Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification

Advancing biosensing through super-resolution fluorescence microscopy

Advancement of super-resolution fluorescence microscopy (SRM) has recently allowed applications to the biosensing by offering significant advantages over conventional methods. Its nanoscale spatial resolution and single-molecule sensitivity allow visualization and quantification of biomolecular targets without the need of signal amplification steps typically required in traditional biosensing methods. Moreover, recent innovations in probe design and imaging protocols have expanded SRM capabilities to enable dynamic biosensing in living cells, revealing molecular processes in their native cellular contexts. In this review, we discuss these applications of various SRM techniques to biosensing by highlighting their unique capabilities in providing spatial distribution information and high molecular sensitivity. We address several challenges that must be overcome for the broader application of SRM-based biosensing. Finally, we discuss perspectives on future directions for advancing this field towards practical applications.

Exosomes in melanoma: Future potential for clinical theranostics

Melanoma, an aggressive form of skin cancer, presents significant therapeutic challenges due to its resistance to conventional treatments and propensity for metastasis. Exosomes, nanoscale vesicles secreted by a wide variety of cells, have emerged as promising tools for developing novel melanoma therapies. Exosome-based therapeutic approaches offer several advantages, including inherent biocompatibility, low immunogenicity, and the ability to cross biological barriers. This review explores the therapeutic potential of exosomes in melanoma treatment, focusing on their multifaceted roles in modulating tumor cell behavior, enhancing anti-tumor immune responses, and serving as targeted drug delivery vehicles. We discuss various strategies employed to engineer exosomes for enhanced therapeutic efficacy, including loading them with chemotherapeutic agents, small interfering RNAs (siRNAs), microRNAs (miRNAs), and immunomodulatory molecules. Additionally, we highlight the potential of exosomes derived from diverse sources to enhance anti-cancer effects. Furthermore, we address the challenges and future directions in translating exosome-based therapies from bench to bedside, emphasizing the need for standardized isolation and manufacturing protocols, as well as rigorous preclinical and clinical evaluations to unlock the full therapeutic potential of exosomes in the fight against melanoma.

Freeze-Thaw Imaging for Microorganism Classification Assisted with Artificial Intelligence

Fast and cost-effective microbial classification is crucial for clinical diagnosis, environmental monitoring, and food safety. However, traditional methods encounter challenges including intricate procedures, skilled personnel needs, and sophisticated instrumentations. Here, we propose a cost-effective microbe classification system, also termed freeze-thaw-induced floating pattern of AuNPs (FTFPA), coupled with artificial intelligence, which is capable of identifying microbes at a cost of $0.0023 per sample. Specifically, FTFPA utilizes AuNPs for coincubation with microbes, resulting in distinct patterns upon freeze-thawing due to their weak interaction. These patterns are digitized to train models that distinguish nine microbes in various tasks. The positive sample detection model achieved an F1 score of 0.976 (n = 194), while the multispecies classification task reached a macro F1 score of 0.859 (n = 1728). To address scalability and lightweight requirements across diverse classification scenarios, we categorized microbes based on species classification levels. The macro F1 score of the hierarchical model (n = 5184), order level model (n = 5184), Enterobacteriales level model (n = 2550), and Bacillales level model (n = 1974) was 0.854, 0.907, 0.958, and 0.843. In summary, our method is user-friendly, requiring only simple equipment, is easy to operate, and convenient, providing a platform for microbial identification.

Demystifying EV heterogeneity: emerging microfluidic technologies for isolation and multiplexed profiling of extracellular vesicles

Extracellular vesicles (EVs) are heterogeneous lipid containers carrying complex molecular cargoes, including proteins, nucleic acids, glycans, etc. These vesicles are closely associated with specific physiological characteristics, which makes them invaluable in the detection and monitoring of various diseases. However, traditional isolation methods are often labour-intensive, inefficient, and time-consuming. In addition, single biomarker analyses are no longer accurate enough to meet diagnostic needs. Routine isolation and molecular analysis of high-purity EVs in clinical applications is even more challenging. In this review, we discuss a promising solution, microfluidic-based techniques, that combine efficient isolation and multiplex detection of EVs, to further demystify EV heterogeneity. These microfluidic-based EV multiplexing platforms will hopefully facilitate development of liquid biopsies and offer promising opportunities for personalised therapy.

EpCAM Signaling in Oral Cancer Stem Cells: Implications for Metastasis, Tumorigenicity, and Therapeutic Strategies

Oral cancer, a subtype of head and neck cancer, poses significant global health challenges owing to its late diagnosis and high metastatic potential. The epithelial cell adhesion molecule (EpCAM), a transmembrane glycoprotein, has emerged as a critical player in cancer biology, particularly in oral cancer stem cells (CSCs). This review highlights the multifaceted roles of EPCAM in regulating oral cancer metastasis, tumorigenicity, and resistance to therapy. EpCAM influences key pathways, including Wnt/β-catenin and EGFR, modulating CSC self-renewal, epithelial-to-mesenchymal transition (EMT), and immune evasion. Moreover, EpCAM has been implicated in metabolic reprogramming, epigenetic regulation, and crosstalk with other signaling pathways. Advances in EpCAM-targeting strategies, such as monoclonal antibodies, chimeric antigen receptor (CAR) T/NK cell therapies, and aptamer-based systems hold promise for personalized cancer therapies. However, challenges remain in understanding the precise mechanism of EpCAM in CSC biology and its translation into clinical applications. This review highlights the need for further investigation into the role of EPCAM in oral CSCs and its potential as a therapeutic target to improve patient outcomes.

