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Surface-enhanced Raman scattering (SERS) nanotags functionalized with lectins as the biological recognition element can be used to target the carbohydrate portion of carbohydrate-carrying molecules (glycoconjugates). An investigation of the optical stability of such functionalized SERS nanotags is an essential initial step before future application and quantification of surface glycan biomarkers on cells and extracellular vesicles. Herein, we report an innovative approach to evaluate the SERS stability of lectin-conjugated nanotags by investigating any possible interfering lectin-lectin interactions in a mixture of different lectin-conjugated SERS nanotags, as well as an assessment of lectin-glycan interaction by mixing wheat germ agglutinin (WGA)-conjugated SERS nanotags with different glycoproteins. No lectin cross-reactivity was found in the mixture of lectin-conjugated SERS nanotags, evidenced by the constant SERS intensity. Additionally, the results showed that the lectins conjugated to SERS nanotags retain their ability to interact with glycans, as evidenced by the changes in the nanotag color and extinction spectra. Their SERS intensity remained constant as supported by finite-element method (FEM) simulation results, demonstrating a high SERS stability and selectivity of lectin-conjugated nanotags towards multiplex applications.

Cancer-derived small extracellular vesicles (sEVs) are specific subgroups of lipid bilayer vesicles secreted from cancer cells to the extracellular environment. They carry distinct biomolecules ( e.g. , proteins, lipids and nucleic acids) from their parent cancer cells. Therefore, the analysis of cancer-derived sEVs can provide valuable information for cancer diagnosis. However, the use of cancer-derived sEVs in clinics is still limited due to their small size, low amounts in circulating fluids, and heterogeneous molecular features, making their isolation and analysis challenging. Recently, microfluidic technology has gained great attention for its ability to isolate sEVs in minimal volume. In addition, microfluidics allows the isolation and detection of sEVs to be integrated into a single device, offering new opportunities for clinical application. Among various detection techniques, surface-enhanced Raman scattering (SERS) has emerged as a promising candidate for integrating with microfluidic devices due to its ultra-sensitivity, stability, rapid readout, and multiplexing capability. In this tutorial review, we start with the design of microfluidics devices for isolation of sEVs and introduce the key factors to be considered for the design, and then discuss the integration of SERS and microfluidic devices by providing descriptive examples of the currently developed platforms. Lastly, we discuss the current limitations and provide our insights for utilising integrated SERS-microfluidics to isolate and analyse cancer-derived sEVs in clinical settings.

Small extracellular vesicles (sEVs) are lipid bilayer vesicles that carry key molecules ( e.g. , proteins, DNAs, RNAs, and lipids) for cell-to-cell communication, being regarded as promising biomarkers for cancer diagnosis. However, the detection of sEVs is still challenging due to their unique characteristics such as size and phenotype heterogeneity. The surface-enhanced Raman scattering (SERS) assay is a promising tool for sEV analysis as it shows the advantages of robustness, high sensitivity, and specificity. Previous studies proposed different "sandwich" immunocomplex assembling strategies and various capturing probes for sEV detection by the SERS assay. However, no studies have reported the effect of immunocomplex assembling strategies and capturing probes on the analysis of sEVs using this assay. Hence, to achieve the highest performance of the SERS assay for analysing ovarian cancer-derived sEVs, we first assessed the presence of ovarian cancer markers such as EpCAM on cancer cells and sEVs by using flow cytometry and immunoblotting. We found that cancer cells and their derived sEVs present EpCAM and therefore EpCAM was used to functionalise SERS nanotags for the comparison study of "sandwich" immunocomplex assembling strategies. Then, we compared three types of capturing probes (magnetic beads conjugated with anti-CD9, CD63, or CD81 antibodies) for sEV detection. Our study showed the strategy of pre-mixing of sEVs with SERS nanotags and the anti-CD9 capturing probe would achieve the best performance with the minimum detection of sEVs down to 1.5 × 10 5 particles per μL and with high specificity in distinguishing sEVs from different ovarian cancer cell lines. We further profiled the surface protein biomarkers (EpCAM, CA125, and CD24) on ovarian cancer-derived sEVs in both PBS and plasma (sEVs spiked in healthy plasma) using the improved SERS assay, showing high sensitivity and specificity. As such, we anticipate that our improved SERS assay has the potential to be used clinically as one of the effective detection methods of ovarian cancer.

