Detecting alterations of glucose and lipid components in human serum by near-infrared Raman spectroscopy

Comparison of different Raman spectrometer models in the quantification of blood serum analytes

The analytical performance of three commercial Raman spectrometers in determining the concentrations of analytes associated with diagnosing metabolic, cardiovascular, and renal disorders has been compared. Two portable and one benchtop spectrometers were tested to predict the serum concentrations of triglycerides (TRI), cholesterol (COL), high-density cholesterol, creatinine, urea, and glucose in 193 serum samples using partial least squares (PLS) regression and PLS discriminant analysis, the latter for classifying samples as either altered or within reference values. Strong correlations (r > 0.81) were obtained for TRI and COL analytes using the benchtop and one of the portable spectrometers, and the classification accuracy rates exceeded 90%, suggesting potential for use in clinical screening. Adding a glucose solution improved the correlation and the root mean square error for TRI and COL analytes; however, it did not enhance the correlation or error for the glucose analyte. Raman spectroscopy showed potential to support routine laboratory activities and may have applications in clinical screening.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Understanding the role of membrane cholesterol upon Epstein Barr virus infection in astroglial cells

Background

EBV infection has long been postulated to trigger multiple sclerosis (MS) and anti-EBV antibodies showed a consistent presence in MS patients. Previous reports from our group have shown that the EBV infects different brain cells. Entry of the virus in neuronal cells is assisted by several host factors including membrane cholesterol. By using an inhibitor, methyl-β-cyclodextrin (MβCD), we evaluated the role of membrane cholesterol in EBV infection and pathogenesis.

Methodology

The membrane cholesterol depleted cells were infected with EBV and its latent genes expression were assessed. Further, EBV-mediated downstream signalling molecules namely STAT3, RIP, NF-kB and TNF-α levels was checked at protein level along with spatial (periphery and nucleus) and temporal changes in biomolecular fingerprints with Raman microspectroscopy (RS).

Results

Upon treatment with MβCD, lmp1 and lmp2a suggested significant downregulation compared to EBV infection. Downstream molecules like STAT3 and RIP, exhibited a decrease in protein levels temporally upon exposure to MβCD while NF-kB levels were found to be increased. Further, the intensity of the Raman spectra exhibited an increase in triglycerides and fatty acids in the cytoplasm of EBV-infected LN-229 cells compared to MβCD+EBV. Likewise, the Raman peak width of cholesterol, lipid and fatty acids were found to be reduced in EBV-infected samples indicates elevation in the cholesterol specific moieties. In contrast, an opposite pattern was observed in the nucleus. Moreover, the ingenuity pathway analysis revealed protein molecules such as VLDLR, MBP and APP that are associated with altered profile of cholesterol, fatty acids and triglycerides with infection-related CNS disorders.

Conclusion

Taken together, our results underline the important role of membrane cholesterol over EBV entry/pathogenesis in astroglia cells which further trigger/exacerbate virus-associated neuropathologies. These results likely to aid into the prognosis of neurological disease like MS.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C

BACKGROUND

Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV).

OBJECTIVES

To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1^st principle component. By analyzing data using partial least square discriminate analysis, differentiation of two disease classes was considered more valid with sensitivity, specificity and accuracy value of 96%, 94% and 98% respectively. Value of area under curve (AUROC) was 0.7527.

CONCLUSION

SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.

A Preliminary Study of FTIR Spectroscopy as a Potential Non-Invasive Screening Tool for Pediatric Precursor B Lymphoblastic Leukemia

Early detection of the most common pediatric neoplasm, B-cell precursor lymphoblastic leukemia (BCP-ALL), is challenging and requires invasive bone marrow biopsies. The purpose of this study was to establish new biomarkers for early screening to detect pediatric leukemia. In this small cohort study, Fourier transform infrared (FTIR) spectra were obtained from blood sera of 10 patients with BCP-ALL and were compared with the control samples from 10 children with some conditions other than neoplasm. Using various analytical approaches, including a new physical model, some significant differences were observable. The most important include: the different peak area ratio 2965/1645 cm⁻¹ (p = 0.002); the lower average percentage of both β-sheet and β-turn protein structures in the sera of BCP-ALL patients (p = 0.03); an AdaBoost-based predictive model for classifying healthy vs. BCP-ALL patients with 85% accuracy; and the phase shift of the first derivative in the spectral range 1050–1042 cm⁻¹ correlating with white blood cell (WBC) and blast cell count in BCP-ALL patients contrary to the samples obtained from healthy controls. Although verification in larger groups of patients will be necessary, these promising results suggest that FTIR spectroscopy may have future potential for the early screening of BCP-ALL.