Digital Profiling of Tumor Extracellular Vesicle-Associated RNAs Directly from Unprocessed Blood Plasma

Tumor-derived extracellular vesicle (tEV)-associated RNAs hold promise as diagnostic biomarkers, but their clinical use is hindered by the rarity of tEVs among nontumor EVs. Here, we present EV-CLIP, a highly sensitive droplet-based digital method for profiling EV RNA. EV-CLIP utilizes the fusion of EVs with charged liposomes (CLIPs) in a microfluidic chip. Optimized CLIP surface charge enables exceptional sensitivity and selectivity for EV-derived miRNAs and mRNAs. This approach streamlines detection with minimal plasma volume (20 μL) and eliminates the need for prior EV isolation or RNA preparation, preventing loss of EVs or RNA. In testing with 83 patient samples, EV-CLIP detected EGFR L858R and T790M mutations with high AUC values of 1.0000 and 0.9784, respectively. Its success in serial monitoring during chemotherapy highlights its potential for precise quantification of rare EV subpopulations, facilitating the exploration of single EV RNA content and enhancing understanding of diverse EV populations in various disease states.

Specific isolation and quantification of PD-L1 positive tumor derived exosomes for accurate breast cancer discrimination via aptamer-functionalized magnetic composites and SERS immunoassay

PD-L1 positive tumor derived exosomes (TEXsPD-L1) play a significant role in disease progression, tumor metastasis and cancer immunotherapy. However, the overlap of PD-L1 between TEXs and non-tumor derived exosomes (non-TEXs) restricts the specific isolation and quantification of TEXPD-L1 from clinical samples. Herein, a new aptamer-functionalized and hydrophilic immunomagnetic substrate was designed by decorating generation 5 polyamidoamine dendrimers (G5 PAMAM), zwitterionic trimethylamine N-oxide (TMAO) and EpCAM (Epithelial cell adhesion molecule) aptamers on magnetic cores sequentially (Fe3O4@PAMAM@TMAO@Aptamer, named as FPTA) for rapid target and efficient capture of TEXs. The FPTA substrate gathered excellent characters of strong magnetic responsiveness of Fe3O4, abundant affinity sites of PAMAM, strong hydrophilicity of TMAO and enhanced affinity properties of EpCAM aptamers. Because of these advantages, FPTA can isolate TEXs quickly within 30min with high capture efficiency of 90.5 % ± 3.0 % and low nonspecific absorption of 8.2 % ± 2.0 % for non-TEXs. Furthermore, PD-L1 (Programmed cell death-ligand 1) positive TEXs (TEXsPD-L1) from the captured TEXs were recognized and quantitatively analyzed by utilizing SERS (surface-enhanced Raman spectroscopy) reporter molecules 4-NTP (4-Nitrothiophenol) on PD-L1 aptamers-functionalized gold immunoaffinity probe. The signal of TEXsPD-L1 was converted to SERS signal of 4-NTP at 1344 cm-1 which exhibited a linear correlation to concentration of TEXsPD-L1(R2 = 0.9905). With these merits, this strategy was further applied to clinical plasma samples from breast cancer (BC) patients and healthy controls (HC), exhibited an excellent diagnosis accuracy with area under curve (AUC) of receiver operating characteristic (ROC) curve reaching 0.988. All these results demonstrate that the FPTA immunomagnetic substrate combined with SERS immunoaffinity probe may become a generic tool for specific isolation and quantitative analysis of PD-L1 positive tumor-derived exosomes in clinics.

Exosomes in cancer diagnosis based on the Latest Evidence: Where are We?

Exosomes are small extracellular vesicles (EVs) derived from various cellular sources and have emerged as favorable biomarkers for cancer diagnosis and prognosis. These vesicles contain a variety of molecular components, including nucleic acids, proteins, and lipids, which can provide valuable information for cancer detection, classification, and monitoring. However, the clinical application of exosomes faces significant challenges, primarily related to the standardization and scalability of their use. In order to overcome these challenges, sophisticated methods such as liquid biopsy and imaging are being combined to augment the diagnostic capabilities of exosomes. Additionally, a deeper understanding of the interaction between exosomes and immune system components within the tumor microenvironment (TME) is essential. This review discusses the biogenesis and composition of exosomes, addresses the current challenges in their clinical translation, and highlights recent technological advancements and integrative approaches that support the role of exosomes in cancer diagnosis and prognosis.

The new advance of exosome-based liquid biopsy for cancer diagnosis

Liquid biopsy is a minimally invasive method that uses biofluid samples instead of tissue samples for cancer diagnosis. Exosomes are small extracellular vesicles secreted by donor cells and act as mediators of intercellular communication in human health and disease. Due to their important roles, exosomes have been considered as promising biomarkers for liquid biopsy. However, traditional methods for exosome isolation and cargo detection methods are time-consuming and inefficient, limiting their practical application. In the past decades, many new strategies, such as microfluidic chips, nanowire arrays and electrochemical biosensors, have been proposed to achieve rapid, accurate and high-throughput detection and analysis of exosomes. In this review, we discussed about the new advance in exosome-based liquid biopsy technology, including isolation, enrichment, cargo detection and analysis approaches. The comparison of currently available methods is also included. Finally, we summarized the advantages and limitations of the present strategies and further gave a perspective to their future translational use.