Cancer-derived small extracellular vesicles (sEVs) may be a promising drug delivery system that targets cancer cells due to their unique features, such as native homing ability, biological barrier crossing capability, and low immune response. However, the oncogenic cargos within them pose safety concerns, hence limiting their application thus far. We proposed using an electroporation-based strategy to extract the endogenous cargos from cancer-derived sEVs and demonstrated that their homing ability was still retained. A membrane fusion technique was used to fuse these sEVs with liposomes to form hybrid particles, which possessed both benefits of sEVs and liposomes. Anti-EGFR monoclonal antibodies were modified on the hybrid particles to improve their targeting ability further. The engineered hybrid particles showed higher drug loading ability that is 33.75 and 43.88% higher than that of liposomes and sEVs, respectively, and improved targeting ability by 52.23% higher than hybrid particles without modification. This delivery system showed >90% cell viability and enhanced treatment efficiency with 91.58 and 79.26% cell migration inhibition rates for the miR-21 inhibitor and gemcitabine, respectively.

Cells and their derived extracellular vesicles (EVs) or exosomes contain unique molecular signatures that could be used as biomarkers for the detection of severe diseases such as cancer, as well as monitoring the treatment response. Revealing these molecular signatures requires developing non-invasive ultrasensitive tools to enable single molecule/cell-level detection using a small volume of sample with low signal-to-noise ratio background and multiplex capability. Surface-enhanced Raman scattering (SERS) can address the current limitations in studying cells and EVs through two main mechanisms: plasmon-enhanced electric field (the so-called electromagnetic mechanism (EM)), and chemical mechanism (CM). In this review, we first highlight these two SERS mechanisms and then discuss the nanomaterials that have been used to develop SERS biosensors based on each of the aforementioned mechanisms as well as the combination of these two mechanisms in order to take advantage of the synergic effect between electromagnetic enhancement and chemical enhancement. Then, we review the recent advances in designing label-aided and label-free SERS biosensors in both colloidal and planar systems to investigate the surface biomarkers on cancer cells and their derived EVs. Finally, we discuss perspectives of emerging SERS biosensors in future biomedical applications. We believe this review article will thus appeal to researchers in the field of nanobiotechnology including material sciences, biosensors, and biomedical fields.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal cancers worldwide with high mortality, which is mainly due to the lack of reliable biomarkers for PDAC diagnosis/prognosis in the early stages and effective therapeutic strategies for the treatment. Cancer-derived small extracellular vesicles (sEVs), which carry various messages and signal biomolecules (e.g. RNAs, DNAs, proteins, lipids, and glycans) to constitute the key features (e.g. genetic and phenotypic status) of cancer cells, are regarded as highly competitive non-invasive biomarkers for PDAC diagnosis/prognosis. Additionally, new insights on the biogenesis and molecular functions of cancer-derived sEVs pave the way for novel therapeutic strategies based on cancer-derived sEVs for PDAC treatment such as inhibition of the formation or secretion of cancer-derived sEVs, using cancer-derived sEVs as drug carriers and for immunotherapy. This review provides a comprehensive overview of the most recent scientific and clinical research on the discovery and involvement of key molecules in cancer-derived sEVs for PDAC diagnosis/prognosis and strategies using cancer-derived sEVs for PDAC treatment. The current limitations and emerging trends toward clinical application of cancer-derived sEVs in PDAC diagnosis/prognosis and treatment have also been discussed., (© 2022. The Author(s).)

Circulating pancreatic ductal adenocarcinoma (PDAC) derived small extracellular vesicles (sEVs) are nano-sized membranous vesicles secreted from PDAC cells and released into surrounding body fluids, such as blood. The use of plasma-derived sEVs for cancer diagnosis is particularly appealing in biomedical research because the sEVs reflect some key features ( e.g. genetic and phenotypic status) related to the organs from which they originate. For example, the surface membrane proteins and their expression level on sEVs were reported to be related to the presence and progression of PDAC. However, difficulty in sEVs isolation and lack of ultrasensitive assays for simultaneous analysis of multiple protein biomarkers on patient plasma-derived sEVs hinder their application in the clinic. In our previous study, we have demonstrated the application of magnetic beads (MBs) and surface-enhanced Raman scattering (SERS) assay for phenotypic analysis of cancer cells-derived sEVs using different cell lines. To further demonstrate the clinical application of the proposed assay, we have profiled the sEVs' phenotypes (relative expression of biomarker Glypican 1, EpCAM and CD44V6) of healthy donors and PDAC patients to enable simultaneous detection of multiple surface membrane proteins on plasma-derived sEVs. We discovered that the PDAC sEVs' phenotype signatures had high accuracy for PDAC diagnosis (100%) and showed strong correlation with cancer stages, which were further validated by the imaging techniques ( e.g. computerized tomography and magnetic resonance imaging) and also the correlation of cancer stages with CA19-9 (gold standard biomarker) and the sEVs' phenotype signatures. The present proof-of-concept study thus provides an initial investigation of using the proposed SERS assay for PDAC diagnosis and early cancer stage prediction in the clinic.

