Jinyong Lin

Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening

Surface-enhanced Raman scattering (SERS) spectra of serum proteins purified from human serum samples were employed to detect colorectal cancer. Acetic acid as a new aggregating agent was introduced to increase the magnitude of the SERS enhancement. High-quality SERS spectra of serum proteins were acquired from 103 cancer patients and 103 healthy volunteers. Tentative assignments of SERS bands reflect that some specific biomolecular contents and protein secondary structures change with colorectal cancer progression. Principal component analysis combined with linear discriminant analysis was used to assess the capability of this approach for identifying colorectal cancer, yielding diagnostic accuracies of 100% (sensitivity: 100%; specificity: 100%) based on albumin SERS spectroscopy and 99.5% (sensitivity: 100%; specificity: 99%) based on globulin SERS spectroscopy, respectively. A partial least squares (PLS) approach was introduced to develop diagnostic models. An albumin PLS model successfully predicted the unidentified subjects with a diagnostic accuracy of 93.5% (sensitivity: 95.6%; specificity: 91.3%) and the globulin PLS model gave a diagnostic accuracy of 93.5% (sensitivity: 91.3%; specificity: 95.6%). These results suggest that serum protein SERS spectroscopy can be a sensitive and clinically powerful means for colorectal cancer detection.

A noninvasive cancer detection strategy based on gold nanoparticle surface-enhanced raman spectroscopy of urinary modified nucleosides isolated by affinity chromatography.

The search for tumor biomarkers in the urine for cancer diagnosis is currently a hot topic in clinical oncology, with potential for cancer screening and diagnosis. Modified nucleosides excreted through the urine are considered to be a general tumor marker for various cancer types. Herein, we explore a new method that utilizes surface-enhanced Raman scattering (SERS) spectroscopy to obtain a complete biochemical profile of urinary modified nucleosides. In our method, modified nucleosides are first isolated from urine sample utilizing the excellent separation ability of affinity chromatography; then supplemented with gold (Au) nanoparticles as substrate for SERS spectroscopy analysis. The obtained SERS spectra present rich diagnostic and fingerprinting type signatures of urinary modified nucleosides. The utility of this new method in cancer detection was evaluated by analyzing urine samples from three groups of subjects: nasopharyngeal cancer patients (n=62), esophageal cancer patients (n=55), and healthy volunteers (n=52). Partial least squares and linear discriminant analysis (PLS-DA) were used to analyze and classify the SERS spectra of urinary modified nucleosides from nasopharyngeal cancer, esophageal cancer, and the normal group, achieving diagnostic sensitivities of 95.2%, 90.9% and 98.1% and specificities of 97.2%, 98.2% and 95.7%, respectively. These results demonstrated great potential of this novel method for non-invasive and label-free cancer detection and screening.

RAMAN SPECTROSCOPY OF HUMAN HEMOGLOBIN FOR DIABETES DETECTION

Glycated hemoglobin (HbA1c) has been increasingly accepted as the gold standard for diabetes monitoring. In this study, Raman spectroscopy was tentatively employed for human hemoglobin (Hb) biochemical analysis aimed at developing a simple blood test for diabetes monitoring. Raman spectroscopy measurements were performed on hemoglobin samples of patients (n = 39) with confirmed diabetes and healthy volunteers (n = 37). The tentative assignments of the measured Raman bands were performed to compare the difference between these two groups. Meanwhile, principal component analysis (PCA) combined with linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification between normal controls and patients with diabetes. As a result, the spectral features of these two groups demonstrated two distinct clusters with a sensitivity and specificity of 92.3% and 73%, respectively. Then the effectiveness of the diagnostic algorithm based on PCA-LDA technique was confirmed by receiver operating characteristic (ROC) curve. The area under the ROC curve was 0.92, indicating a good diagnostic result. In summary, our preliminary results demonstrate that proposing Raman spectroscopy can provide a significant potential for the noninvasive detection of diabetes.

Gold nanoaggregates for probing single-living cell based on surface-enhanced Raman spectroscopy

Abstract. Gold nanoparticles are delivered into living cells by transient electroporation method to obtain intracellular surface-enhanced Raman spectroscopy (SERS). The subcellular localization of gold nanoparticles is characterized by transmission electron microscopy, and the forming large gold nanoaggregates are mostly found in the cytoplasm. The SERS detection of cells indicates that this kind of gold nanostructures induces a high signal enhancement of cellular chemical compositions, in addition to less cellular toxicity than that of silver nanoparticles. These results demonstrate that rapid incorporation of gold nanoparticles by electroporation into cells has great potential applications in the studies of cell biology and biomedicine.

Erythrocyte membrane analysis for type II diabetes detection using Raman spectroscopy in high-wavenumber region

Raman spectroscopy was employed to detect lipid variation occurring in type II diabetic erythrocyte membrane (EM) without using exogenous reagents. In high-wavenumber (HW) region, significant Raman spectral differences between diabetic and normal EM are observed at 2850, 2873, 2885, 2935, and 2965 cm−1, which are mainly related to lipid in EM. Based on principal component analysis, the diagnostic accuracy of HW region for diabetes detection is 98.8%, which is much higher than that of low-wavenumber region (82.9%). The results suggest that EM HW Raman region has great promise for the reagent-free and non-invasive detection of type II diabetes.