DNA Nanomaterial-Empowered Surface Engineering of Extracellular Vesicles

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that are critical mediators of intercellular communication. They contain diverse bioactive components, which are promising diagnostic biomarkers and therapeutic agents. Their nanosized membrane-bound structures and innate ability to transport functional cargo across major biological barriers make them promising candidates as drug delivery vehicles. However, the complex biology and heterogeneity of EVs pose significant challenges for their controlled and actionable applications in diagnostics and therapeutics. Recently, DNA molecules with high biocompatibility emerge as excellent functional blocks for surface engineering of EVs. The robust Watson-Crick base pairing of DNA molecules and the resulting programmable DNA nanomaterials provide the EV surface with precise structural customization and adjustable physical and chemical properties, creating unprecedented opportunities for EV biomedical applications. This review focuses on the recent advances in the utilization of programmable DNA to engineer EV surfaces. The biology, function, and biomedical applications of EVs are summarized and the state-of-the-art achievements in EV isolation, analysis, and delivery based on DNA nanomaterials are introduced. Finally, the challenges and new frontiers in EV engineering are discussed.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

Tumor-Derived Extracellular Vesicles as Liquid Biopsy for Diagnosis and Prognosis of Solid Tumors: Their Clinical Utility and Reliability as Tumor Biomarkers

Early cancer detection and accurate monitoring are crucial to ensure increased patient survival. Recent research has focused on developing non-invasive biomarkers to diagnose cancer early and monitor disease progression at low cost and risk. Extracellular vesicles (EVs), nanosized particles secreted into extracellular spaces by most cell types, are gaining immense popularity as novel biomarker candidates for liquid cancer biopsy, as they can transport bioactive cargo to distant sites and facilitate intercellular communications. A literature search was conducted to discuss the current approaches for EV isolation and the advances in using EV-associated proteins, miRNA, mRNA, DNA, and lipids as liquid biopsies. We discussed the advantages and challenges of using these vesicles in clinical applications. Moreover, recent advancements in machine learning as a novel tool for tumor marker discovery are also highlighted.

Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n = 37), nonmetastatic OS patients (n = 13), and metastatic patients (n = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

Small extracellular vesicles: Multi-functional aspects in non-small cell lung carcinoma

Extracellular vesicles (EVs) impact normal and pathological cellular signaling through bidirectional trafficking. Exosomes, a subset of EVs possess biomolecules including proteins, lipids, DNA fragments and various RNA species reflecting a speculum of their parent cells. The involvement of exosomes in bidirectional communication and their biological constituents substantiate its role in regulating both physiology and pathology, including multiple cancers. Non-small cell lung cancer (NSCLC) is the most common lung cancers (85%) with high incidence, mortality and reduced overall survival. Lack of efficient early diagnostic and therapeutic tools hurdles the management of NSCLC. Interestingly, the exosomes from body fluids similarity with parent cells or tissue offers a potential future multicomponent tool for the early diagnosis of NSCLC. The structural twinning of exosomes with a cell/tissue and the competitive tumor derived exosomes in tumor microenvironment (TME) promotes the unpinning horizons of exosomes as a drug delivery, vaccine, and therapeutic agent. Exosomes in clinical point of view assist to trace: acquired resistance caused by various therapeutic agents, early diagnosis, progression, and surveillance. In an integrated approach, EV biomarkers offer potential cutting-edge techniques for the detection and diagnosis of cancer, though the purification, characterization, and biomarker identification processes for the translational research regarding EVs need further optimization.

Sea Anemone-Inspired Conducting Polymer Sensing Platform for Integrated Detection of Tumor Protein Marker and Circulating Tumor Cell

Combining the detection of tumor protein markers with the capture of circulating tumor cells (CTCs) represents an ultra-promising approach for early tumor detection. However, current methodologies have not yet achieved the necessary low detection limits and efficient capture. Here, a novel polypyrrole nanotentacles sensing platform featuring anemone-like structures capable of simultaneously detecting protein biomarkers and capturing CTCs is introduced. The incorporation of nanotentacles significantly enhances the electrode surface area, providing abundant active sites for antibody binding. This enhancement allows detecting nucleus matrix protein22 and bladder tumor antigen with 2.39 and 3.12 pg mL-1 detection limit, respectively. Furthermore, the developed sensing platform effectively captures MCF-7 cells in blood samples with a detection limit of fewer than 10 cells mL-1, attributed to the synergistic multivalent binding facilitated by the specific recognition antibodies and the positive charge on the nanotentacles surface. This sensing platform demonstrates excellent detection capabilities and outstanding capture efficiency, offering a simple, accurate, and efficient strategy for early tumor detection.

