Microfluidic SERS devices: brightening the future of bioanalysis

A review of SERS coupled microfluidic platforms: From configurations to applications

Microfluidic systems have attracted considerable attention due to their low reagent consumption, short analysis time, and ease of integration in comparison to conventional methods, but still suffer from shortcomings in sensitivity and selectivity. Surface enhanced Raman scattering (SERS) offers several advantages in the detection of compounds, including label-free detection at the single-molecule level, and the narrow Raman peak width for multiplexing. Combining microfluidics with SERS is a viable way to improve their detection sensitivity. Researchers have recently developed several SERS coupled microfluidic platforms with substantial potential for biomolecular detection, cellular and bacterial analysis, and hazardous substance detection. We review the current development of SERS coupled microfluidic platforms, illustrate their detection principles and construction, and summarize the latest applications in biology, environmental protection and food safety. In addition, we innovatively summarize the current status of SERS coupled multi-mode microfluidic platforms with other detection technologies. Finally, we discuss the challenges and countermeasures during the development of SERS coupled microfluidic platforms, as well as predict the future development trend of SERS coupled microfluidic platforms.

Plasmonic cellulose microfilament assisted SERS detection in microfluidics

Limited by the narrow enhanced area of nanoscale on the metal surface, the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection in solution is usually much lower than the detection in a solid substrate, which is dramatic in microfluidics for online detection. In this work, a cellulose microfilament embraced by Ag nanoparticles, called plasmonic cellulose microfilament, is located in a microchannel for SERS detection in microfluidics. Benefiting from the congestion caused by the plasmonic cellulose microfilament in a microchannel, the trace molecule in the solution is much easier to gather in Ag nanoparticles for Raman enhancement. To obtain high sensitivity, the structure of plasmonic cellulose microfilament is optimized. The SERS spectra collected in this novel microfluidics demonstrate that the plasmonic cellulose microfilament presents a high sensitivity at 10-13 M and good reproducibility in SERS detection. In addition, automatic identification of urea presence or absence was achieved based on deep learning (DL) here. The results show excellent diagnostic accuracy (99 %), which suggests that a fast, label-free urea screening tool can be developed. These results point out this SERS microfluidics with plasmonic cellulose microfilament has a great application prospective in online SERS detection with high sensitivity.

Simultaneous detection of urea and lactate in sweat based on a wearable sweat biosensor

Urea and lactate are biomarkers in sweat that is closely associated with human health. This study introduces portable, rapid, sensitive, stable, and high-throughput wearable sweat biosensors utilizing Au-Ag nanoshuttles (Au-Ag NSs) for the simultaneous detection of sweat urea and lactate. The Au-Ag NSs arrays within the biosensor's microfluidic cavity provide a substantial surface-enhanced Raman scattering (SERS) enhancement effect. The limit of detection (LOD) for urea and lactate are 2.35 × 10-6 and 8.66 × 10-7 mol/L, respectively. This wearable sweat biosensor demonstrates high resistance to compression bending, repeatability, and stability and can be securely attached to various body parts. Real-time sweat analysis of volunteers wearing the biosensors during exercise demonstrated the method's practicality. This wearable sweat biosensor holds significant potential for monitoring sweat dynamics and serves as a valuable tool for assessing bioinformation in sweat.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Microfluidics for Profiling miRNA Biomarker Panels in AI-Assisted Cancer Diagnosis and Prognosis

Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine.