Methods and instrumentation for raman characterization of bladder cancer tumor

High incidence and recurrence rates make bladder cancer the most common malignant tumor in the urinary system. Cystoscopy is the gold standard test used for diagnosis, nevertheless small flat tumors might be missed, and the procedure still represents discomfort to patients and high recurrence can result from of urethral injuries. During cystoscopy, suspicious tumors are detected through white light endoscopy and resected tissue is further examined by histopathology. after resection, the pathologist provides information on the differentiation of the cells and the penetration depth of the tumor in the tissue, known as grading and staging of tumor, respectively. During cystoscopy, information on tumor grading and morphological depth characterization can assist onsite diagnosis and significantly reduce the amount of unnecessarily resected tissue. Recently, new developments in optical imaging and spectroscopic approaches have been demonstrated to improve the results of standard techniques by providing real-time detection of macroscopic and microscopic biomedical information. Different applications to detect anomalies in tissues and cells based on the chemical composition and structure at the microscopic level have been successfully tested. There is, nevertheless, the need to cope with the demands for clinical translation. This doctoral thesis presents the investigations, clinical studies and approaches applied to filling the main open research questions when applying Raman spectroscopy as a diagnostic tool for bladder cancer tumor grading and general Raman spectroscopy-based oncological clinical studies

Assessment of shifted excitation Raman difference spectroscopy in highly fluorescent biological samples

Shifted excitation Raman difference spectroscopy (SERDS) can be used as an instrumental baseline correction technique to retrieve Raman bands in highly fluorescent samples. Genipin (GE) cross-linked equine pericardium (EP) was used as a model system since a blue pigment is formed upon cross-linking, which results in a strong fluorescent background in the Raman spectra. EP was cross-linked with 0.25% GE solution for 0.5 h, 2 h, 4 h, 6 h, 12 h, and 24 h, and compared with corresponding untreated EP. Raman spectra were collected with three different excitation wavelengths. For the assessment of the SERDS technique, the preprocessed SERDS spectra of two excitation wavelengths (784 nm-786 nm) were compared with the mathematical baseline-corrected Raman spectra at 785 nm excitation using extended multiplicative signal correction, rubberband, the sensitive nonlinear iterative peak and polynomial fitting algorithms. Whereas each baseline correction gave poor quality spectra beyond 6 h GE crosslinking with wave-like artefacts, the SERDS technique resulted in difference spectra, that gave superior reconstructed spectra with clear collagen and resonance enhanced GE pigment bands with lower standard deviation. Key for this progress was an advanced difference optimization approach that is described here. Furthermore, the results of the SERDS technique were independent of the intensity calibration because the system transfer response was compensated by calculating the difference spectrum. We conclude that this SERDS strategy can be transferred to Raman studies on biological and non-biological samples with a strong fluorescence background at 785 nm and also shorter excitation wavelengths which benefit from more intense scattering intensities and higher quantum efficiencies of CCD detectors. This journal i

New methodology to process shifted excitation Raman difference spectroscopy data : a case study of pollen classification

Shifted excitation Raman difference spectroscopy (SERDS) is a background correction method for Raman spectroscopy. Here, the difference spectra were directly used as input for SERDS-based classification after an optimization procedure to correct for photobleaching of the autofluorescence. Further processing included a principal component analysis to compensate for the reduced signal to noise ratio of the difference spectra and subsequent classification by linear discriminant analysis. As a case study 6,028 Raman spectra of single pollen originating from plants of eight different genera and four different growth habits were automatically recorded at excitation wavelengths 784 and 786 nm using a high-throughput screening Raman system. Different pollen were distinguished according to their growth habit, i.e. tree versus non-tree with an accuracy of 95.9%. Furthermore, all pollen were separated according to their genus, providing also insight into similarities based on their families. Classification results were compared using spectra reconstructed from the differences and raw spectra after state-of-art baseline correction as input. Similar sensitivities, specificities, accuracies and precisions were found for all spectra with moderately background. Advantages of SERDS are expected in scenarios where Raman spectra are affected by variations due to detector etaloning, ambient light, and high background

Wide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique

Wide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time.Comment: Accepted and Published by "Sensors" Journal, 19 pages, 8 figure

Raman Spectroscopy for In Vivo Medical Diagnosis

Raman spectroscopy is a noninvasive optical technique that can be used as an aid in diagnosing certain diseases and as an alternative to more invasive diagnostic techniques such as the biopsy. Due to these characteristics, Raman spectroscopy is also known as an optical biopsy technique. The success of Raman spectroscopy in biomedical applications is based on the fact that the molecular composition of healthy tissue is different from diseased tissue; also, several disease biomarkers can be identified in Raman spectra, which can be used to diagnose or monitor the progress of certain medical conditions. This chapter outlines an overview of the use of Raman spectroscopy for in vivo medical diagnostics and demonstrates the potential of this technique to address biomedical issues related to human health

FLIm and raman spectroscopy for investigating biochemical changes of bovine pericardium upon genipin cross-linking