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis

Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Long non-coding RNAs and tyrosine kinase-mediated drug resistance in pancreatic cancer

Pancreatic cancer (PC) is one of the most malignant tumors with a dismal survival rate, this is primarily due to inevitable chemoresistance. Dysfunctional tyrosine kinases (TKs) and long non-coding RNAs (lncRNAs) affect the drug resistance and prognosis of PC. Here, we summarize the mechanisms by which TKs or lncRNAs mediate drug resistance and other malignant phenotypes. We also discuss that lncRNAs play oncogenic or tumor suppressor roles and different mechanisms including lncRNA-proteins/microRNAs to mediate drug resistance. Furthermore, we highlight that lncRNAs serve as upstream regulators of TKs mediating drug resistance. Finally, we display the clinical significance of TKs (AXL, EGFR, IGF1R, and MET), clinical trials, and lncRNAs (LINC00460, PVT1, HIF1A-AS1). In the future, TKs and lncRNAs may become diagnostic and prognostic biomarkers or drug targets to overcome the drug resistance of PC.

Exploiting Sound for Emerging Applications of Extracellular Vesicles

Extracellular vesicles are nano- to microscale, membrane-bound particles released by cells into extracellular space, and act as carriers of biomarkers and therapeutics, holding promising potential in translational medicine. However, the challenges remain in handling and detecting extracellular vesicles for disease diagnosis as well as exploring their therapeutic capability for disease treatment. Here, we review the recent engineering and technology advances by leveraging the power of sound waves to address the challenges in diagnostic and therapeutic applications of extracellular vesicles and biomimetic nanovesicles. We first introduce the fundamental principles of sound waves for understanding different acoustic-assisted extracellular vesicle technologies. We discuss the acoustic-assisted diagnostic methods including the purification, manipulation, biosensing, and bioimaging of extracellular vesicles. Then, we summarize the recent advances in acoustically enhanced therapeutics using extracellular vesicles and biomimetic nanovesicles. Finally, we provide perspectives into current challenges and future clinical applications of the promising extracellular vesicles and biomimetic nanovesicles powered by sound.

Ultrasensitive determination of surface proteins on tumor-derived small extracellular vesicles for breast cancer identification based on lanthanide-activated signal amplification strategy

Small extracellular vesicles (sEVs) carrying multiple tumor-associated proteins inherited from parental cells play crucial roles in noninvasive breast cancer (BC) diagnosis. However, it is challenging to assess the subtle variations of surface proteins on sEV membranes due to the highly heterogeneous BC. Therefore, a simple and ultrasensitive assay based on lanthanide (Ln3+)-activated luminescence signal amplification was developed to detect multiple surface proteins on BC-derived sEVs. Multiple protein biomarkers on sEVs can be well identified with high sensitivity and specificity through dissolution-amplified luminescence of the NaEuF4 nanoparticle-based nanoprobe. We employ linear discriminant analysis to successfully discriminate triple negative BC cell (MDA-MB-231 cell) derived sEVs from other breast cell lines (MCF-7, SK-BR-3, BT474 and MCF-10A cell). Furthermore, the strategy enables high accuracy for districting the progression stages of BC patients and healthy donors. The simple and sensitive signal amplification strategy exhibits great potential for early clinic diagnosis by precise protein profiling of sEVs.

Exosomes as Targeted Delivery Drug System: Advances in Exosome Loading, Surface Functionalization and Potential for Clinical Application

Exosomes are subtypes of vesicles secreted by almost all cells and can play an important role in intercellular communication. They contain various proteins, lipids, nucleic acids and other natural substances from their metrocytes. Exosomes are expected to be a new generation of drug delivery systems due to their low immunogenicity, high potential to transfer bioactive substances and biocompatibility. However, exosomes themselves are not highly targeted, it is necessary to develop new surface modification techniques and targeted drug delivery strategies, which are the focus of drug delivery research. In this review, we introduced the biogenesis of exosomes and their role in intercellular communication. We listed various advanced exosome drug-loading techniques. Emphatically, we summarized different exosome surface modification techniques and targeted drug delivery strategies. In addition, we discussed the application of exosomes in vaccines and briefly introduced milk exosomes. Finally, we clarified the clinical application prospects and shortcomings of exosomes.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Exosomes Barcoding: A smart approach for cancer liquid biopsy

Cancer is an unsolved health crisis worldwide. Extracellular vesicles (EVs) address this problem in a new way. In cancer, early detection is highly challenging, exosomes (a subpopulation of EVs, originating from endosomes) overcomes this limitation. In cancer, tumor-derived exosomes (TEXs) play a role as signaling molecules in cancer development and progression. TEXs provide detailed investigation for specific cancer biomarkers research. Exosomes heterogeneity (variation in exosomes size, exosomes origin, and inner molecular diversity) has led to complications in understanding and studying cancer liquid biopsies. Single exosome profiling and exosomes barcoding has helped in supporting and overcoming this limitation and has played a significant role in precision oncology. Exosomes barcoding is a promising interdisciplinary approach for screening cancers more specifically.

Role of exosomes in prostate cancer and male fertility

Prostate cancer (PCa) is the second most common and fifth most aggressive neoplasm among men worldwide. In the last decade, extracellular vesicle (EV) research has decoded multiple unsolved cancer-related mysteries. EVs can be classified as microvesicles, apoptotic bodies, and exosomes, among others. Exosomes play a key role in cellular signaling. Their internal cargos (nucleic acids, proteins, lipids) influence the recipient cell. In PCa, the exosome is the regulator of cancer progression. It is also a promising theranostics tool for PCa. Moreover, exosomes have strong participation in male fertility complications. This review aims to highlight the exosome theranostics signature in PCa and its association with male fertility.

