Shona Stewart

Studying Bacterial Metabolic States Using Raman Spectroscopy

Natural metabolic variability expected during characteristic growth phases in batch cultures of Escherichia coli and Staphylococcus epidermidis were studied by Raman spectroscopy. Spectral changes induced by metabolic changes found in the growth phases (i.e., lag, exponential, stationary, and decay) were identified. Maximum intensity of bands assigned to DNA and RNA bases are seen at the beginning of the exponential phase, when cells are metabolically active, and minimum intensities are seen when cells are decaying. High agreement in spectral variation due to growth phases was seen for all the trials that were performed, four growth cycles for E. coli and two for S. epidermidis. Batch cultures were monitored by standard plate counts to identify all growth phases, including decay. Spectral data were analyzed by principal component analysis (PCA) and discriminant analysis to identify similarities and differences and to estimate a classification performance based on growth phases. For the species evaluated, spectra during decay are grouped closer to each other and separated from lag, exponential, and stationary cells. These results suggest that Raman spectroscopy can be used to study metabolic states in bacteria and in particular cell viability.

Raman Spectroscopic Discrimination of Cell Response to Chemical and Physical Inactivation

Raman spectroscopy was applied to study Escherichia coli and Staphylococcus epidermidis cells that were inactivated by different chemicals and stress conditions including starvation and high temperature. E. coli cells exposed to starvation conditions over several days lost viability at the same rate that spectral bands assigned to DNA and RNA bases decreased in intensity. Band intensities correlate with standard plate counts with R2 = 0.99 and R2 = 0.97, respectively. Principal components analysis and discriminant analysis multivariate statistical techniques were used to evaluate the spectral data collected. Significant changes were observed in the spectra of treated cells in comparison with their respective controls (samples without treatment). As a result, there was a significant differentiation between viable and non-viable cells (treated and non-treated cells) in the first and second principal component plots for all the treatments. Discriminant analysis was used along with PCA to estimate a classification rate based on viability status of the cells. Non-viable cells were differentiated from viable cells with classification rates that ranged between 60 and 90% for specific treatments (i.e., EDTA-treated cells versus control cells). The classification rate obtained considering all the treatments (non-viable cells) and controls (viable cells) at the same time for each of the species studied was 86%. The classification rate based on species differentiation when all the spectra (viable and non-viable) were used was 87%. These results suggest that Raman spectroscopy is a powerful tool that can be used to evaluate viability and to study metabolic changes in microorganisms. It is a robust method for bacterial identification even when high spectral variations are introduced.

Early mineralization of normal and pathologic calvaria as revealed by Raman spectroscopy

Bone tissue consists of a carbonated apatite-like mineral supported on a hydrated, collagen-rich protein matrix. Despite extensive studies into the macroscopic characteristics of bone, much about the early stages of bone formation remains unknown. Raman microspectroscopy and imaging are increasingly important tools for the study of mineralized tissue, due to advancements in both spectral acquisition and analysis protocols. With this technique, mapping of both organic and inorganic components of bone, in addition to determining their distributions with high spatial resolution across a specimen, can be realized. We have employed Raman microscopy to investigate the early stages of mineralization in four different mouse calvarial systems: typical and atypical osteoblastic (bone forming) cell cultures and healthy and diseased bone tissue. These systems are commonly utilized as models for mineralization. The mineral deposited by osteoblast cultures grown atypically gives a Raman signal completely different to that observed in osteoblast cultures grown in the conventional manner. Similarly, Raman images of healthy and diseased bone tissue show differences in the relationship of the mineral and matrix environments. In this report, we compare the several differences between these four mineral environments, and discuss the chemistry of mineral maturation observed.

Effects of treatment protocols and subcutaneous implantation on bovine pericardium: a Raman spectroscopy study

Using Raman microspectroscopy, we have studied mineral deposition on bovine pericardia, fixed according to three different protocols and either implanted subcutaneously or not implanted (controls). A lightly carbonated apatitic phosphate mineral, similar to that found in bone tissue, was deposited on the surface of a glutaraldehyde-fixed, implanted pericardium. Implanted pericardia fixed in glutaraldehyde followed by treatment in either an 80% ethanol or a 5% octanol/40% ethanol solution did not mineralize on implantation. Collagen secondary structure changes were observed on glutaraldehyde fixation by monitoring the center of gravity of the amide I envelope. It is proposed that the decrease in the amide I center of gravity frequency for the glutaraldehyde-fixed tissue compared to the nonfixed tissue is due to an increase in nonreducible collagen cross-links (1660 cm(-1)) and a decrease in reducible cross-links (1690 cm(-1)). The amide I center of gravity in the glutaraldehyde/ethanol-fixed pericardium was higher than the glutaraldehyde-fixed tissue center of gravity. This increase in center of gravity could possibly be due to a decrease in hydrogen bonding within the collagen fibrils following the ethanol pretreatment. In addition, we found a secondary structure change to the pericardial collagen after implantation: an increase in the frequency of the center of gravity of amide I is indicative of an increase in cross-links.

