to improve early cancer detection; however, a clinically relevant device with rapid Raman-imaging capability has not been described. Here we report the design and in vivo demonstration of a miniature, non-contact, opto-electro-mechanical Raman device as an accessory to clinical endoscopes that can provide multiplexed molecular data via a panel of SERS NPs. This device enables rapid circumferential scanning of topologically complex luminal surfaces of hollow organs (e.g., colon and esophagus) and produces quantitative images of the relative concentrations of SERS NPs that are present. Human and swine studies have demonstrated the speed and simplicity of this technique. This approach also offers unparalleled multiplexing capabilities by simultaneously detecting the unique spectral fingerprints of multiple SERS NPs. Therefore, this new screening strategy has the potential to improve diagnosis and to guide therapy by enabling sensitive quantitative molecular detection of small and otherwise hard-to-detect lesions in the context of white-light endoscopy.

High-sensitivity, real-time, ratiometric imaging of surface-enhanced Raman scattering nanoparticles with a clinically translatable Raman endoscope device.

Topical application and quantification of targeted, surface-enhanced Raman scattering (SERS) nanoparticles offer a new technique that has the potential for early detection of epithelial cancers of hollow organs. Although less toxic than intravenous delivery, the additional washing required to remove unbound nanoparticles cannot necessarily eliminate nonspecific pooling. Therefore, we developed a real-time, ratiometric imaging technique to determine the relative concentrations of at least two spectrally unique nanoparticle types, where one serves as a nontargeted control. This approach improves the specific detection of bound, targeted nanoparticles by adjusting for working distance and for any nonspecific accumulation following washing. We engineered hardware and software to acquire SERS signals and ratios in real time and display them via a graphical user interface. We report quantitative, ratiometric imaging with nanoparticles at pM and sub-pM concentrations and at varying working distances, up to 50 mm. Additionally, we discuss optimization of a Raman endoscope by evaluating the effects of lens material and fiber coating on background noise, and theoretically modeling and simulating collection efficiency at various working distances. This work will enable the development of a clinically translatable, noncontact Raman endoscope capable of rapidly scanning large, topographically complex tissue surfaces for small and otherwise hard to detect lesions.

A small animal Raman instrument for rapid, wide-area, spectroscopic imaging.

Raman spectroscopy, amplified by surface enhanced Raman scattering (SERS) nanoparticles, is a molecular imaging modality with ultra-high sensitivity and the unique ability to multiplex readouts from different molecular targets using a single wavelength of excitation. This approach holds exciting prospects for a range of applications in medicine, including identification and characterization of malignancy during endoscopy and intraoperative image guidance of surgical resection. The development of Raman molecular imaging with SERS nanoparticles is presently limited by long acquisition times, poor spatial resolution, small field of view, and difficulty in animal handling with existing Raman spectroscopy instruments. Our goal is to overcome these limitations by designing a bespoke instrument for Raman molecular imaging in small animals. Here, we present a unique and dedicated small-animal Raman imaging instrument that enables rapid, high-spatial resolution, spectroscopic imaging over a wide field of view (> 6 cm(2)), with simplified animal handling. Imaging of SERS nanoparticles in small animals demonstrated that this small animal Raman imaging system can detect multiplexed SERS signals in both superficial and deep tissue locations at least an order of magnitude faster than existing systems without compromising sensitivity.

A Raman-based endoscopic strategy for multiplexed molecular imaging.

Endoscopic imaging is an invaluable diagnostic tool allowing minimally invasive access to tissues deep within the body. It has played a key role in screening colon cancer and is credited with preventing deaths through the detection and removal of precancerous polyps. However, conventional white-light endoscopy offers physicians structural information without the biochemical information that would be advantageous for early detection and is essential for molecular typing. To address this unmet need, we have developed a unique accessory, noncontact, fiber optic-based Raman spectroscopy device that has the potential to provide real-time, multiplexed functional information during routine endoscopy. This device is ideally suited for detection of functionalized surface-enhanced Raman scattering (SERS) nanoparticles as molecular imaging contrast agents. This device was designed for insertion through a clinical endoscope and has the potential to detect and quantify the presence of a multiplexed panel of tumor-targeting SERS nanoparticles. Characterization of the Raman instrument was performed with SERS particles on excised human tissue samples, and it has shown unsurpassed sensitivity and multiplexing capabilities, detecting 326-fM concentrations of SERS nanoparticles and unmixing 10 variations of colocalized SERS nanoparticles. Another unique feature of our noncontact Raman endoscope is that it has been designed for efficient use over a wide range of working distances from 1 to 10 mm. This is necessary to accommodate for imperfect centering during endoscopy and the nonuniform surface topology of human tissue. Using this endoscope as a key part of a multiplexed detection approach could allow endoscopists to distinguish between normal and precancerous tissues rapidly and to identify flat lesions that are otherwise missed.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy.

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm's superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy.

The least squares fitting algorithm is the most commonly used algorithm in Raman spectroscopy. In this paper, however, we show that it is sensitive to variations in the background signal when the signal of interest is weak. To address this problem, we propose a novel algorithm to analyze measured spectra in Raman spectroscopy. The method is a hybrid least squares and principal component analysis algorithm. It explicitly accounts for any variations expected in the reference spectra used in the signal decomposition. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, and demonstrate the novel algorithm's superior performance by comparing quantitative error metrics. Our experiments use both simulated data and data acquired from an in vitro solution of Raman-enhanced gold nanoparticles.