Intrinsic SERS Fingerprints of Aptamer-Peptide Conjugates for Direct High-Specific Profiling Abnormal Protein Levels in Cancer Patients

Advances in Functional Nucleic Acid SERS Sensing Strategies

Functional nucleic acids constitute a distinct category of nucleic acids that diverge from conventional nucleic acid amplification methodologies. They are capable of forming intricate hybrid structures through Hoogsteen and reverse Hoogsteen hydrogen bonding interactions between double-stranded and single-stranded DNA, thereby broadening the spectrum of DNA interactions. In recent years, functional DNA/RNA-based surface-enhanced Raman spectroscopy (SERS) has emerged as a potent platform capable of ultrasensitive and multiplexed detection of a variety of analytes of interest. This review aims to elucidate the operational principles of several functional nucleic acids in SERS detection, including DNAzymes, G-quadruplexes, aptamers, CRISPR, origami etc., alongside the design methodologies and practical applications of functional DNA/RNA-based SERS sensing. Initially, an overview is summarized encompassing the structural attributes and SERS sensing mechanisms inherent to diverse functional DNA/RNA. Following this, various innovative strategies for constructing functional nucleic acid-based SERS sensors are illustrated in detail, aimed at improving the present detection capabilities. A comprehensive summing up is then conducted on the applications of these sensors in crucial fields, such as disease diagnosis, environmental monitoring, and food safety detection, with a particular focus on SERS sensitivity, specificity, and analytical versatility. Finally, conclusive remarks are offered along with an exploration of the existing challenges and prospective avenues for future research in this developed field.

A practical approach to quantitative analytical surface-enhanced Raman spectroscopy

Many of the features of SERS, such as its high sensitivity, molecular specificity and speed of analysis make it attractive as an analytical technique. However, SERS currently remains a specialist technique which has not yet entered the mainstream of analytical chemistry. Therefore, this review draws out the underlying principles for analytical SERS and provides practical tips and tricks for SERS quantitation. The aim is to show the readers how to rationally design their SERS experiments to improve quantitation performance. We begin by introducing the three core components in SERS analysis: (1) the enhancing substrate material, (2) the Raman instrument and (3) the processed data that is used to establish a calibration curve. This is followed by discussion of the analytical figures of merit relevant to SERS. In the following sections each of the three essential components in SERS quantitation and how they affect the quality of the analysis are described in more detail using examples from the literature. Finally, we highlight the current challenges in applying SERS to the analysis of complex real-life samples and briefly introduce the state-of-the-art developments on multifunctional substrates, digital SERS and AI-assisted data processing, which will help SERS rise to the challenge of moving out into routine real-world analysis.

Immune-like sandwich multiple hotspots SERS biosensor for ultrasensitive detection of NDKA biomarker in serum

Developing the rapid, specific, and sensitive tumor marker NDKA biosensor has become an urgent need in the field of early diagnosis of colorectal cancer (CRC). Surface-enhanced Raman spectroscopy (SERS) with the advantages of high sensitivity, high resolution as well as providing sample fingerprint, enables rapid and sensitive detection of tumor markers. However, many SERS biosensors rely on boosting the quantity of Raman reporter molecules on individual nanoparticle surfaces, which can result in nanoparticle agglomeration, diminishing the stability and sensitivity of NDKA detection. Here, we proposed an immune-like sandwich multiple hotspots SERS biosensor for highly sensitive and stable analysis of NDKA in serum based on molecularly imprinted polymers and NDKA antibody. The SERS biosensor employs an array of gold nanoparticles, which are coated with a biocompatible polydopamine molecularly imprinted polymer as a substrate to specifically capture NDKA. Then the biosensor detects NDKA through Raman signals as a result of the specific binding of NDKA to the SERS nanotag affixed to the capture substrate along with the formation of multiple hotspots. This SERS biosensor not only avoids the aggregation of nanoparticles but also presents a solution to the obstacles encountered in immune strategies for certain proteins lacking multiple antibody or aptamer binding sites. Furthermore, the practical application of the SERS biosensor is validated by the detection of NDKA in serum with the lower limit of detection (LOD) of 0.25 pg/mL, meanwhile can detect NDKA of 10 ng/mL in mixed proteins solution, illustrating high sensitivity and specificity. This immune-like sandwich multiple hotspots biosensor makes it quite useful for the early detection of CRC and also provides new ideas for cancer biomarker sensing strategy in the future.

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.