Control of Teflon AF 2400 Permeability in a Liquid-Core Waveguide by an Ultra-Thin Crosslinked Polyamide Coating

In the last few years Teflon AF has emerged as the leading material for implementing waveguiding with an aqueous core because of its low refractive index (nD = 1.29). This low index should make it possible for very low limits of detection to be achieved in Teflon AF as a result of the ability to excite with laser light over an increased area. Detection limits have remained high, however, due in part to the porosity of the material. In this communication we report a significant reduction in the permeability of Teflon AF 2400 capillary walls with the deposition and subsequent treatment of polyelectrolyte multilayers. Alternating layers of polycations and polyanions on a bare Teflon AF surface are sufficient to reduce its permeability to small molecules such as methanol and benzene. Crosslinking and deprotonation of these multilayers further reduces permeability to less than 10% of the permeability value through uncoated TAF. As a consequence, detection limits are reduced. Evidence of these results is presented with gas chromatography-mass spectrometry (GC/MS) and Raman spectroscopic measurements.

Raman molecular imaging of tissue and cell samples using tunable multiconjugate filter

Raman spectroscopy is a powerful technique for rapid, non-invasive and reagentless analysis of materials, including biological cells and tissues. Raman Molecular Imaging combines high molecular information content Raman spectroscopy and digital full field imaging to enable the investigation of cells and tissues. We have conducted widefield imaging using a new class of birefringent liquid crystal tunable filter that provides high throughput over an extended wavelength range. This tool has been applied to investigate the linkage between reagentless spectral imaging in tissue and cells and standard reagent based approaches. In this report, we describe Raman imaging data on a clinical tissue sample and cultured cells. The results demonstrate the sensitivity of Raman Molecular Imaging and fluorescence spectral imaging to molecular differences in biological systems laying the foundation for the eventual use of this approach as a biological research and clinical diagnostic tool.

Abstract B079: Prospective study on the use of Raman molecular imaging to determine postoperative oncological outcomes in patients with prostate cancer: Analysis of a single center

Background: Prostate cancer (PCa) is the most common cancer in males and the second leading cause of cancer deaths. One of the most confounding problems faced by urologists is predicting the progression of PCa. Current prognostic tools for PCa rely solely on clinical and pathologic variables that cannot always accurately predict the progression of the disease. More accurate diagnostic tools to aid clinicians and patients to decide the course of treatment, even in the early stage of cancer, have long been sought. In a prospective study, we utilized Raman molecular imaging (RMI) to identify patients at risk for biochemical recurrence following a prostatectomy. RMI is a technology that combines the molecular chemical analysis capability of Raman spectroscopy with high-definition digital image visualization, enabling analysis of the molecular environment of prostate tissue by providing highly specific molecular imaging data. Objective: The aim of this study is to evaluate RMI as a methodology for predicting the outcome of PCa patients following radical prostatectomy. Methods: PCa patients who underwent prostatectomy were prospectively enrolled with informed consent. Surgeries were performed by two practiced surgeons in a single clinical center from 2009–2011, and 60 months’ follow-up time was recorded. Pathology data were reviewed by one genitourinary pathologist. Preoperative and postoperative data (preoperation prostate-specific antigen level [PSA], Nadir PSA, Gleason scores, pathologic stage, biochemical recurrence time) were recorded, and biochemical failure was defined as 2 consecutive increases in postoperative PSA (≥0.2 ng/mL). Patient identifiers were removed. Raman molecular images were collected from unstained prostatectomy tissue sections. Raman spectral data were extracted from epithelial and stromal regions, and multivariate statistical methodologies were applied to these spectra. Partial least squares discriminant analysis (PLS-DA) was employed in conjunction with leave-one-patient-out cross-validation to generate models based on the pathology data and RMI. Results: Initial data analysis was performed on a representative population of 38 PCa patients. In this population the mean (median) age at the time of diagnosis was 60 (61) years; the pathologic stage for 57.9% of the patients was T3 and for 42.1% of the patients T2. Nine patients (23.7%) progressed to biochemical failure in 60 months. Analysis of RMI performed on prostatectomy tissues from these patients indicates that the RMI data are distinguishable between those with biochemical failure (progressors) and those with no evidence of disease (NED). A PLS-DA model comprising 9 progressors and 29 NED patients differentiated between the two tissue classes with 89% sensitivity and 90% specificity, and with an area under the ROC curve of 0.87. In this model, classification accuracy was 90% and the p–value Analysis revealed most notable differentiation between progressors and NEDs when evaluating the epithelium and stroma as separate histologic elements. A PLS-DA model based on epithelial cells from 9 progressors and 10 NEDs discriminated between the two populations with 89% sensitivity and 100% specificity, with AUROC of 0.92 and a p-value Conclusion: RMI is a novel technique that shows promise for identifying patients at risk of progression by visualizing molecular information not seen using other current methods. In Gleason 7 disease, RMI indicates distinctive chemical differences in patients who had biochemical failure postoperatively. This preliminary work lays the foundation for the further study of RMI for evaluating prostate tissue and developing an assay that may impact clinicians and patients with PCa. Citation Format: Arash Samiei, Ralph Miller, Jeffrey Cohen, Heather Gomer, Shona Stewart, Patrick Treado. Prospective study on the use of Raman molecular imaging to determine postoperative oncological outcomes in patients with prostate cancer: Analysis of a single center [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B079.