Unlocking Exosome-Based Theragnostic Signatures: Deciphering Secrets of Ovarian Cancer Metastasis

Ovarian cancer (OC) is a common gynecological cancer worldwide. Unfortunately, the lack of early detection methods translates into a substantial cohort of women grappling with the pressing health crisis. The discovery of extracellular vesicles (EVs) (their major subpopulation exosomes, microvesicles, and apoptotic bodies) has provided new insights into the understanding of cancer. Exosomes, a subpopulation of EVs, play a crucial role in cellular communication and reflect the cellular status under both healthy and pathological conditions. Tumor-derived exosomes (TEXs) dynamically influence ovarian cancer progression by regulating uncontrolled cell growth, immune suppression, angiogenesis, metastasis, and the development of drug and therapeutic resistance. In the field of OC diagnostics, TEXs offer potential biomarkers in various body fluids. On the other hand, exosomes have also shown promising abilities to cure ovarian cancer. In this review, we address the interlink between exosomes and ovarian cancer and explore their theragnostic signature. Finally, we highlight future directions of exosome-based ovarian cancer research.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

Long-lived SERS Matrix for Real-Time Biochemical Detection Using "Frozen" Transition State

For the long-time tracking of biological events, maintaining the bioactivity of the analytes during the detection process is essential. Here, we show a versatile surface-enhanced Raman Scattering (SERS) platform, termed a superwettable-omniphobic lubricous porous SERS (SOLP-SERS) substrate. The SOLP-SERS substrate could generate a three-dimensional liquid "hotspots" matrix with an ultra-long lifetime (tens of days) by confining tiny amounts of liquids within the gaps between nanoparticles. Then, the analytes are trapped in the uniform liquid "hotspots", whose bioactivity can be well maintained over a long period of time during SERS detection. Limits of detection down to femtomolar levels were achieved for various molecules. More importantly, SERS signals were uniform within the substrate and remained stable for more than 30 days. As a proof-of-concept experiment, the dynamic detection of the polymerization of Aβ peptides into amyloids was monitored by the SOLP-SERS substrate within 48 h. Moreover, the exosomes secreted by breast cancer cells, an important biomarker of cancer, were also measured. These results demonstrate that the SOLP-SERS platform will provide new insights into the development of real-time biochemical sensors with ultrahigh sensitivity.

Application of Single Extracellular Vesicle Analysis Techniques

Extracellular vesicles (EVs) are lipid containers that are actively released by cells and contain complex molecular cargoes. These cargoes include abundant material such as genomes and proteins from cells of origin. They are involved in intercellular communication and various pathological processes, showing excellent potential for diagnosing and treating diseases. Given the significant heterogeneity of EVs in complex physiopathological processes, unveiling their composition is essential to understanding their function. Bulk detection methods have been previously used to analyze EVs, but they often mask their heterogeneity, leading to the loss of valuable information. To overcome this limitation, single extracellular vesicle (SEV) analysis techniques have been developed and advanced. These techniques allow for analyzing EVs' physical information and biometric molecules at the SEV level. This paper reviews recent advances in SEV detection methods and summarizes some clinical applications for SEV detection strategies.

Precise Identification and Profiling of Surface Proteins of Ultra Rare Tumor Specific Extracellular Vesicle with Dynamic Quantitative Plasmonic Imaging

Specific detection of tumor-derived EVs (tEVs) in plasma is complicated by nontumor EVs and non-EV particles. To accurately identify tEVs and profile their surface protein expression at single tEV resolution directly with clinical plasma is still an unmet need. Here, we present a Dynamic Immunoassay for Single tEV surface protein Profiling (DISEP), a kinetic assay based on surface plasmon resonance microscopy (SPRM) for specific single tEV profiling. DISEP adopts a pair of low-affinity oligonucleotide probes to respectively label EV surface proteins and functionalize an SPRM biosensor interface. tEVs labeled with the oligonucleotide probes possess distinctive binding kinetics from nonspecific particles in plasma, which permits accurate digital plasmonic counting of single EVs. We demonstrate DISEP for recognizing target EVs among 350-fold background plasma particles with high sensitivity (4677 EVs per μL). Clinical plasma samples were analyzed to discriminate between pancreatic cancer patients (n = 40) and healthy donors (n = 45). With a panel of biomarker signatures (EpCAM, HER2, and GPC1), DISEP only requires 10 μL primary sample from each donor to classify tumor patients with an area under the curve of 0.98. DISEP provides a highly specific EV detection and surface protein profiling strategy for early cancer diagnosis.

Clinical Theranostics Trademark of Exosome in Glioblastoma Metastasis

Glioblastoma (GBM) is an aggressive type of cancer that has led to the death of a large population. The traditional approach fails to develop a solution for GBM's suffering life. Extensive research into tumor microenvironments (TME) indicates that TME extracellular vesicles (EVs) play a vital role in cancer development and progression. EVs are classified into microvacuoles, apoptotic bodies, and exosomes. Exosomes are the most highlighted domains in cancer research. GBM cell-derived exosomes participate in multiple cancer progression events such as immune suppression, angiogenesis, premetastatic niche formation (PMN), ECM (extracellular matrix), EMT (epithelial-to-mesenchymal transition), metastasis, cancer stem cell development and therapeutic and drug resistance. GBM exosomes also carry the signature of a glioblastoma-related status. The exosome-based GBM examination is part of the new generation of liquid biopsy. It also solved early diagnostic limitations in GBM. Traditional therapeutic approaches do not cross the blood-brain barrier (BBB). Exosomes are a game changer in GBM treatment and it is emerging as a potential platform for effective, efficient, and specific therapeutic development. In this review, we have explored the exosome-GBM interlink, the clinical impact of exosomes on GBM biomarkers, the therapeutics signature of exosomes in GBM, exosome-based research challenges, and future directions in GBM. Therefore, the GBM-derived exosomes offer unique therapeutic opportunities, which are currently under preclinical and clinical testing.