Direct surface-enhanced Raman scattering (SERS) has contributed to characterizing extracellular vesicles (EVs) by providing molecular signatures. However, little work has been carried out to understand the heterogeneity of EVs created by different methods or from different biological sources. Herein, we pioneered the use of positively charged gold-silver nanostars to explore the SERS profiles of different EVs. The physical features of EVs from cancer cells including the size, concentration, morphology and surface potential have been characterized via nanoparticle tracking analysis, transmission electron microscopy and zeta potential analysis. The results show that negatively charged EVs are attracted to positively charged gold-silver nanostar surfaces via electrostatic forces resulting in SERS spectra showing characteristic vibrational modes of the different components of EVs (i.e. proteins, lipids and nucleic acids). SERS data were complemented by other spectroscopic techniques including atomic force microscopy-infrared spectroscopy, UV-visible absorbance spectroscopy and fluorescence spectroscopy providing a more complete molecular picture of EVs. SERS signatures of EVs from different origins, batches, and isolation approaches were compared and analyzed. A statistical method (principal component analysis-linear discriminant analysis) was utilized to differentiate EV subtypes. Consequently, a desirable discrimination outcome for blind samples was obtained. This study provides novel insights to deepen our understanding of EV heterogeneity.

Circulating cancer-derived small extracellular vesicles (EVs) are nanoscale membranous vesicles shed from cancer cells that are released into surrounding body fluids. Small EVs contain biomolecules associated with cancer such as DNA and proteins for cell-to-cell communication. Therefore, small EVs have been regarded as important cancer biomarkers for liquid biopsy-based cancer diagnosis and drug treatment monitoring. However, because of the high heterogeneity and low level of small EVs in body fluids, there is a high demand for sensitive detection and characterization of such vesicles at a molecular level. In this study, we have developed a sensitive and effective approach to simultaneously profile multiple protein biomarkers expressed on cancer-derived small EVs using surface-enhanced Raman spectroscopy (SERS) nanotags in a single test, without complex isolation steps. Rapid and multiplexed phenotypic profiling of small EVs is achieved by mixing specific detection antibody-coated SERS nanotags, filtered conditioned EV-suspended medium (conditioned EVs), and capture antibody (CD63)-conjugated magnetic beads to form a sandwich immunoassay. As a proof-of-concept demonstration, we applied this approach to characterize pancreatic cancer-derived EVs by simultaneously detecting three specific EV surface receptors including Glypican-1, epithelial cell adhesion molecules (EpCAMs), and CD44 variant isoform 6 (CD44V6). The sensitivity of this method was measured down to 2.3 × 10 6 particles/mL, which is more sensitive and shows higher multiplexing capability than most other reported EV profiling techniques, such as western blot, enzyme-linked immunosorbent assay, and flow cytometry. Furthermore, phenotypic profiling of small EVs from colorectal cancer and bladder cancer cell lines (SW480 and C3) was conducted and compared to those derived from pancreatic cancer (Panc-1), highlighting the significant difference in EV phenotypes for various cancer cell types suspended in both phosphate-buffered saline and plasma. Thus, we believe that this technology enables a comprehensive evaluation of small secreted EV heterogeneity with high sensitivity, offering strong potential for accurate noninvasive cancer diagnosis and monitoring of drug treatment. In addition, this assay provides point-of-care use because of the easy sample preparation and portable nature of the Raman spectrometer.

Monitoring targeted therapy in real time for cancer patients could provide vital information about the development of drug resistance and improve therapeutic outcomes. Extracellular vesicles (EVs) have recently emerged as a promising cancer biomarker, and EV phenotyping shows high potential for monitoring treatment responses. Here, we demonstrate the feasibility of monitoring patient treatment responses based on the plasma EV phenotypic evolution using a multiplex EV phenotype analyzer chip (EPAC). EPAC incorporates the nanomixing-enhanced microchip and the multiplex surface-enhanced Raman scattering (SERS) nanotag system for direct EV phenotyping without EV enrichment. In a preclinical model, we observe the EV phenotypic heterogeneity and different phenotypic responses to the treatment. Furthermore, we successfully detect cancer-specific EV phenotypes from melanoma patient plasma. We longitudinally monitor the EV phenotypic evolution of eight melanoma patients receiving targeted therapy and find specific EV profiles involved in the development of drug resistance, reflecting the potential of EV phenotyping for monitoring treatment responses., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)