A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma.

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis-support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Stokes shift spectroscopy and machine learning for label-free human prostate cancer detection.

The Stokes shift spectra (S3) of human cancerous and normal prostate tissues were collected label free at a selected wavelength interval of 40 nm to investigate the efficacy of the approach based on three key molecules-tryptophan, collagen, and reduced nicotinamide adenine dinucleotide (NADH)-as cancer biomarkers. S3 combines both fluorescence and absorption spectra in one scan. The S3 spectra were analyzed using machine learning (ML) algorithms, including principal component analysis (PCA), nonnegative matrix factorization (NMF), and support vector machines (SVMs). The components retrieved from the S3 spectra were considered principal biomarkers. The differences in the weights of the components between the two types of tissues were found to be significant. Sensitivity, specificity, and accuracy were calculated to evaluate the performance of SVM classification. This research demonstrates that S3 spectroscopy is effective for detecting the changes in the relative concentrations of the endogenous fluorophores in tissues due to the development of cancer label free.

Intraoperative detection of human meningioma using a handheld visible resonance Raman analyzer.

To report for the first time the preliminary results for the evaluation of a VRR-LRR™ analyzer based on visible resonance Raman technique to identify human meningioma grades and margins intraoperatively. Unprocessed primary and recurrent solid human meningeal tissues were collected from 33 patients and underwent Raman analysis during surgeries. A total of 1180 VRR spectra were acquired from fresh solid tissues using a VRR-LRR™ analyzer. A confocal HR Evolution (HORIBA, France SAS) Raman system with 532-nm excitation wavelength was also used to collect data for part of the ex vivo samples after they were thawed from - 80 °C for comparison. The preliminary analysis led to the following observations. (1) The intensity ratio of VRR peaks of protein to fatty acid (I2934/I2888) decreased with the increase of meningioma grade. (2) The ratio of VRR peaks of phosphorylated protein to amid I (I1588/I1639) decreased for the higher grade of meningioma. (3) Three RR vibration modes at 1378, 3174, and 3224 cm-1 which were related to the molecular vibrational bands of oxy-hemeprotein, amide B, and amide A protein significantly changed in peak intensities in the two types of meningioma tissues compared to normal tissue. (4) The changes in the intensities of VRR modes of carotenoids at 1156 and 1524 cm-1 were also found in the meningioma boundary. The VRR-LRR™ analyzer demonstrates a new approach for label-free, rapid, and objective identification of primary human meningioma in quasi-clinical settings. The accuracy for detecting meningioma tissues using support vector machines (SVMs) was over 70% based on Raman peaks of key biomolecules and up to 100% using principal component analysis (PCA).

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods.

Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy.

Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

Exploring Multiphoton Microscopy as a Novel Tool to Differentiate Chromophobe Renal Cell Carcinoma From Oncocytoma in Fixed Tissue Sections.

CONTEXT: - Distinguishing chromophobe renal cell carcinoma (chRCC), especially in the presence of eosinophilic cytoplasm, from oncocytoma on hematoxylin-eosin can be difficult and often requires time-consuming ancillary procedures that ultimately may not be informative. OBJECTIVE: - To explore the potential of multiphoton microscopy (MPM) as an alternative and rapid diagnostic tool in differentiating oncocytoma from chRCC at subcellular resolution without tissue processing. DESIGN: - Unstained, deparaffinized tissue sections from 27 tumors (oncocytoma [n = 12], chRCC [n = 12], eosinophilic variant of chRCC [n = 1], and atypical oncocytic renal neoplasm [n = 2]) were imaged with MPM. Morphologic evaluation and automated quantitative morphometric analysis were conducted to distinguish between chRCC and oncocytoma. RESULTS: - The typical cases of oncocytomas (12 of 12) and chRCC (12 of 12) could be readily differentiated on MPM based on the morphologic features similar to hematoxylin-eosin. The most striking MPM signature of both of the tumors was the presence of autofluorescent intracytoplasmic granules, which are not seen on hematoxylin-eosin-stained slides. Although we saw these granules in both types of tumors, they appeared distinct, based on their size, shape, cytoplasmic distribution, and autofluorescence wavelengths, and were valuable in arriving at a definitive diagnosis. For oncocytomas and chRCC, high diagnostic accuracies of 100% and 83.3% were achieved on blinded MPM and morphometric analysis, respectively. CONCLUSIONS: - To the best of our knowledge, this is the first demonstration of MPM to distinguish chRCC from oncocytoma in fixed tissues. Our study was limited by small sample size and only a few variants of oncocytic tumors. Prospective studies are warranted to assess the utility of MPM as a diagnostic aid in oncocytic renal tumors.