MoS2-Plasmonic Nanocavities for Raman Spectra of Single Extracellular Vesicles Reveal Molecular Progression in Glioblastoma

Extracellular vesicles (EVs) are continually released from cancer cells into biofluids, carrying actionable molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. The scarcity, heterogeneity and intrinsic complexity of tumor EVs present a major technological challenge in real-time monitoring of complex cancers such as glioblastoma (GBM). Surface-enhanced Raman spectroscopy (SERS) outputs a label-free spectroscopic fingerprint for EV molecular profiling. However, it has not been exploited to detect known biomarkers at the single EV level. We developed a multiplex fluidic device with embedded arrayed nanocavity microchips (MoSERS microchip) that achieves 97% confinement of single EVs in a minute amount of fluid (<10 μL) and enables molecular profiling of single EVs with SERS. The nanocavity arrays combine two featuring characteristics: (1) An embedded MoS₂ monolayer that enables label-free isolation and nanoconfinement of single EVs due to physical interaction (Coulomb and van der Waals) between the MoS₂ edge sites and the lipid bilayer; and (2) A layered plasmonic cavity that enables sufficient electromagnetic field enhancement inside the cavities to obtain a single EV level signal resolution for stratifying the molecular alterations. We used the GBM paradigm to demonstrate the diagnostic potential of the SERS single EV molecular profiling approach. The MoSERS multiplexing fluidic achieves parallel signal acquisition of glioma molecular variants (EGFRvIII oncogenic mutation and MGMT expression) in GBM cells. The detection limit of 1.23% was found for stratifying these key molecular variants in the wild-type population. When interfaced with a convolutional neural network (CNN), MoSERS improved diagnostic accuracy (87%) with which GBM mutations were detected in 12 patient blood samples, on par with clinical pathology tests. Thus, MoSERS demonstrates the potential for molecular stratification of cancer patients using circulating EVs.

Quantification of GPC1(+) Exosomes Based on MALDI-TOF MS In Situ Signal Amplification for Pancreatic Cancer Discrimination and Evaluation

Pancreatic cancer (PC) has a high mortality, with a fairly low five-year survival rate, because of its delayed diagnosis. Recently, liquid biopsy, especially based on exosomes, has attracted vast attention, thanks to its low invasiveness. Herein, we constructed a protocol for pancreatic cancer related Glypican 1 (GPC1) exosome quantification, based on in situ mass spectrometry signal amplification, by utilizing mass tag molecules on gold nanoparticles (AuNPs). Exosomes were extracted and purified by size-exclusion chromatography (SEC), captured by TiO₂ modified magnetic nanoparticles, and then targeted specifically by anti-GPC1 antibody modified on AuNPs. With matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), the signal of PC biomarker, GPC1, was converted to a mass tag signal and amplified. With addition of a certain amount of internal standard molecules modified on AuNPs, the relative intensity ratio of mass tag to internal standard was proportional to the concentration of GPC1(+) exosomes derived from pancreatic cancer cell lines, PANC-1, with good linearity (R² = 0.9945) in a wide dynamic range from 7.1 × 10 to 7.1 × 10⁶ particles/μL. This method was further applied to plasma samples from healthy control (HC) and pancreatic cancer patients with different tumor load, and exhibited a great potential in discriminating diagnosed PC patients from HC, and has the monitoring potential in PC progression.

Fluorescence labeling of extracellular vesicles for diverse bio-applications in vitro and in vivo

Extracellular vesicles (EVs) are nanosized vesicles enclosed in a lipid membrane that are sustainably released by nearly all cell types. EVs have been deemed as valuable biomarkers for diagnostics and effective drug carriers, owing to the physiological function of transporting biomolecules for intercellular communication. To investigate their biological properties, efficient labeling strategies have been constructed for EV research, among which fluorescence labeling exerts a powerful function due to the capability of visualizing the nanovesicles with high sensitivity both in vitro and in vivo. In one aspect, with the help of functional fluorescence tags, EVs could be differentiated and categorized in vitro by various analytical techniques, which exert vital roles in disease diagnosis, prognosis, and treatment monitoring. Additionally, innovative EV reporters have been utilized for visualizing EVs, in combination with powerful microscopy techniques, which provide potential tools for investigating the dynamic events of EV release and intercellular communication in suitable animal models. In this feature article, we survey the latest advances regarding EV fluorescence labeling strategies and their application in biomedical application and in vivo biology investigation, highlighting the progresses in individual EV imaging. Finally, the challenges and future perspectives in unravelling EV physiological properties and further biomedical application are discussed.

