SERS-based sensor for direct L-selectin level determination in plasma samples as alternative method of tumor detection

Identification of Molecular Profile of Cell Membrane via Magnetic Plasmonic Nanoprobe

Cell membranes are primarily composed of lipids, membrane proteins, and carbohydrates, and the related studies of membrane components and structures at different stages of disease development, especially membrane proteins, are of great significance. Here, we investigate the chemical signature profiles of cell membranes as biomarkers for cancer cells via label-free surface-enhanced Raman scattering (SERS). A magnetic plasmonic nanoprobe was proposed by decorating magnetic beads with silver nanoparticles, allowing self-driven cell membrane-targeted accumulation within 5 min. SERS profiles of three types of breast cells were achieved under the plasmon enhancement effect of these nanoprobes. Membrane fingerprint spectra from breast cell lines were further classified with the convolutional neural network model, which perfectly distinguished between two breast cancer subtypes. We further tested various clinical samples using this method and obtained fingerprint spectra from primary cells and frozen slices. This study presents a practical, user-friendly approach for label-free and in situ analysis of cell membranes, which can work for early tumor screening and treatment assessment for tumors reliant on the Molecular profiles of cell membranes. Additionally, this method can be applied universally to explore cell membrane components of other cells, thus assisting Human Cell Atlas.

Determination of L-selectin in blood plasma using DNA aptamer-based surface-enhanced Raman spectroscopy assay

In the human body, tumor cell occurrence can be indirectly monitored using the L-selectin concentration in the blood, since selectin ligands are present on the surface of tumor cells, and with tumor progression, a decrease in L-selectin levels can be expected and observed. In this study, we present a selective DNA-based surface-enhanced Raman spectroscopy (SERS) assay for the detection and determination of L-selectin in biological samples. Two calibration curves (linear in the 40-190 ng mL-1 region and exponential in the 40-500 ng mL-1 region) are fitted to the obtained SERS experimental data, i.e., the ratio of I732/I1334 band intensities (LOQ = 46 ng mL-1). Calculated determination coefficients are found to be R2 = 0.997 for the linear region of the calibration curve and R2 = 0.977 for the exponential region. Moreover, we demonstrate very good selectivity of the assay even in the presence of P- and E-selectin in a sample containing L-selectin. With our SERS assay, the L-selectin concentration in biological samples can be estimated directly from the calibration curves.

Gold nanoparticle decorated blu-ray digital versatile disc as a highly reproducible surface-enhanced Raman scattering substrate for detection and analysis of rotavirus RNA in laboratory environment

Detection and estimation of various biomolecular samples are often required in research and clinical laboratory applications. Present work demonstrates the functioning of a surface-enhanced Raman scattering (SERS) substrate that has been obtained by drop-casting of citrate-reduced gold nanoparticles (AuNPs) of average dimension of 23 nm on a bare blu-ray digital versatile disc (BR-DVD) substrate. The performance of the proposed SERS substrate has been initially evaluated with standard Raman active samples, namely malachite green (MG) and 1,2-bis(4-pyridyl)ethylene (BPE). The designed SERS substrate yields an average enhancement factor of 3.2 × 10⁶ while maintaining reproducibility characteristics as good as 94% over the sensing region of the substrate. The usability of the designed SERS substrate has been demonstrated through the detection and analysis of purified rotavirus double-stranded RNA (dsRNA) samples in the laboratory environment condition. Rotavirus RNA concentrations as low as 10 ng/μL could be detected with the proposed sensing scheme.

How Surface-Enhanced Raman Spectroscopy Could Contribute to Medical Diagnoses

In the last decade, there has been a rapid increase in the number of surface-enhanced Raman scattering (SERS) spectroscopy applications in medical research. In this article we review some recent, and in our opinion, most interesting and promising applications of SERS spectroscopy in medical diagnostics, including those that permit multiplexing within the range important for clinical samples. We focus on the SERS-based detection of markers of various diseases (or those whose presence significantly increases the chance of developing a given disease), and on drug monitoring. We present selected examples of the SERS detection of particular fragments of DNA or RNA, or of bacteria, viruses, and disease-related proteins. We also describe a very promising and elegant ‘lab-on-chip’ approach used to carry out practical SERS measurements via a pad whose action is similar to that of a pregnancy test. The fundamental theoretical background of SERS spectroscopy, which should allow a better understanding of the operation of the sensors described, is also briefly outlined. We hope that this review article will be useful for researchers planning to enter this fascinating field.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.