Design, Rationalization, and Automation of a Catalytic Sensing Mechanism for Homogeneous SERS Biosensors

AI in SERS sensing moving from discriminative to generative

This perspective discusses the present and future role of artificial intelligence (AI) and machine learning (ML) in surface-enhanced Raman scattering (SERS) sensing. Our goal is to guide the reader through current applications, mainly focused on discriminative approaches aimed at developing new and improved SERS diagnostic capabilities, towards the future role of AI in SERS sensing, with the use of generative approaches to design new materials and biomaterials.

Lyophilizing SERS biosensors to enable translation into an easy-to-use assay

The COVID-19 pandemic has highlighted the importance of point-of-care (POC) pathogen detection. Accurate and accessible diagnostic techniques for virus identification are crucial for controlling the spread of diseases and have profound implications for our communities and global health. Reagentless surface-enhanced Raman scattering (SERS) sensors offer a promising solution for POC testing due to their capability to function without complex processing steps. However, their application in this space is limited by the fact that these solution-based assays are challenging to administer, transport and store. To overcome these limitations, we employed a freeze-drying (lyophilization) process on reagentless SERS sensors and investigated their improved stability and shelf-life. We explored this mechanism using different concentrations of cryoprotectants. Lyophilized sensors were then tested in a mix-and-detect fashion by adding to the dry sensors a drop of the sample, consisting of saliva spiked with target DNA oligonucleotides relative to different SARS-CoV-2 variants. In addition, we further uncovered how lyophilization benefits sensors with a DNA-catalysis mechanism. In summary, our findings indicate that lyophilization substantially enhances the practicality and usability of reagentless SERS sensors, contributing to the translation of this powerful diagnostic tool to POC testing in remote areas with limited resources.

Biomedical SERS - the current state and future trends

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

Soft interface confined DNA walker for sensitive and specific detection of SARS-CoV-2 variants

Nucleic acid detection is conducive to preventing the spread of COVID-19 pandemic. In this work, we successfully designed a soft interface confined DNA walker by anchoring hairpin reporter probes on cell membranes for the detection of SARS-CoV-2 variants. In the presence of target RNA, the cyclic self-assembly reaction occurred between hairpin probes H1 and H2, and the continuous walking of target RNA on cell membranes led to the gradual amplification of fluorescence signal. The enrichment of H1 on membranes and the unique fluidity of membranes promoted the collision efficiency between DNA strands in the reaction process, endowing this method with high sensitivity. In addition, the double-blind test of synthetic RNA in 5% normal human serum demonstrated the good stability and anti-interference in complex environment of this method, which exhibited great potential in clinical diagnostics.

Automation and Computerization of (Bio)sensing Systems

Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.

Cascade Amplified Plasmonics Molecular Biosensor for Sensitive Detection of Disease Biomarkers

Recent advances in molecular technologies have provided various assay strategies for monitoring biomarkers, such as miRNAs for early detection of various diseases and cancers. However, there is still an urgent unmet need to develop practical and accurate miRNA analytical tools that could facilitate the incorporation of miRNA biomarkers into clinical practice and management. In this study, we demonstrate the feasibility of using a cascade amplification method, referred to as the "Cascade Amplification by Recycling Trigger Probe" (CARTP) strategy, to improve the detection sensitivity of the inverse Molecular Sentinel (iMS) nanobiosensor. The iMS nanobiosensor developed in our laboratory is a unique homogeneous multiplex bioassay technique based on surface-enhanced Raman scattering (SERS) detection, and was used to successfully detect miRNAs from clinical samples. The CARTP strategy based on the toehold-mediated strand displacement reaction is triggered by a linear DNA strand, called the "Recycling Trigger Probe" (RTP) strand, to amplify the iMS SERS signal. Herein, by using the CARTP strategy, we show a significantly improved detection sensitivity with the limit of detection (LOD) of 45 fM, which is 100-fold more sensitive than the non-amplified iMS assay used in our previous report. We envision that the further development and optimization of this strategy ultimately will allow multiplexed detection of miRNA biomarkers with ultra-high sensitivity for clinical translation and application.