Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, surface-enhanced Raman spectroscopy (SERS)-based detection of biomarkers has attracted growing interest in this area. Oligonucleotides are an important type of genetic biomarkers as their alterations can be linked to the disease prior to symptom onset. We propose a machine-learning (ML)-enabled framework to analyze complex direct SERS spectra of short, single-stranded DNA and RNA targets to identify relevant mutations occurring in genetic biomarkers, which are key disease indicators. First, by employing ad hoc-synthesized colloidal silver nanoparticles as SERS substrates, we analyze single-base mutations in ssDNA and RNA sequences using a direct SERS-sensing approach. Then, an ML-based hypothesis test is proposed to identify these changes and differentiate the mutated sequences from the corresponding native ones. Rooted in "functional data analysis," this ML approach fully leverages the rich information and dependencies within SERS spectral data for improved modeling and detection capability. Tested on a large set of DNA and RNA SERS data, including from miR-21 (a known cancer miRNA biomarker), our approach is shown to accurately differentiate SERS spectra obtained from different oligonucleotides, outperforming various data-driven methods across several performance metrics, including accuracy, sensitivity, specificity, and F1-scores. Hence, this work represents a step forward in the development of the combined use of SERS and ML as effective methods for disease diagnosis with real applicability in the clinic.

Challenges and opportunities for SERS in the infrared: materials and methods.

In the wake of a global, heightened interest towards biomarker and disease detection prompted by the SARS-CoV-2 pandemic, surface enhanced Raman spectroscopy (SERS) positions itself again at the forefront of biosensing innovation. But is it ready to move from the laboratory to the clinic? This review presents the challenges associated with the application of SERS to the biomedical field, and thus, to the use of excitation sources in the near infrared, where biological windows allow for cell and through-tissue measurements. Two main tackling strategies will be discussed: (1) acting on the design of the enhancing substrate, which includes manipulation of nanoparticle shape, material, and supramolecular architecture, and (2) acting on the spectral collection set-up. A final perspective highlights the upcoming scientific and technological bets that need to be won in order for SERS to stably transition from benchtop to bedside.

Direct and Water-Mediated Adsorption of Stabilizers on SERS-Active Colloidal Bimetallic Plasmonic Nanomaterials: Insight into Citrate-AuAg Interactions from DFT Calculations.

In previous studies, AuAg colloidal nanostar formulations were developed with the two-fold aim of producing optimized surface-enhanced Raman spectroscopy (SERS) substrates and investigating the nature of the capping process itself. Findings demonstrated that the nanoparticle metals are alloyed and neutral, and capping by stabilizers occurs via chemisorption. This study utilizes citrate as the model stabilizer and investigates the mechanistic aspects of its interaction with mono- (Au20) and bimetallic (Au19Ag) surfaces by density functional theory (DFT) calculations. Citrate was modeled according to the colloid's pH and surrounded by a water and sodium first solvation shell. A population of stable cluster-citrate structures was obtained, and energies were refined at the uB3LYP//LANL2TZ(f)/cc-pVTZ level of theory. Solvation was accounted for both explicitly and implicitly by the application of the continuum model SMD. Results indicate that both direct binding and binding by water proxy through the charge-transfer complex formation are thermodynamically favorable. Water participation in citrate adsorption is supported by the adsorption behavior observed experimentally and the comparison between experimental and DFT-simulated IR spectra. Vibrational mode analysis suggests the possible presence of water within a crystal in dried nanostar residues. All ΔGads(aq) indicate a weak chemisorptive process, leading to the hypothesis that citrate could be displaced by analytes during SERS measurements.

Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer.

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

Micro Solid Phase Extraction Surface-Enhanced Raman Spectroscopy (µ-SPE/SERS) Screening Test for the Detection of the Synthetic Cannabinoid JWH-018 in Oral Fluid.

An analytical protocol based on surface-enhanced Raman spectroscopy (SERS) and aimed at the detection of toxicologically relevant concentrations of JWH-018 in oral fluid is presented for the first time. A DFT-supported in-depth vibrational characterization of the drug in the solid state and in solution was also performed, providing a body of literature for future spectroscopic work on the compound. A Langmuir adsorption model was used to derive quantitative parameters such as the affinity of JWH-018 for citrate-capped gold nanospheres as well as the LOD. The application of the implemented method to the analysis of extracts from fortified oral fluid samples demonstrates the feasibility of SERS as an alternative to current immunoassays as a screening tool for use in emergency room settings.