Analytical performance of Raman spectroscopy in assaying biochemical components in human serum

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

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.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

Carotenoids contribution in rapid diagnosis of multiple sclerosis by Raman spectroscopy

Rapid and accurate diagnosis of any illness determines the success of treatment. The same applies to multiple sclerosis (MS), chronic, inflammatory, and neurodegenerative diseases (ND) of the central nervous system (CNS). Unfortunately, the definitive diagnosis of MS is prolonged and involves mainly clinical symptoms observation and magnetic resonance imaging (MRI) of the CNS. However, as we previously reported, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy shed new light on the minimally invasive, label-free, and rapid diagnosis of this illness through blood fraction. Herein we introduce Raman spectroscopy coupled with chemometric analysis to provide more detailed information about the biochemical changes behind MS. This pilot study demonstrates that mentioned combination may provide a new diagnostic biomarker and bring closer to rapid MS diagnosis. It has been shown that Raman spectroscopy provides lipid and carotenoid molecules as useful biomarkers which may be applied for both diagnosis and treatment monitoring.

Electroporation-Assisted Surface-Enhanced Raman Detection for Long-Term, Label-Free, and Noninvasive Molecular Profiling of Live Single Cells

Molecule characterization of live single cells is greatly important in disease diagnoses and personalized treatments. Conventional molecule detection methods, such as mass spectrography, gene sequencing, or immunofluorescence, are usually destructive or labeled and unable to monitor the dynamic change of live cellular molecules. Herein, we propose an electroporation-assisted surface-enhanced Raman scattering (EP-SERS) method using a microchip to implement label-free, noninvasive, and continuous detections of the molecules of live single cells. The microchip containing microelectrodes with nanostructured EP-SERS probes has a multifunction of cell positioning, electroporation, and SERS detection. The EP-SERS method capably detects both the intracellular and extracellular molecules of live single cells without losing cell viability so as to enable long-term monitoring of the molecular pathological process in situ. We detect the molecules of single cells for two breast cancer cell lines with different malignancies (MCF-7 and MDA-MB-231), one liver cancer cell line (Huh-7), and one normal cell line (293T) using the EP-SERS method and classify these cell types to achieve high accuracies of 91.4-98.3% using their SERS spectra. Furthermore, 24 h continuous monitoring of the heterogeneous molecular responses of different cancer cell lines under doxorubicin treatment is successfully implemented using the EP-SERS method. This work provides a long-term, label-free, and biocompatible approach to simultaneously detect intracellular and extracellular molecules of live single cells on a chip, which would facilitate research and applications of cancer diagnoses and personalized treatments.

Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.

The Potential Applications of Raman Spectroscopy in Kidney Diseases

Raman spectroscopy (RS) is a spectroscopic technique based on the inelastic interaction of incident electromagnetic radiation (from a laser beam) with a polarizable molecule, which, when scattered, carries information from molecular vibrational energy (the Raman effect). RS detects biochemical changes in biological samples at the molecular level, making it an effective analytical technique for disease diagnosis and prognosis. It outperforms conventional sample preservation techniques by requiring no chemical reagents, reducing analysis time even at low concentrations, and working in the presence of interfering agents or solvents. Because routinely utilized biomarkers for kidney disease have limitations, there is considerable interest in the potential use of RS. RS may identify and quantify urinary and blood biochemical components, with results comparable to reference methods in nephrology.

Diagnosing COVID-19 in human serum using Raman spectroscopy

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Diagnosing COVID-19 in human sera with detected immunoglobulins IgM and IgG by means of Raman spectroscopy

The severe COVID-19 pandemic requires the development of novel, rapid, accurate, and label-free techniques that facilitate the detection and discrimination of SARS-CoV-2 infected subjects. Raman spectroscopy has been used to diagnose COVID-19 in serum samples of suspected patients without clinical symptoms of COVID-19 but presented positive immunoglobulins M and G (IgM and IgG) assays versus Control (negative IgM and IgG). A dispersive Raman spectrometer (830 nm, 350 mW) was employed, and triplicate spectra were obtained. A total of 278 spectra were used from 94 serum samples (54 Control and 40 COVID-19). The main spectral differences between the positive IgM and IgG versus Control, evaluated by principal component analysis (PCA), were features assigned to proteins including albumin (lower in the group COVID-19 and in the group IgM/IgG and IgG positive) and features assigned to lipids, phospholipids, and carotenoids (higher the group COVID-19 and in the group IgM/IgG positive). Features referred to nucleic acids, tryptophan, and immunoglobulins were also seen (higher the group COVID-19). A discriminant model based on partial least squares regression (PLS-DA) found sensitivity of 84.0%, specificity of 95.0%, and accuracy of 90.3% for discriminating positive Ig groups versus Control. When considering individual Ig group versus Control, it was found sensitivity of 77.3%, specificity of 97.5%, and accuracy of 88.8%. The higher classification error was found for the IgM group (no success classification). Raman spectroscopy may become a technique of choice for rapid serological evaluation aiming COVID-19 diagnosis, mainly detecting the presence of IgM/IgG and IgG after COVID-19 infection.