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

Chronic non-infectious diseases are important to research as they are the main causes of death in Brazil and worldwide. One very important chronic non-infectious disease is cardiovascular disease, whose risk factors (diabetes, dyslipidemia, and renal failure) can be detected through assessments of serum biochemical components. The objective of this study was to evaluate the analytical performance of Raman spectroscopy for analysis of lipid profile (total cholesterol, triglycerides, and HDL cholesterol), non-protein nitrogenous compounds (urea and creatinine), and glucose in 242 human serum samples. Models to discriminate and quantify the samples were developed using the predicted concentration by quantitative regression model based on partial least squares (PLS). The analytical error for the "leave-one-out" cross-validation based on the predicted PLS concentration was 10.5 mg/dL for total cholesterol, 21.4 mg/dL for triglyceride, 13.0 mg/dL for HDL cholesterol, 4.9 mg/dL for urea, 0.21 mg/dL for creatinine, and 15.4 mg/dL for glucose. The Kappa coefficient indicate very good agreement for cholesterol (0.83), good for triglyceride (0.77), urea (0.70) and creatinine (0.66), and fair for HDL cholesterol (0.38) and glucose (0.30). The results of the analytical performance demonstrated that Raman spectroscopy can be considered an important methodology to screen the population, especially for serum triglycerides and cholesterol.

Temporal In Vitro Raman Spectroscopy for Monitoring Replication Kinetics of Epstein-Barr Virus Infection in Glial Cells

Raman spectroscopy can be used as a tool to study virus entry and pathogen-driven manipulation of the host efficiently. To date, Epstein-Barr virus (EBV) entry and altered biochemistry of the glial cell upon infection are elusive. In this study, we detected biomolecular changes in human glial cells, namely, HMC-3 (microglia) and U-87 MG (astrocytes), at two variable cellular locations (nucleus and periphery) by Raman spectroscopy post-EBV infection at different time points. Two possible phenomena, one attributed to the response of the cell to viral attachment and invasion and the other involved in duplication of the virus followed by egress from the host cell, are investigated. These changes corresponded to unique Raman spectra associated with specific biomolecules in the infected and the uninfected cells. The Raman signals from the nucleus and periphery of the cell also varied, indicating differential biochemistry and signaling processes involved in infection progression at these locations. Molecules such as cholesterol, glucose, hyaluronan, phenylalanine, phosphoinositide, etc. are associated with the alterations in the cellular biochemical homeostasis. These molecules are mainly responsible for cellular processes such as lipid transport, cell proliferation, differentiation, and apoptosis in the cells. Raman signatures of these molecules at distinct time points of infection indicated their periodic involvement, depending on the stage of virus infection. Therefore, it is possible to discern the details of variability in EBV infection progression in glial cells at the biomolecular level using time-dependent in vitro Raman scattering.

Optical screening of hepatitis-B infected blood sera using optical technique and neural network classifier

In this study we demonstrate the analysis of biochemical changes in the human blood sera infected with Hepatitis B virus (HBV) using Raman spectroscopy. In total, 120 diseased blood samples and 170 healthy blood samples, collected from Pakistan Atomic Energy Commission (PAEC) general hospital, were analyzed. Spectra from each sample of both groups were collected in the spectral range 400-1700 cm^-1. Careful spectral analyses demonstrated significant spectral variations (p < 0.0001) in the HBV infected individuals as compared to the normal ones. The spectral variations presumably occur because of the variations in the concentration of important biomolecules. Variations in spectral signatures were further exploited by using a neural network classifier towards machine-assisted classification of the two groups. Evaluation metrics of the classifier showed the diagnostic accuracy of (0.993), sensitivity ( = 0.992), specificity ( = 0.994), positive predictive value ( = 0.992) and negative predictive value ( = 0.994). The observed variations in the molecular concentration may be important markers of the hepatic performance and can be used in the diagnosis and machine-assisted classification of HBV infection.

Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera

This study investigated the potential of Raman spectroscopy, to differentiate between two different types of human pathological conditions with some symptom similarity; typhoid and dengue. Raman spectra were recorded from 20 Salmonella Typhi infected and 22 dengue virus (DENV) infected sera samples using laser wavelength 785 nm. When Raman spectrum (540 to 1830 cm^-1) of serum samples was compared it showed significant variations. Twelve characteristic Raman bands at 562, 649, 716, 780, 838, 1099, 1144, 1156, 1260, 1386, 1556 and 1746 cm^-1 were recorded specifically in case of the typhoid infected samples, whereas four Raman bands at 756, 1218, 1672 and 1686 cm^-1 were found specifically in the DENV infected samples. For statistical differentiation principal component analysis (PCA) and linear discriminant analysis (LDA) was applied which successfully separated two sets of data. These disease specific Raman signatures may be labeled as biomarkers for identification as well as differential diagnosis of typhoid and dengue infection. Hence we propose that Raman spectroscopy has the potential to not only identify but also to differentiate between different pathological conditions, having some similar symptoms like high grade fever in this case.

Analysis of bodily fluids using vibrational spectroscopy: a direct comparison of Raman scattering and infrared absorption techniques for the case of glucose in blood serum

Analysis of biomarkers present in the blood stream can potentially deliver crucial information on patient health and indicate the presence of numerous pathologies.

Reliable and quantitative SERS detection of dopamine levels in human blood plasma using a plasmonic Au/Ag nanocluster substrate

Accurate and rapid blood-based detection of dopamine levels can aid in the diagnosis and monitoring of diseases related to dopaminergic dysfunction. For the sensitive detection of dopamine levels in human blood plasma (i.e., plasma dopamine levels), a silver-plated Au bimetallic nanocluster (so called plasmonic Au/Ag nanocluster) was prepared as a surface-enhanced Raman scattering (SERS) substrate by the combination of electrodeposition and electroless plating methods. The plasmonic effect of the Au/Ag nanocluster substrate was optimized by controlling the particle morphology, packing density, and interparticle distance, showing the best performance in its SERS activity. The lowest detection limit of dopamine was ∼10^-11 M. A linear standard curve was obtained by plotting the log-scale of dopamine concentration (log C) versus Raman intensity at 1152 cm^-1. The optimized SERS substrate quantified the plasma dopamine levels of patients with antipsychotic drug-induced Parkinsonism (n = 15) as 3.24 × 10^-9 M and healthy control subjects (n = 15) as 2.31 × 10^-8 M. Patients with drug-induced Parkinsonism had ∼86% lower plasma dopamine concentration than healthy subjects (two-tailed p-value = 0.000002), indicating a clear separation between the groups. Our study provides the first report on the quantitative SERS detection of dopamine levels in human blood plasma with Parkinsonism. The results highlight the potential clinical utility of the optimized SERS technique in screening clinical populations with dopaminergic dysfunction, i.e., differentiating between healthy subjects and patients with Parkinsonism.

Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220-1275, and 1524 cm^-1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

Comparison of FTIR-ATR and Raman spectroscopy in determination of VLDL triglycerides in blood serum with PLS regression

Hypertriglyceridemia, related with triglyceride (TG) in plasma above 1.7mmol/L is one of the cardiovascular risk factors. Very low density lipoproteins (VLDL) are the main TG carriers. Despite being time consuming, demanding well-qualified staff and expensive instrumentation, ultracentrifugation technique still remains the gold standard for the VLDL isolation. Therefore faster and simpler method of VLDL-TG determination is needed. Vibrational spectroscopy, including FT-IR and Raman, is widely used technique in lipid and protein research. The aim of this study was assessment of Raman and FT-IR spectroscopy in determination of VLDL-TG directly in serum with the isolation step omitted. TG concentration in serum and in ultracentrifugated VLDL fractions from 32 patients were measured with reference colorimetric method. FT-IR and Raman spectra of VLDL and serum samples were acquired. Partial least square (PLS) regression was used for calibration and leave-one-out cross validation. Our results confirmed possibility of reagent-free determination of VLDL-TG directly in serum with both Raman and FT-IR spectroscopy. Quantitative VLDL testing by FT-IR and/or Raman spectroscopy applied directly to maternal serum seems to be promising screening test to identify women with increased risk of adverse pregnancy outcomes and patient friendly method of choice based on ease of performance, accuracy and efficiency.