Isaac J. Pence

1064 nm dispersive Raman spectroscopy of tissues with strong near-infrared autofluorescence

Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.

Assessing Variability of In Vivo Tissue Raman Spectra

Raman spectroscopy (RS) has received increasing attention as a potential tool for clinical diagnostics. However, the unknown comparability of multiple tissue RS systems remains a major issue for technique standardization and future multisystem trials. In this study, we evaluated potential factors affecting data collection and interpretation, utilizing the skin as an example tissue. The effects of contact pressure and probe angle were characterized as potential user-induced variability sources. Similarly, instrumentation-induced variability sources of system stability and system-dependent response were also analyzed on skin and a nonvolatile biological tissue analog. Physiologically induced variations were studied on multiple tissue locations and patients. The effect of variability sources on spectral line shape and dispersion was analyzed with analysis-of-variance methods, and a new metric for comparing spectral dispersion was defined. In this study, in vivo measurements were made on multiple sites of skin from five healthy volunteers, with four stand-alone fiber optic probe–based tissue RS systems. System stability and controlled user-induced variables had no effects on obtained spectra. By contrast, instrumentation and anatomical location of measurement were significant sources of variability. These findings establish the comparability of tissue Raman spectra obtained by unique systems. Furthermore, we suggest steps for further procedural and instrumentation standardization prior to broad clinical applications of the technique.

Discrimination of Liver Malignancies with 1064 nm Dispersive Raman Spectroscopy

Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

In vivo Raman spectral analysis of impaired cervical remodeling in a mouse model of delayed parturition

Monitoring cervical structure and composition during pregnancy has high potential for prediction of preterm birth (PTB), a problem affecting 15 million newborns annually. We use in vivo Raman spectroscopy, a label-free, light-based method that provides a molecular fingerprint to non-invasively investigate normal and impaired cervical remodeling. Prostaglandins stimulate uterine contractions and are clinically used for cervical ripening during pregnancy. Deletion of cyclooxygenase-1 (Cox-1), an enzyme involved in production of these prostaglandins, results in delayed parturition in mice. Contrary to expectation, Cox-1 null mice displayed normal uterine contractility; therefore, this study sought to determine whether cervical changes could explain the parturition differences in Cox-1 null mice and gestation-matched wild type (WT) controls. Raman spectral changes related to extracellular matrix proteins, lipids, and nucleic acids were tracked over pregnancy and found to be significantly delayed in Cox-1 null mice at term. A cervical basis for the parturition delay was confirmed by other ex vivo tests including decreased tissue distensibility, hydration, and elevated progesterone levels in the Cox-1 null mice at term. In conclusion, in vivo Raman spectroscopy non-invasively detected abnormal remodeling in the Cox-1 null mouse, and clearly demonstrated that the cervix plays a key role in their delayed parturition.

Clinical characterization of in vivo inflammatory bowel disease with Raman spectroscopy

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohns disease (CD), affects over 1 million Americans and 2 million Europeans, and the incidence is increasing worldwide. While these diseases require unique medical care, the differentiation between UC and CD lacks a gold standard, and therefore relies on long term follow up, success or failure of existing treatment, and recurrence of the disease. Here, we present colonoscopy-coupled fiber optic probe-based Raman spectroscopy as a minimally-invasive diagnostic tool for IBD of the colon (UC and Crohns colitis). This pilot in vivo study of subjects with existing IBD diagnoses of UC (n = 8), CD (n = 15), and normal control (n = 8) aimed to characterize spectral signatures of UC and CD. Samples were correlated with tissue pathology markers and endoscopic evaluation. The collected spectra were processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate IBD and disease classes. Confounding factors including the presence of active inflammation and the particular colon segment measured were investigated and integrated into the devised prediction algorithm, reaching 90% sensitivity and 75% specificity to CD from this in vivo data set. These results represent significant progress towards improved real-time classification for accurate and automated in vivo detection and discrimination of IBD during colonoscopy procedures.

