Extracellular Vesicle Antibody Microarray for Multiplexed Inner and Outer Protein Analysis

Advances in the application of extracellular vesicles in precise diagnosis of pancreatic cancer

Pancreatic cancer is a highly malignant tumor with poor prognosis, emphasizing the need for accurate early diagnosis. EVs, as mediators of intercellular communication, carry DNA, RNA, and proteins that show differential but not tumor-specific expression patterns in pancreatic cancer. Studies have shown that combining RNA markers in EVs (such as miRNA, circRNA, and lncRNA) with serum CA 19-9 testing can significantly enhance diagnostic accuracy for pancreatic cancer. EV-associated proteins have exhibited favorable diagnostic performance in early-stage pancreatic cancer in preliminary studies, though their clinical applicability remains to be further validated. Furthermore, mutations in KRAS, TP53, and SMAD4 genes within EVs offer a promising avenue for non-invasive liquid biopsy. However, challenges such as standardization, low sensitivity, and specificity still hinder the clinical application of EVs. Future research should focus on strategies including multi-omics integration, AI-assisted analysis, multi-marker combined detection, and large-scale clinical validation to further improve the diagnostic capability for pancreatic cancer. Overcoming these obstacles may position EVs as a vital tool in the diagnosis of pancreatic cancer.

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics

Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics.

Stoichiometric constraints for detection of EV-borne biomarkers in blood

Stochiometric issues, encompassing both the quantity and heterogeneity of extracellular vesicles (EVs) derived from tumour or other tissues in blood, pose important challenges across various stages of biomarker discovery and detection, affecting the integrity of data, introducing losses and artifacts during blood processing, EV purification and analysis. These challenges shape the diagnostic utility of EVs especially within the framework of established and emerging methodologies. By addressing these challenges, we aim to delineate crucial parameters and requirements for tumour-specific EV detection, or more precisely, for tumour identification via EV based assays. Our endeavour involves a comprehensive examination of the layers that mask or confound the traceability of EV markers such as nucleic acids and proteins, and focus on 'low prevalence-low concentration' scenario. Finally, we evaluate the advantages versus limitations of single-particle analysers over more conventional bulk assays, suggesting that the combined use of both to capture and interpret the EV signals, in particular the EV surface displayed proteins, may ultimately provide quantitative information on their absolute abundance and distribution.

Harnessing extracellular vesicle heterogeneity for diagnostic and therapeutic applications

Extracellular vesicles (EVs) are diverse nanoparticles with large heterogeneity in size and molecular composition. Although this heterogeneity provides high diagnostic value for liquid biopsy and confers many exploitable functions for therapeutic applications in cancer detection, wound healing and neurodegenerative and cardiovascular diseases, it has also impeded their clinical translation-hence heterogeneity acts as a double-edged sword. Here we review the impact of subpopulation heterogeneity on EV function and identify key cornerstones for addressing heterogeneity in the context of modern analytical platforms with single-particle resolution. We outline concrete steps towards the identification of key active biomolecules that determine EV mechanisms of action across different EV subtypes. We describe how such knowledge could accelerate EV-based therapies and engineering approaches for mimetic artificial nanovesicle formulations. This approach blunts one edge of the sword, leaving only a single razor-sharp edge on which EV heterogeneity can be exploited for therapeutic applications across many diseases.

Playing pin-the-tail-on-the-protein in extracellular vesicle (EV) proteomics

Extracellular vesicles (EVs) are anucleate particles enclosed by a lipid bilayer that are released from cells via exocytosis or direct budding from the plasma membrane. They contain an array of important molecular cargo such as proteins, nucleic acids, and lipids, and can transfer these cargoes to recipient cells as a means of intercellular communication. One of the overarching paradigms in the field of EV research is that EV cargo should reflect the biological state of the cell of origin. The true relationship or extent of this correlation is confounded by many factors, including the numerous ways one can isolate or enrich EVs, overlap in the biophysical properties of different classes of EVs, and analytical limitations. This presents a challenge to research aimed at detecting low-abundant EV-encapsulated nucleic acids or proteins in biofluids for biomarker research and underpins technical obstacles in the confident assessment of the proteomic landscape of EVs that may be affected by sample-type specific or disease-associated proteoforms. Improving our understanding of EV biogenesis, cargo loading, and developments in top-down proteomics may guide us towards advanced approaches for selective EV and molecular cargo enrichment, which could aid EV diagnostics and therapeutics research.

Recent advances in chemical biology tools for protein and RNA profiling of extracellular vesicles

Extracellular vesicles (EVs) are nano-sized vesicles secreted by cells that contain various cellular components such as proteins, nucleic acids, and lipids from the parent cell. EVs are abundant in body fluids and can serve as circulating biomarkers for a variety of diseases or as a regulator of various biological processes. Considering these characteristics of EVs, analysis of the EV cargo has been spotlighted for disease diagnosis or to understand biological processes in biomedical research. Over the past decade, technologies for rapid and sensitive analysis of EVs in biofluids have evolved, but detection and isolation of targeted EVs in complex body fluids is still challenging due to the unique physical and biological properties of EVs. Recent advances in chemical biology provide new opportunities for efficient profiling of the molecular contents of EVs. A myriad of chemical biology tools have been harnessed to enhance the analytical performance of conventional assays for better understanding of EV biology. In this review, we will discuss the improvements that have been achieved using chemical biology tools.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform

Label-free detection of multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. First, we characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles are required.

Effect of Sample Preprocessing and Size-Based Extraction Methods on the Physical and Molecular Profiles of Extracellular Vesicles

Extracellular vesicles (EVs) are nanometric lipid vesicles that shuttle cargo between cells. Their analysis could shed light on health and disease conditions, but EVs must first be preserved, extracted, and often preconcentrated. Here we first compare plasma preservation agents, and second, using both plasma and cell supernatant, four EV extraction methods, including (i) ultracentrifugation (UC), (ii) size-exclusion chromatography (SEC), (iii) centrifugal filtration (LoDF), and (iv) accousto-sorting (AcS). We benchmarked them by characterizing the integrity, size distribution, concentration, purity, and expression profiles for nine proteins of EVs, as well as the overall throughput, time-to-result, and cost. We found that the difference between ethylenediaminetetraacetic acid (EDTA) and citrate anticoagulants varies with the extraction method. In our hands, ultracentrifugation produced a high yield of EVs with low contamination; SEC is low-cost, fast, and easy to implement, but the purity of EVs is lower; LoDF and AcS are both compatible with process automation, small volume requirement, and rapid processing times. When using plasma, LoDF was susceptible to clogging and sample contamination, while AcS featured high purity but a lower yield of extraction. Analysis of protein profiles suggests that the extraction methods extract different subpopulations of EVs. Our study highlights the strengths and weaknesses of sample preprocessing methods, and the variability in concentration, purity, and EV expression profiles of the extracted EVs. Preanalytical parameters such as collection or preprocessing protocols must be considered as part of the entire process in order to address EV diversity and their use as clinically actionable indicators.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform

Label-free detecting multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. Herein, we first thoroughly characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles is required.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform

Label-free detecting multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. Herein, we first thoroughly characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles is required.