Analysis of Tumor-Derived Exosomes by Nanoscale Flow Cytometry

Branched hybridization chain reaction and tetrahedral DNA-based trivalent aptamer powered SERS sensor for ultra-highly sensitive detection of cancer-derived exosomes

Exosomes have emerged as a promising noninvasive biomarker for early cancer diagnosis due to their ability to carry specific bioinformation related to cancer cells. However, accurate detection of trace amount of cancer-derived exosomes in complex blood remains a significant challenge. Herein, an ultra-highly sensitive SERS sensor, powered by the branched hybridization chain reaction (bHCR) and tetrahedral DNA-based trivalent aptamer (triApt-TDN), has been proposed for precise detection of cancer-derived exosomes. Taking gastric cancer SGC-7901 cells-derived exosomes as a test model, the triApt-TDNs were constructed by conjugating aptamers specific to mucin 1 (MUC1) protein with tetrahedral DNAs and subsequently immobilized on the surface of silver nanorods (AgNRs) arrays to create SERS-active sensing chips capable of specifically capturing exosomes overexpressing MUC1 proteins. The bHCR was further initiated by the trigger aptamers (tgApts) bound to exosomes, and as a result the SERS tags were assembled into AuNP network structures with abundant SERS hotspots. By optimizing the sensing conditions, the SERS sensor showed good performance in ultra-highly sensitive detection of target exosomes within 60 min detection time, with a broad response ranging of 1.44 to 1.44 × 104 particles·μL-1 and an ultralow limit of detection capable of detecting a single exosome in 2 μL sample. Furthermore, the SERS sensor exhibited good uniformity, repeatability and specificity, and capability to distinguish between gastric cancer (GC) patients and healthy controls (HC) through the detection of exosomes in clinical human serums, indicating its promising clinical potential for early diagnosis of gastric cancer.

Factors to consider before choosing EV labeling method for fluorescence-based techniques

A well-designed fluorescence-based analysis of extracellular vesicles (EV) can provide insights into the size, morphology, and biological function of EVs, which can be used in medical applications. Fluorescent nanoparticle tracking analysis with appropriate controls can provide reliable data for size and concentration measurements, while nanoscale flow cytometry is the most appropriate tool for characterizing molecular cargoes. Label selection is a crucial element in all fluorescence methods. The most comprehensive data can be obtained if several labeling approaches for a given marker are used, as they would provide complementary information about EV populations and interactions with the cells. In all EV-related experiments, the influence of lipoproteins and protein corona on the results should be considered. By reviewing and considering all the factors affecting EV labeling methods used in fluorescence-based techniques, we can assert that the data will provide as accurate as possible information about true EV biology and offer precise, clinically applicable information for future EV-based diagnostic or therapeutic applications.

Nanoarray Enabled Size-Dependent Isolation and Proteomics Profiling of Small Extracellular Vesicle Subpopulations toward Accurate Cancer Diagnosis and Prognosis

Small extracellular vesicles (sEVs) have emerged as noninvasive biomarkers in liquid biopsy due to their significant function in pathology and physiology. However, the phenotypic heterogeneity of sEVs presents a significant challenge to their study and has significant implications for their applications in liquid biopsies. In this study, anodic aluminum oxide films with different pore sizes (AAO nanoarray) were introduced to enable size-based isolation and downstream proteomics profiling of sEV subpopulations. The adjustable pore size and abundant Al3+ on the framework of AAOs allowed size-dependent isolation of sEV subpopulations through nanoconfined effects and Lewis acid-base interaction between AAOs and sEVs. Benefiting from the strong concerted effect, the simple AAO nanoarray enabled specific isolation of three sEV subpopulations, termed "50", "90", and "150 nm" groups, from 10 μL of complex biological samples within 10 min with high capture efficiencies and purities. Moreover, the nanopores of AAOs also acted as nanoreactors for comprehensive proteomic profiling of the captured sEV subpopulations to reveal their heterogeneity. The AAO nanoarray was first investigated on sEVs from a cell culture medium, where sEV subpopulations could be clearly distinguished, and three traditional sEV-specific proteins (CD81, CD9, and FLOT1) could be identified by proteomic analysis. A total of 3946, 3951, and 3940 proteins were identified from 50, 90, and 150 nm sEV subpopulations, respectively, which is almost twice the number compared to those obtained from the conventional approach. The concept was further applied to complex real-case sample analysis from prostate cancer patients. Machine learning and gene ontology (GO) information analysis of the identified proteins indicate that different-sized sEV subpopulations contain unique protein cargos and have distinct cellular components and molecular functions. Further receiver operating characteristic curve (ROC) analysis of the top five differential proteins from the three sEV subpopulations demonstrated the high accuracy of the proposed approach toward prostate cancer diagnosis (AUC > 0.99). More importantly, several proteins involved in focal adhesion and antigen processing and presentation pathways were found to be upregulated in prostate cancer patients, which may serve as potential biomarkers of prostate cancer. These results suggest that the sEV subpopulation-based AAO nanoarray is of great value in facilitating the early diagnosis and prognosis of cancer and opens a new avenue for sEVs in liquid biopsy.

Biophotonics for diagnostic detection of extracellular vesicles

Extracellular Vesicles (EVs) are versatile carriers for biomarkers involved in the pathogenesis of multiple human disorders. Despite the increasing scientific and commercial interest in EV application in diagnostics, traditional biomolecular techniques usually require consistent sample amount, rely on operator-dependent and time- consuming procedures and cannot cope with the nano-size range of EVs, limiting both sensitivity and reproducibility of results. The application of biophotonics, i.e. light-based methods, for the diagnostic detection of EVs has brought to the development of innovative platforms with excellent sensitivity. In this review, we propose an overview of the most promising and emerging technologies used in the field of EV-related biomarker discovery. When tested on clinical samples, the reported biophotonic approaches in most cases have managed to discriminate between nanovesicles and contaminants, achieved much higher resolution compared to traditional procedures, and reached moderate to excellent diagnostic accuracy, thus demonstrating great potentialities for their clinical translation.