Intraoperative Raman spectroscopy of soft tissue sarcomas

Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated through surgical resection. Current intraoperative margin assessment methods are limited and highlight the need for an improved approach with respect to time and specificity. Here we investigate the potential of near‐infrared Raman spectroscopy for the intraoperative differentiation of STS from surrounding normal tissue.

Endoscopy-coupled Raman spectroscopy for in vivo discrimination of inflammatory bowel disease

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s colitis (CC), affects nearly 2 million Americans, and the incidence is increasing worldwide. It has been established that UC and CC are distinct forms of IBD and require different medical care, however the distinction made between UC and CC is based upon inexact clinical, radiological, endoscopic, and pathologic features. A diagnosis of indeterminate colitis occurs in up to 15% of patients when UC and CC features overlap and cannot be differentiated; in these patients, diagnosis relies on long term followup, success or failure of existing treatment, and recurrence of the disease. Thus, there is need for a tool that can improve the sensitivity and specificity for fast, accurate and automated diagnosis of IBD. Here we present colonoscopy-coupled fiber probe-based Raman spectroscopy as a novel in vivo diagnostic tool for IBD. This in vivo study of both healthy control (NC, N=10) and diagnosed IBD patients with UC (N=15) and CC (N=26) aims to characterize spectral signatures of NC, UC, and CC. Samples are correlated with tissue pathology markers and endoscopic evaluation. Optimal collection parameters for detection have been identified based upon the new, application specific instrument design. The collected spectra are processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate NC, UC, and CC. Development of spectral markers to discriminate disease type is a necessary first step in the development of real-time, accurate and automated in vivo detection of IBD during colonoscopy procedures.

Polarization in Raman spectroscopy helps explain bone brittleness in genetic mouse models

Abstract. Raman spectroscopy (RS) has been extensively used to characterize bone composition. However, the link between bone biomechanics and RS measures is not well established. Here, we leveraged the sensitivity of RS polarization to organization, thereby assessing whether RS can explain differences in bone toughness in genetic mouse models for which traditional RS peak ratios are not informative. In the selected mutant mice—activating transcription factor 4 (ATF4) or matrix metalloproteinase 9 (MMP9) knock-outs—toughness is reduced but differences in bone strength do not exist between knock-out and corresponding wild-type controls. To incorporate differences in the RS of bone occurring at peak shoulders, a multivariate approach was used. Full spectrum principal components analysis of two paired, orthogonal bone orientations (relative to laser polarization) improved genotype classification and correlation to bone toughness when compared to traditional peak ratios. When applied to femurs from wild-type mice at 8 and 20 weeks of age, the principal components of orthogonal bone orientations improved age classification but not the explanation of the maturation-related increase in strength. Overall, increasing polarization information by collecting spectra from two bone orientations improves the ability of multivariate RS to explain variance in bone toughness, likely due to polarization sensitivity to organizational changes in both mineral and collagen.

Evaluating feasibility of an automated 3-dimensional scanner using Raman spectroscopy for intraoperative breast margin assessment

Breast conserving surgery is the preferred treatment for women diagnosed with early stage invasive breast cancer. To ensure successful breast conserving surgeries, efficient tumour margin resection is required for minimizing tumour recurrence. Currently surgeons rely on touch preparation cytology or frozen section analysis to assess tumour margin status intraoperatively. These techniques have suboptimal accuracy and are time-consuming. Tumour margin status is eventually confirmed using postoperative histopathology that takes several days. Thus, there is a need for a real-time, accurate, automated guidance tool that can be used during tumour resection intraoperatively to assure complete tumour removal in a single procedure. In this paper, we evaluate feasibility of a 3-dimensional scanner that relies on Raman Spectroscopy to assess the entire margins of a resected specimen within clinically feasible time. We initially tested this device on a phantom sample that simulated positive tumour margins. This device first scans the margins of the sample and then depicts the margin status in relation to an automatically reconstructed image of the phantom sample. The device was further investigated on breast tissues excised from prophylactic mastectomy specimens. Our findings demonstrate immense potential of this device for automated breast tumour margin assessment to minimise repeat invasive surgeries.

Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.