Biomaterials used in tissue engineering and regenerative medicine applications benefit from longitudinal monitoring in a non-destructive manner. Label-free imaging based on fluorescence lifetime imaging (FLIm) and Raman spectroscopy were used to monitor the degree of genipin (GE) cross-linking of antigen-removed bovine pericardium (ARBP) at three incubation time points (0.5, 1.0, and 2.5 h). Fluorescence lifetime decreased and the emission spectrum redshifted compared to that of uncross-linked ARBP. The Raman signature of GE-ARBP was resonance-enhanced due to the GE cross-linker that generated new Raman bands at 1165, 1326, 1350, 1380, 1402, 1470, 1506, 1535, 1574, 1630, 1728, and 1741 cm-1. These were validated through density functional theory calculations as cross-linker-specific bands. A multivariate multiple regression model was developed to enhance the biochemical specificity of FLIm parameters fluorescence intensity ratio (R2 = 0.92) and lifetime (R2 = 0.94)) with Raman spectral results. FLIm and Raman spectroscopy detected biochemical changes occurring in the collagenous tissue during the cross-linking process that were characterized by the formation of a blue pigment which affected the tissue fluorescence and scattering properties. In conclusion, FLIm parameters and Raman spectroscopy were used to monitor the degree of cross-linking non-destructively. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Raman spectroscopic characterization and analysis of agricultural and biological systems

Technical progresses in the past two decades in instrumental design, laser and electronic technology, and computer-based data analysis have made Raman spectroscopy, a noninvasive, nondestructive optical molecular spectroscopic imaging technique, an attractive choice for analytical tasks. Raman spectroscopy provides chemical structural information at molecular level with minimal sample preparation in a quick, easy-to-operate and reproducible fashion. In recent years it has been applied more and more to the analysis and characterization of agricultural products and biological samples. This dissertation documents the innovative research in Raman spectroscopic characterization and analysis in both biomedical and agricultural systems that I have been working on throughout my PhD training. The biomedical research conducted was focused on glaucoma. Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of pathological changes and progression in glaucoma and other neuroretinal diseases, which is critical for the prevention of permanent structural damage and irreversible vision loss, remains a great challenge. In my research, the Raman spectra from canine retinal tissues were subjected to multivariate discriminant analysis with a support vector machine algorithm to differentiate disease tissues versus healthy tissues. The high classification accuracy suggests that Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue not only at late stage but also at early stage with high specificity. To expand the scope of application of Raman analysis, it was also applied to characterize agricultural and food materials. More specifically, Raman spectroscopy was applied to analyze meat. Existing objective methods (e.g., mechanical stress/strain analysis, near infrared spectroscopy) to predict sensory attributes of pork in general do not yield satisfactory correlation to panel evaluations. Raman spectroscopic methodology was investigated in this study to evaluate and predict tenderness, juiciness and chewiness of fresh, uncooked pork loins from 169 pigs. The method developed in this thesis yielded good prediction of sensory attributes such as tenderness and chewiness, and it has the potential to become a rapid objective assay for tenderness and chewiness of pork products that may find practical applications in pork industry. In addition, a Raman spectroscopic screening method in conjunction with discriminant modeling was developed for rapid evaluation of boar taint level in pork. Through the research demonstrated in this dissertation, Raman spectroscopy has been shown to have great potential to address analytical needs in new fields with great potential for innovative applications

A Study of Raman Spectroscopy as a Clinical Diagnostic Tool for the Detection of Lynch Syndrome/Hereditary NonPolyposis Colorectal Cancer (HNPCC)

Lynch syndrome also known as hereditary non-polyposis colorectal cancer (HNPCC) is a highly penetrant hereditary form of colorectal cancer that accounts for approximately 3% of all cases. It is caused by mutations in DNA mismatch repair resulting in accelerated adenoma to carcinoma progression. The current clinical guidelines used to identify Lynch Syndrome (LS) are known to be too stringent resulting in overall underdiagnoses. Raman spectroscopy is a powerful analytical tool used to probe the molecular vibrations of a sample to provide a unique chemical fingerprint. The potential of using Raman as a diagnostic tool for discriminating LS from sporadic adenocarcinoma is explored within this thesis. A number of experimental parameters were initially optimized for use with formalin fixed paraffin embedded colonic tissue (FFPE). This has resulted in the development of a novel cost-effective backing substrate shown to be superior to the conventionally used calcium fluoride (CaF2). This substrate is a form of silanized super mirror stainless steel that was found to have a much lower Raman background, enhanced Raman signal and complete paraffin removal from FFPE tissues. Performance of the novel substrate was compared against CaF2 by acquiring large high resolution Raman maps from FFPE rat and human colonic tissue. All of the major histological features were discerned from steel mounted tissue with the benefit of clear lipid signals without paraffin obstruction. Biochemical signals were comparable to those obtained on CaF2 with no detectable irregularities. By using principal component analysis to reduce the dimensionality of the dataset it was then possible to use linear discriminant analysis to build a classification model for the discrimination of normal colonic tissue (n=10) from two pathological groups: LS (n=10) and sporadic adenocarcinoma (n=10). Using leaveone-map-out cross-validation of the model classifier has shown that LS was predicted with a sensitivity of 63% and a specificity of 89% - values that are competitive with classification techniques applied routinely in clinical practice

Multimodal optical spectroscopy for application in the life sciences

Many optical modalities are being investigated, applied, and further developed for non-invasive analysis and sensing in the life sciences. Often, the combination of two or more modalities is required for in depth analysis because of the complexity of the study objects and questions in this field. The work presents multimodal sensing concepts for applications ranging from non-invasive quantification of biomolecules in the living organism to supporting medical diagnosis showing the combined capabilities of Raman spectroscopy, optical coherence tomography, and optoacoustic