Combined Ultrasensitive Detection of Renal Cancer Proteins and Cells Using an Optical Microfiber Functionalized with Ti3C2 MXene and Gold Nanorod-Nanosensitized Interfaces

The ultrasensitive and quantitative detection of renal cancer protein biomarkers present at ultralow concentrations for early-stage cancer diagnosis requires a biosensing probe possessing ultrahigh detection sensitivity and remarkable biosensing selectivity. Here, we report an optical microfiber integrated with Ti₃C₂-supported gold nanorod hybrid nanointerfaces for implementation in ultrasensitive sensing of the carbonic anhydrase IX (CAIX) protein and renal cancer cells. Because the evanescent field of the fiber is strongly coupled with nanointerfaces in the near-infrared region, the proposed optical microfiber biosensor achieves ultrahigh-sensitivity detection of the CAIX protein biomarker with ultralow limits of detection (LODs) of 13.8 zM in pure buffer solution and 0.19 aM in 30% serum solution. In addition, the proposed sensor also successfully and specifically recognizes living renal cancer cells in cell culture media with a LOD of 180 cells/mL. This strategy may serves as a powerful biosensing platform that combines the quantification of protein biomarkers and cancer cells, resulting in a higher accuracy of early-stage renal cancer diagnosis and screenings.

From Conventional to Microfluidic: Progress in Extracellular Vesicle Separation and Individual Characterization

Extracellular vesicles (EVs) are nanoscale membrane vesicles, which contain a wide variety of cargo such as proteins, miRNAs, and lipids. A growing body of evidence suggests that EVs are promising biomarkers for disease diagnosis and therapeutic strategies. Although the excellent clinical value, their use in personalized healthcare practice is not yet feasible due to their highly heterogeneous nature. Taking the difficulty of isolation and the small size of EVs into account, the characterization of EVs at a single-particle level is both imperative and challenging. In a bid to address this critical point, more research has been directed into a microfluidic platform because of its inherent advantages in sensitivity, specificity, and throughput. This review discusses the biogenesis and heterogeneity of EVs and takes a broad view of state-of-the-art advances in microfluidics-based EV research, including not only EV separation, but also the single EV characterization of biophysical detection and biochemical analysis. To highlight the advantages of microfluidic techniques, conventional technologies are included for comparison. The current status of artificial intelligence (AI) for single EV characterization is then presented. Furthermore, the challenges and prospects of microfluidics and its combination with AI applications in single EV characterization are also discussed. In the foreseeable future, recent breakthroughs in microfluidic platforms are expected to pave the way for single EV analysis and improve applications for precision medicine.

Metal nanoprobe-decorated all-inorganic perovskite nanocrystal-based fluorescence-linked immunosorbent assay for the detection of tumor-derived exosomes

All-inorganic perovskite nanocrystals (CsPbX₃ NCs, X = Cl, Br, I) are promising fluorescence materials for biological detection due to their excellent optical properties. However, there is still a challenge to obtain stable CsPbX₃ NCs with more biofunctions. Here, we proposed a distinct strategy by absorbing the functionalized metal nanoprobes onto the phospholipid encapsulated CsPbX₃ NCs to achieve CsPbX₃-metal hybrids as probes for the detection of tumor-derived exosomes. Here, the metal nanoprobes have two functions: first, it endows phospholipid encapsulated CsPbX₃ NCs with recognition ability; second, it avoids the fluorescence quenching of CsPbX₃ NCs during the biological modification process by using metal nanoparticles as a bridge to connect with CsPbX₃ NCs and various biomolecules. The obtained CPXD-AD exhibited a bright fluorescence signal, narrow full width at half-maximum (FWHM), and high specificity. Under optimal conditions, the CPXD-AD-based fluorescence-linked immunosorbent assay (FLISA) was successfully established and used for both qualitative and quantitative detection of tumor-derived exosomes.

Precision and Accuracy of Receptor Quantification on Synthetic and Biological Surfaces Using DNA-PAINT

Characterization of the number and distribution of biological molecules on 2D surfaces is of foremost importance in biology and biomedicine. Synthetic surfaces bearing recognition motifs are a cornerstone of biosensors, while receptors on the cell surface are critical/vital targets for the treatment of diseases. However, the techniques used to quantify their abundance are qualitative or semi-quantitative and usually lack sensitivity, accuracy, or precision. Detailed herein a simple and versatile workflow based on super-resolution microscopy (DNA-PAINT) was standardized to improve the quantification of the density and distribution of molecules on synthetic substrates and cell membranes. A detailed analysis of accuracy and precision of receptor quantification is presented, based on simulated and experimental data. We demonstrate enhanced accuracy and sensitivity by filtering out non-specific interactions and artifacts. While optimizing the workflow to provide faithful counting over a broad range of receptor densities. We validated the workflow by specifically quantifying the density of docking strands on a synthetic sensor surface and the densities of PD1 and EGF receptors (EGFR) on two cellular models.

Current Advances in Technologies for Single Extracellular Vesicle Analysis and Its Clinical Applications in Cancer Diagnosis

Extracellular vesicles (EVs) have been regarded as one of the most potential diagnostic biomarkers for different cancers, due to their unique physiological and pathological functions. However, it is still challenging to precisely analyze the contents and sources of EVs, due to their heterogeneity. Herein, we summarize the advances in technologies for a single EV analysis, which may provide new strategies to study the heterogeneity of EVs, as well as their cargo, more specifically. Furthermore, the applications of a single EV analysis on cancer early diagnosis are also discussed.

Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool

Principal component analysis (PCA) as a machine-learning technique could serve in disease diagnosis and prognosis by evaluating the dynamic morphological features of exosomes via Cryo-TEM-imaging. This hypothesis was investigated after the crude isolation of similarly featured exosomes derived from the extracellular vehicles (EVs) of immature dendritic cells (IDCs) JAWSII. It is possible to identify functional molecular groups by FTIR, but the unique physical and morphological characteristics of exosomes can only be revealed by specialized imaging techniques such as cryo-TEM. On the other hand, PCA has the ability to examine the morphological features of each of these IDC-derived exosomes by considering software parameters such as various membrane projections and differences in Gaussians, Hessian, hue, and class to assess the 3D orientation, shape, size, and brightness of the isolated IDC-derived exosome structures. In addition, Brownian motions from nanoparticle tracking analysis of EV IDC-derived exosomes were also compared with EV IDC-derived exosome images collected by scanning electron microscopy and confocal microscopy. Sodium-Dodecyl-Sulphate-Polyacrylamide-Gel-Electrophoresis (SDS-PAGE) was performed to separate the protein content of the crude isolates showing that no considerable protein contamination occurred during the crude isolation technique of IDC-derived-exosomes. This is an important finding because no additional purification of these exosomes is required, making PCA analysis both valuable and novel.

Understanding the versatile roles and applications of EpCAM in cancers: from bench to bedside

Epithelial cell adhesion molecule (EpCAM) functions not only in physiological processes but also participates in the development and progression of cancer. In recent decades, extensive efforts have been made to decipher the role of EpCAM in cancers. Great advances have been achieved in elucidating its structure, molecular functions, pathophysiological mechanisms, and clinical applications. Beyond its well-recognized role as a biomarker of cancer stem cells (CSCs) or circulating tumor cells (CTCs), EpCAM exhibits novel and promising value in targeted therapy. At the same time, the roles of EpCAM in cancer progression are found to be highly context-dependent and even contradictory in some cases. The versatile functional modules of EpCAM and its communication with other signaling pathways complicate the study of this molecule. In this review, we start from the structure of EpCAM and focus on communication with other signaling pathways. The impacts on the biology of cancers and the up-to-date clinical applications of EpCAM are also introduced and summarized, aiming to shed light on the translational prospects of EpCAM.

Quantitative analysis of multiple breast cancer biomarkers using DNA-PAINT

Immunotherapy has become an efficient treatment method of breast cancer. Detection of proteins such as PD-L1 and CTLA-4, which are important immune checkpoint molecules, is attracting more and more attention as they play key roles in immunotherapy. Here, by combining the high resolution of DNA-PAINT (DNA points accumulation for imaging in nanoscale topography) with the qPAINT quantitative analysis method, accurate spatial localization and absolute quantification of PD-L1 and CTLA-4 on the membrane of breast cancer cells could be achieved. Meanwhile, exchange-PAINT was also conducted to count three other biomarkers (EpCAM, EGFR, and HER2). Simultaneous analysis of these biomarkers can greatly facilitate the differentiation of different kinds of breast cancer. Such a simple quantitative analysis method holds great potential in diagnosis and immunotherapy of cancers.

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Highly Sensitive Exosome Detection for Early Diagnosis of Pancreatic Cancer Using Immunoassay Based on Hierarchical Surface-Enhanced Raman Scattering Substrate

Exosomes have emerged as potential biomarkers for pancreatic cancer (PaC). However, it is still challenging to get quantitative detection of exosomes with the specific surface receptors. In this study, a highly sensitive detection system is first constructed for the direct quantitation of specific exosomes in real samples using hierarchical surface-enhanced Raman scattering substrate (H-SERS substrate) and rapid enrichment strategy magnetic beads @ exosomes @ SERS detection probes (MEDP). It is found that the detection system (MEDP @ H-SERS substrate) could provide a 3.5 times higher SERS intensity compared with MEDP sandwich immunocomplex only. Moreover, LRG1-positive exosomes (LRG1-Exos) and GPC1-positive exosomes (GPC1-Exos) are chosen to distinguish PaC through exosome proteomics and database screening. The lower limit of detection (LOD) is 15 particles µL-1 using the MEDP @ H-SERS substrate. Significantly, the detection in clinical samples shows that the innovative combination of LRG1-Exos and GPC1-Exos could improve the diagnostic efficiency of PaC, with an area under the operating characteristic curve (AUC) of 0.95. Even for the early-stage PaC, the diagnostic accuracy is still high (AUC = 0.95). Collectively, the findings indicate that the MEDP @ H-SERS substrate has great potential for the early diagnosis of PaC.

Insight of pancreatic cancer: recommendations for improving its therapeutic efficacy in the next decade

Pancreatic cancer is one of the most malignant digestive system tumors. The effectiveness of pancreatic cancer treatment is still dismal, and the 5-year survival rate is only about 10%. Further improving the diagnosis and treatment of pancreatic cancer is the top priority of oncology research and clinical practice. Based on the existing clinical and scientific research experience, the review provides insight into the hotspots and future directions for pancreatic cancer, which focuses on early detection, early diagnosis, molecular typing and precise treatment, new drug development and regimen combination, immunotherapy, database development, model establishment, surgical technology and strategy change, as well as innovation of traditional Chinese medicine and breakthrough of treatment concept.

Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.