Multifunctional in vivo imaging of pancreatic islets during diabetes development.

Pancreatic islet dysfunction leading to insufficient glucose-stimulated insulin secretion triggers the clinical onset of diabetes. How islet dysfunction develops is not well understood at the cellular level, partly owing to the lack of approaches to study single islets longitudinally in vivo Here, we present a noninvasive, high-resolution system to quantitatively image real-time glucose metabolism from single islets in vivo, currently not available with any other method. In addition, this multifunctional system simultaneously reports islet function, proliferation, vasculature and macrophage infiltration in vivo from the same set of images. Applying our method to a longitudinal high-fat diet study revealed changes in islet function as well as alternations in islet microenvironment. More importantly, this label-free system enabled us to image real-time glucose metabolism directly from single human islets in vivo for the first time, opening the door to noninvasive longitudinal in vivo studies of healthy and diabetic human islets.

Fluorescence tomography of targets in a turbid medium using non-negative matrix factorization.

A near-infrared optical tomography approach for detection, three-dimensional localization, and cross-section imaging of fluorescent targets in a turbid medium is introduced. The approach uses multisource probing of targets, multidetector acquisition of diffusely transmitted fluorescence signal, and a non-negative matrix factorization based blind source separation scheme to obtain three-dimensional location of the targets. A Fourier transform back-projection algorithm provides an estimate of target cross section. The efficacy of the approach is demonstrated in an experiment involving two laterally separated small fluorescent targets embedded in a human breast tissue-simulating sample of thickness 60 times the transport mean free path. The approach could locate the targets within ∼1 mm of their known positions, and provide estimates of their cross sections. The high spatial resolution, fast reconstruction speed, noise tolerance, and ability to detect small targets are indicative of the potential of the approach for detecting and locating fluorescence contrast-enhanced breast tumors in early growth stages, when they are more amenable to treatment.

Time reversal optical tomography: locating targets in a highly scattering turbid medium.

A time reversal optical tomography (TROT) method for near-infrared (NIR) diffuse optical imaging of targets embedded in a highly scattering turbid medium is presented. TROT combines the basic symmetry of time reversal invariance and subspace-based signal processing for retrieval of target location. The efficacy of TROT is tested using simulated data and data obtained from NIR imaging experiments on absorptive and scattering targets embedded in Intralipid-20% suspension in water, as turbid medium. The results demonstrate the potential of TROT for detecting and locating small targets in a turbid medium, such as, breast tumors in early stages of growth.

[Study of poly (3,4-ethylene dioxythiophene) : poly (styrene sulfonate) by in-situ resonance Raman spectroscopy].

Poly (3,4-ethylene dioxythiophene) (PEDOT): Poly (styrene sulfonate) (PSS) has attracted a lot of interest for application in organic electronics due to good stability and high electronic conductivity in its doped state. Indeed, thin layers of PEDOT:PSS was regularly used in light emitting diodes (PLEDs) as hole injection and transportation layer. Here, Doping and dedoping states of PEDOT:PSS were studied by absorbance spectra and Raman spectra. A new absorption band centered at 620 nm was observed on dedoped PEDOT:PSS. Consistently, Raman signals of dedoped PEDOT:PSS are resonantly intensified since the Raman excitation wavelength (633 nm) is set in the enhanced absorption band. So it gives a sensitive way to study the doping and dedoping states of PEDOT:PSS. Furthermore, for the encapsulated polymer light-emitting diodes, Raman spectroscopy is a powerful way to study the polymer layers inside the devices.