Fatty Acid-Binding Proteins Identification during the Evolution of Metabolic Syndrome: A Raman Spectroscopy-Based Approach.

Excess fat in abdominal deposits is a risk factor for multiple conditions, including metabolic syndrome (MetS); lipid metabolism plays an essential role in these pathologies; fatty acid-binding proteins (FABPs) are dedicated to the cytosolic transport of fat. FABP4, whose primary source is adipose tissue, is released into the circulation, acting as an adipokine, while FABP5 also accompanies the adverse effects of MetS. FABP4 and 5 are potential biomarkers of MetS, but their behavior during syndrome evolution has not been determined. Raman spectroscopy has been applied as an alternative method to disease biomarker detection. In this work, we detected spectral changes related to FABP4 and 5 in the serum at different points of time, using an animal model of a high-fat diet-induced MetS. FABP4 and 5 spectral changes show a contribution during the evolution of MetS, which indicates alteration to a molecular level that predisposes to established MetS. These findings place FABPs as potential biomarkers of MetS and Raman spectroscopy as an alternative method for MetS assessment.

Raman Spectroscopy Study of Skin Biopsies from Patients with Parkinson's Disease: Trends in Alpha-Synuclein Aggregation from the Amide I Region.

Parkinson's disease (PD) is one of the most common neurological pathologies with a high prevalence worldwide. PD is characterized by Lewy bodies, whose major component is the aggregates of α-synuclein (αSyn) protein. Interestingly, recent works have demonstrated that skin biopsy studies are a promising diagnostic tool for evaluating α-synucleinopathies. In this sense, this work focuses on the detection of αSyn in skin biopsies employing Raman spectroscopy, using three different approaches: (i) the in vitro Raman spectrum of α-synuclein, (ii) the ex vivo Raman spectra of human skin biopsies from healthy and Parkinson's disease patients, and (iii) theoretical calculations of the Raman spectra obtained from different model αSyn fragments using density functional theory (DFT). Significant differences in the intensity and location of Raman active frequencies in the amide I region were found when comparing healthy and PD subjects related to α-synuclein conformational changes and variations in their aggregation behavior. In samples from healthy patients, we identified well-known Raman peaks at 1655, 1664, and 1680 cm-1 associated with the normal state of the protein. In PD subjects, shifted Raman bands and intensity variations were found at 1650, 1670, and 1687 cm-1 associated with aggregated forms of the protein. DFT calculations reveal that the shape of the amide I Raman peak in model αSyn fragments strongly depends on the degree of aggregation. Sizable frequency shifts and intensity variations are found within the highly relevant 1600-1700 cm-1 domain, revealing the sensitivity of the amide I Raman band to the changes in the local atomic environment. Interestingly, we obtain that the presence of surrounding waters also affects the structure of the amide I band, leading to the appearance of new peaks on the low-frequency side and a notable broadening of the Raman spectra. These results strongly suggest that, through Raman spectroscopy, it is possible to infer the presence of aggregated forms of αSyn in skin biopsies, a result that could have important implications for understanding α-synuclein related diseases.

Raman Spectroscopy for Adipose Tissue Assessment in Rat Models of Obesity and Type 1 Diabetes.

Adipose tissue presents structural and functional changes in obesity and type 1 diabetes mellitus (T1DM). In obesity, the size and number of adipocytes and adipokine secretion increases. In T1DM, a loss of adipose tissue suggests changes in the metabolic activity of this tissue. A significant challenge is to find alternative noninvasive methods to evaluate molecular changes in adipose tissue related to obesity and T1DM. Recently, Raman spectroscopy and chemometrics techniques have emerged as a tool for biological tissue analysis. In this work, we propose the use of Raman spectroscopy to characterize spectral differences in adipose tissue from different rat groups (control, obese, and T1DM). The Raman spectra were analyzed using direct band analysis, ratiometric analysis, and chemometric methods (principal component analysis (PCA) and support vector machines (SVMs)). We found that the Raman spectra of obese rats showed significant spectral differences compared to control and diabetic groups related to fatty acids Raman bands. Also, the obese group has a significant decrease in the degree of unsaturation of lipids. The PCA-SVM models showed classification performance ranging from 71.43% to 71.79% accuracy for brown and white adipose tissue samples, respectively. In conclusion, the results demonstrate that Raman spectroscopy can be used as a nondestructive method to assess adipose tissue according to a metabolic condition.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA (ρ>0.91) outperforms VRA (ρ>0.86) for moderate to high noise levels whereas mVRA outperformed EMD (ρ>0.90) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (ρ>0.94). For Raman spectra of synthetic material, good performance of the three methods (ρ=0.99 for VRA, ρ=0.93 for EMD, and ρ=0.99 for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results (ρ=0.96 for VRA, ρ=0.85 for EMD, and ρ=0.91 for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.