KEYWORDS: Raman spectroscopy, Cameras, Charge-coupled devices, Raman scattering, Signal to noise ratio, Computer simulations, Electrons, Principal component analysis, Data modeling
Raman micro-spectroscopy is an optoelectronic technique that can be used to evaluate the chemical composition of biological samples and has been shown to be a powerful diagnostic tool for the investigation of various cancer related diseases including bladder, breast, and cervical cancer. Raman scattering is an inherently weak process with approximately 1 in 107 photons undergoing scattering and for this reason, noise from the recording system can have a significant impact on the quality of the signal, and its suitability for diagnostic classification. The main sources of noise in the recorded signal are shot noise, CCD dark current, and CCD readout noise. Shot noise results from the low signal photon count while dark current results from thermally generated electrons in the semiconductor pixels. Both of these noise sources are time dependent; readout noise is time independent but is inherent in each individual recording and results in the fundamental limit of measurement, arising from the internal electronics of the camera. In this paper, each of the aforementioned noise sources are analysed in isolation, and used to experimentally validate a mathematical model. This model is then used to simulate spectra that might be acquired under various experimental conditions including the use of different cameras, different source wavelength, and power etc. Simulated noisy datasets of T24 and RT112 cell line spectra are generated based on true cell Raman spectrum irradiance values (recorded using very long exposure times) and the addition of simulated noise. These datasets are then input to multivariate classification using Principal Components Analysis and Linear Discriminant Analysis. This method enables an investigation into the effect of noise on the sensitivity and specificity of Raman based classification under various experimental conditions and using different equipment.
Here we report a preliminary study based on the application of Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) to investigate the compositional differences between exosomes derived from ovarian carcinoma cells (cell line A2780) grown in normoxia (normal O2 conditions) and hypoxia (1% O2 conditions). Exosomes are integral to cell signalling, and are of interest in the study of how cells communicate within their environment. We are particularly interested in identifying whether hypoxia induced senescent cells can communi- cate via exosomes with neighbouring tumour cells, thereby causing them to become senescent and therefore radio and chemo resistant. With this goal in mind, we performed a preliminary study on the application of Raman spectroscopy and SERS to analyse the biomolecular fingerprint of both groups of exosomes and to investigate whether there exists a different biomolecular composition associated with exosomes derived from hypoxic cells in comparison to those from normoxic cells. We also applied multivariate statistical techniques for the classification of both groups of exosomes.
Raman microspectroscopy can be applied to the urinary bladder for highly accurate classification and diagnosis of bladder cancer. This technique can be applied in vitro to bladder epithelial cells obtained from urine cytology or in vivo as an optical biopsy" to provide results in real-time with higher sensitivity and specificity than current clinical methods. However, there exists a high degree of variability across experimental parameters which need to be standardised before this technique can be utilized in an everyday clinical environment. In this study, we investigate different laser wavelengths (473 nm and 532 nm), sample substrates (glass, fused silica and calcium fluoride) and multivariate statistical methods in order to gain insight into how these various experimental parameters impact on the sensitivity and specificity of Raman cytology.
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