Location of PRODAN in lipid layer of HDL particle: a Raman study

FT Raman spectroscopy has been applied to determine the location of PRODAN within HDL and to investigate its influence on the structure of the particle. The complex spectra of HDL and HDL labeled with PRODAN were divided into three regions according to the wave numbers, and adherent spectra were compared separately. Additionally, recorded spectra of protein and lipid fractions of HDL were used as a support for the assignment of particular vibrations in intact particles. In high frequency region, the shift in vibrational frequencies of CH3 groups but almost negligible shift of CH2 groups suggests that PRODAN is situated at the water/lipid interface in the vicinity of the protein. The statement is supported by the observed influence of PRODAN on particular lipid vibrations of phospholipids head-groups. In the fingerprint region, the influence of PRODAN is observed as the slight change in beta-strand secondary structure of apolipoprotein and strongly reduced vibrations of the acyl chain in lipids. That additionally confirms that PRODAN mainly interacts with the lipid domain of the particle. In the low frequency region, the lack of change in Tyr Fermi resonance doublet and only slight differences in the pattern of CS and SS stretching vibrations in labeled HDL confirms that PRODAN has no influence on structure of apolipoprotein embedded in lipid domain. The main conclusions drawn from the vibrational spectra of HDL with and without PRODAN clearly confirm that PRODAN induces negligible changes in HDL structure and hence is reliable fluorescent label for the structural analysis

Segmentation of Confocal Raman Microspectroscopic Imaging Data Using Edge-Preserving Denoising and Clustering

Over the past decade, confocal Raman microspectroscopic (CRM) imaging has matured into a useful analytical tool to obtain spatially resolved chemical information on the molecular composition of biological samples and has found its way into histopathology, cytology, and microbiology. A CRM imaging data set is a hyperspectral image in which Raman intensities are represented as a function of three coordinates: a spectral coordinate λ encoding the wavelength and two spatial coordinates x and y. Understanding CRM imaging data is challenging because of its complexity, size, and moderate signal-to-noise ratio. Spatial segmentation of CRM imaging data is a way to reveal regions of interest and is traditionally performed using nonsupervised clustering which relies on spectral domain-only information with the main drawback being the high sensitivity to noise. We present a new pipeline for spatial segmentation of CRM imaging data which combines preprocessing in the spectral and spatial domains with k-means clustering. Its core is the preprocessing routine in the spatial domain, edge-preserving denoising (EPD), which exploits the spatial relationships between Raman intensities acquired at neighboring pixels. Additionally, we propose to use both spatial correlation to identify Raman spectral features colocalized with defined spatial regions and confidence maps to assess the quality of spatial segmentation. For CRM data acquired from midsagittal Syrian hamster (Mesocricetus auratus) brain cryosections, we show how our pipeline benefits from the complex spatial-spectral relationships inherent in the CRM imaging data. EPD significantly improves the quality of spatial segmentation that allows us to extract the underlying structural and compositional information contained in the Raman microspectra