A potential method for non-invasive acute myocardial infarction detection based on saliva Raman spectroscopy and multivariate analysis

Raman spectroscopy (RS) was employed for human saliva biochemical analysis with the aim to develop a rapidly non-invasive test for acute myocardial infarction (AMI) detection. High-quality Raman spectra were obtained from human saliva samples of 46 AMI patients and 43 healthy controls. Significant differences in Raman intensities of prominent bands were observed between AMI and normal saliva. The tentative assignment of the observed Raman bands indicated constituent and conformational differences between the two groups. Furthermore, principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and classify the Raman spectra acquired from AMI and healthy saliva, yielding a diagnostic sensitivity of 80.4% and specificity of 81.4%. The results from this exploratory study demonstrated the feasibility and potential for developing RS analysis of human saliva into a clinical tool for rapid AMI detection and screening.

Early discrimination of nasopharyngeal carcinoma based on tissue deoxyribose nucleic acid surface-enhanced Raman spectroscopy analysis

Abstract. Surface-enhanced Raman spectroscopy (SERS) was employed to detect deoxyribose nucleic acid (DNA) variations associated with the development of nasopharyngeal carcinoma (NPC). Significant SERS spectral differences between the DNA extracted from early NPC, advanced NPC, and normal nasopharyngeal tissue specimens were observed at 678, 729, 788, 1337, 1421, 1506, and 1573  cm−1, which reflects the genetic variations in NPC. Principal component analysis combined with discriminant function analysis for early NPC discrimination yielded a diagnostic accuracy of 86.8%, 92.3%, and 87.9% for early NPC, advanced NPC, and normal nasopharyngeal tissue DNA, respectively. In this exploratory study, we demonstrated the potential of SERS for early detection of NPC based on the DNA molecular study of biopsy tissues.

Detection of human serum proteins using Raman and SERS spectroscopy

The use of normal Raman (NR) spectroscopy and surface enhanced Raman scattering (SERS) spectroscopy to analyze the biochemical information of human serum proteins and hence distinguish between normal and primary hepatic carcinoma (PHC) serum samples was investigated. The serum samples were obtained from patients who were clinically diagnosed with PHC (n=20) and healthy volunteers (n=20). All spectra were collected in the spectral range of 400-1800 cm-1 and analyzed through the multivariate statistical methods of principal component analysis (PCA). The results showed that both NR and SERS combined with PCA had good performance in distinguishing the human serum proteins between PHC patients and healthy volunteers with high sensitivity and specificity of 100%. And we can get more detail information of component and conformation of human serum proteins by considering NR and SERS spectrum. Our results support the concept again that serum protein Raman and SERS spectroscopy combined with PCA analysis both can become noninvasive and rapid diagnostic tools to detect the primary hepatic carcinoma.

Serum albumin analysis for type II diabetes detection using surface-enhanced Raman spectroscopy

Surface-enhanced Raman scattering (SERS) spectroscopy combined with membrane electrophoresis (ME) was firstly employed to detect albumin variation in type II diabetic development. Albumin was first purified from human serum by ME and then mixed with silver nanoparticles to perform SERS spectral analysis. SERS spectra were obtained from blood albumin samples of 20 diabetic patients and 19 healthy volunteers. Subtle but discernible changes in the acquired mean spectra of the two groups were observed. Tentative assignment of albumin SERS bands indicated specific structural changes of albumin molecule with diabetic development. Meanwhile, PCA-LDA diagnostic algorithms were employed to classify the two kinds of albumin SERS spectra, yielding the diagnostic sensitivity of 90% and specificity of 94.7%. The results from this exploratory study demonstrated that the EM-SERS method in combination with multivariate statistical analysis has great potential for the label-free detection of albumin variation for improving type II diabetes screening.

Characterization of secreted proteins in HepG2 and LO2 cells by Raman spectroscopy

Secreted proteins, the promising source of biomarkers for early detection and diagnosis of cancer, have received considerable attention. Raman spectroscopy and principal component analysis (PCA) were used to characterize the secreted proteins collected from the cell cultures of human hepatoma cell line HepG2 and normal human liver cell line LO2 in this paper. We found the major difference of secreted proteins Raman spectra between HepG2 and LO2 cells were in the range of 1200cm-1-1800cm-1. Compared with LO2 cells, some significant changes based on secondary structure of secreted proteins in HepG2 cells were observed, including the increase in the relative intensity of the band at 1004cm-1, 1445cm-1, 1674cm-1 and the decrease at 1074cm-1. These variations of Raman bands indicated that the species and conformation of secreted proteins in HepG2 cells changed. The measured Raman spectra of the two groups were separated into two distinct clusters with no overlap and high specificity and sensitivity by PCA. These results show that the combination of Raman spectroscopy and PCA analysis may be a powerful tool for distinguishing the secreted proteins between human hepatoma cells and normal human liver cells, provide a new thought to analyze the secreted proteins from cancer cells and find a novel cancer biomarker.