Ultrasensitive Characterization of the Prion Protein by Surface-Enhanced Raman Scattering: Selective Enhancement via Electrostatic Tethering of the Intrinsically Disordered Domain with Functionalized Silver Nanoparticles

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

A microfluidic biosensor for the diagnosis of chronic wasting disease

Cervids are affected by a neurologic disease that is always fatal to individuals and has population effects. This disease is called chronic wasting disease (CWD) and is caused by a misfolded prion protein. The disease is transmitted via contact with contaminated body fluids and tissue or exposure to the environment, such as drinking water or food. Current CWD diagnosis depends on ELISA screening of cervid lymph nodes and subsequent immunohistochemistry (IHC) confirmation of ELISA-positive results. The disease has proven to be difficult to control in part because of sensitivity and specificity issues with the current test regimen. We have investigated an accurate, rapid, and low-cost microfluidic microelectromechanical system (MEMS) biosensing device for the detection of CWD pathologic prions in retropharyngeal lymph nodes (RLNs), which is the current standard type of CWD diagnostic sample. The device consists of three novel regions for concentrating, trapping, and detecting the prion. The detection region includes an array of electrodes coated with a monoclonal antibody against pathologic prions. The experimental conditions were optimized using an engineered prion control antigen. Testing could be completed in less than 1 hour with high sensitivity and selectivity. The biosensor detected the engineered prion antigen at a 1:24 dilution, while ELISA detected the same antigen at a 1:8 dilution. The relative limit of detection (rLOD) of the biosensor was a 1:1000 dilution of a known strong positive RLN sample, whereas ELISA showed a rLOD of 1:100 dilution. Thus, the biosensor was 10 times more sensitive than ELISA, which is the currently approved CWD diagnostic test. The biosensor's specificity and selectivity were confirmed using known negative RPLN samples, a negative control antibody (monoclonal antibody against bovine coronavirus BCV), and two negative control antigens (bluetongue virus and Epizootic hemorrhagic disease virus). The biosensor's ability to detect pathogenic prions was verified by testing proteinase-digested positive RLN samples.

Ameliorating SERS Sensitivity for Pesticide Malathion Detection with Synergistic Boosting Effect by Hydrogen Cations and Chloride Anions

Although SERS has been widely recognized as one of the highly sensitive analytical methods that can be deployed in the field with high sensitivity and short analysis time, reports regarding the fast determination of malathion at low concentrations are still scarce. Here, in this work, the solution pH and various halogen co-adsorbates were explored to promote the SERS signal of malathion using the citrate-reduced Ag NPs. It was found that chloride anions were the most efficient signal booster among the three halogen ions screened. Further examination of the SERS profile of the malathion in the presence of different halogen species found that the stretching mode of the P-S bond shifted to a lower frequency with Cl^-, which may imply closer (and stronger) binding of malathion to the Ag NPs. This concurs with literature reports that halogen ions could facilitate the adsorption of a certain analyte onto the SERS substrate. In addition, hydrogen ions showed a synergistic effect on SERS signal enhancement when combined with chloride anions. At optimum conditions, the malathion could be detected with a limit of detection (LOD) of 3 ppb. Malathion-spiked cherry tomatoes and oranges were analyzed, and the recovery rates were found to be within 85-100%.

Correlation coefficient-directed label-free characterization of native proteins by surface-enhanced Raman spectroscopy

Investigation of proteins in their native state is the core of proteomics towards better understanding of their structures and functions. Surface-enhanced Raman spectroscopy (SERS) has shown its unique advantages in protein characterization with fingerprint information and high sensitivity, which makes it a promising tool for proteomics. It is still challenging to obtain SERS spectra of proteins in the native state and evaluate the native degree. Here, we constructed 3D physiological hotspots for a label-free dynamic SERS characterization of a native protein with iodide-modified 140 nm Au nanoparticles. We further introduced the correlation coefficient to quantitatively evaluate the variation of the native degree, whose quantitative nature allows us to explicitly investigate the Hofmeister effect on the protein structure. We realized the classification of a protein of SARS-CoV-2 variants in 15 min, which has not been achieved before. This study offers an effective tool for tracking the dynamic structure of proteins and biomedical research.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.