Qingfu Zhu

Salivary extracellular vesicles (EVs) have been recognized as one of the most promising noninvasive sources of biomarkers for early cancer detection. However, the lack of efficient isolation and accurate identification methods for salivary EVs limits their clinical utility in early cancer diagnosis. Here, we report the rapid and precise identification of esophageal cancer using the Saliva Extracellular vesicle-based Early Diagnostic system (SEEDx). In this system, salivary EVs were isolated, purified, amplified, and analyzed from human saliva by integrating the ultrafast-isolation platform EXODUS with tandem mass tag (TMT)-based proteomics analysis (EXODUS-TMT proteomics). Using TCGA esophageal cancer (EC) and GTEx healthy population cohorts, we developed a diagnostic model consisting of 9 markers (HIST2H2BE, TP53BP2, TPM4, CALR, HDGF, LMNA, GUSB, NADSYN1, and AGRN), which demonstrated high diagnostic accuracy with AUC values of 0.996 for EC and 0.991 for early-stage EC. We further established a cost-effective model using only two markers (HDGF and CALR) to diagnose both EC and early-stage EC, achieving AUC values of 0.950 and 0.935, respectively. This model was validated in an independent, prospectively collected cohort of EC patients, yielding AUC values of 0.873 and 0.854, respectively. Our streamlined EV isolation and identification workflow facilitates noninvasive biomarker discovery for early-stage esophageal cancer detection. This study extends the EXODUS platform to salivary EVs for EC detection, documents matrix-specific workflow optimizations, and identifies saliva-derived biomarker signatures integrated into a multimarker diagnostic model.

Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extracellular vesicle

Cells secrete extracellular vesicles (EVs) to mediate precise communication during viral infections, yet the spatiotemporal regulation of EV composition by herpes simplex virus 1 (HSV-1) remains poorly understood. Here, we develop an integrated single-nanoparticle analysis platform combining nanoporous membrane-based EV isolation with an on-chip immunoassay to quantitatively probe EV-HSV-1 interplay throughout infection. A dual-membrane filter design significantly enhances nanoparticle recovery, enabling high-sensitivity single-particle detection. We reveal that HSV-1-infected neural stem cells display viral glycoprotein B on EV surfaces at an early stage (<8 hpi), while intact virions are selectively packaged into EVs later (24-48 hpi). Proteomic profiling indicates infected cell-derived EVs facilitate antigen processing and presentation, potentially amplifying antiviral responses. Functional studies further demonstrate EVs promote viral entry at late stages (48 hpi), likely via EV-virion encapsulation. These findings elucidate a dynamic EV-virus interplay, offering insights into HSV-1 pathogenesis and EV-mediated immune modulation. Our platform provides a transformative approach for advancing infection diagnostics and therapeutics.

Achieving precise delivery of extracellular vesicles (EVs) to treat pulmonary arterial hypertension (PAH) remains challenging. Here, we propose a strategy using hypoxia-induced and glucuronic acid (GA)-modified mesenchymal stromal-cell-derived EVs (MSC-EVs) to enhance their functionalities and therapeutic targeting. The hypoxia-induced EVs (Hypo-EVs) exhibit enriched exosomal signatures and display heightened inhibition of the proliferation of pulmonary arterial smooth muscle cells (PASMCs) compared to normoxic EVs (Norm-EV). We then modify Hypo-EVs by incorporating GA into their outer membrane, targeting glucose transporter-1 overexpressed on PASMCs. Our studies show that GA-EVs significantly enhance the therapeutic efficacy, both in vitro and in vivo, through improved targeted delivery to diseased PASMCs for improving vascular remodeling. Additionally, we identify miR-5119 involved in the PAH-associated calcium signaling pathway as a key contributor to GA-EVs' superior effects. This work provides a promising strategy for PAH treatment and advances the clinical potential of MSC-EV-based therapies.

Extracellular vesicles (EVs) are increasingly acknowledged as important mediators of intercellular communication, closely related to the occurrence and development of a variety of diseases. Numerous studies have demonstrated that EVs play a multifaceted role in the infection process of viral diseases, elucidating their ability to both facilitate viral spread and inhibit infection progression. These versatile entities not only enhance infection rates and widen the scope of viral infection through the transmission of entire viruses or viral genomes, but also trigger antiviral responses and prompt cytokine secretion near the infection site, thereby fortifying the host's defense mechanisms and safeguarding neighboring cells against infection. This complicated crosstalk between EVs and viral infections prompts a deeper exploration into their roles in potential clinical applications. In this review, we aim to encapsulate the recent advances in understanding the intricate interplay between viruses and EVs, shedding light on the mechanisms underlying this vesicle-to-virion crosstalk. Furthermore, we underscore the significance of harnessing this knowledge for diagnostic and therapeutic functions in combating viral diseases.

Extracellular vesicles (EVs) and lipoproteins (LPPs) serve as important carriers of circulating miRNAs in peripheral blood, offering immense potential for disease diagnosis and therapeutic interventions. Due to their shared physicochemical attributes, EVs and LPPs are frequently coisolated, potentially leading to misunderstandings regarding their distinct functional roles in physiological and pathological processes. Here, we report a highly selective magnetic system based on the pH-mediated affinity displayed by cibacron blue (CB) toward EVs and LPPs, enabling successful separation and collection of these two nanoparticles without cross-contamination for subsequent circulating RNA analysis. First, we found that CB-modified magnetic beads (CBMBs) exhibit a strong affinity toward LPP particles while displaying little interaction with EVs in standard samples under physiological pH conditions. We further demonstrate that the affinity between CB molecules and bionanoparticles in plasma samples is highly pH-dependent. Specifically, CBMBs show affinities for both LPP and EV particles under neutral and acidic conditions. However, at basic pH levels, CB molecules selectively bind only to LPP particles. Consequently, the remaining EV particles present in plasma are subsequently isolated by using titanium dioxide-modified beads (TiMBs) through phospholipid affinity. The simultaneous analysis of the transcriptomic contents of EV and LPP reveals clear differences in their small RNA profiles, with the differentially expressed RNAs reflecting distinct biological processes. Significantly, in a proof-of-concept study, we successfully demonstrated a strong correlation between miRNAs carried by both EV and LPP particles with the occurrence of ocular neovascularization during the progression of diabetic retinopathy. The involved miRNAs may serve as potential biomarkers for DR diagnostics and severity classification. To sum up, this pH-mediated separation system is not only user-friendly but also highly compatible, rendering it a potent tool for probing the molecular compositions, biomarkers, and underlying biological mechanisms of EVs and LPPs.

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

The occurrence of acute pancreatitis (AP) is increasing significantly worldwide. However, current diagnostic methods of AP do not provide a clear clinical stratification of severity, and the prediction of complications in AP is still limited. Here, we present a robust AP identification and diagnosis (RAPIDx) method by the proteomic fingerprinting of intact nanoscale extracellular vesicles (EVs) from clinical samples. By tracking analysis of circulating biological nanoparticles released by cells (i.e., EVs) via bottom-up proteomics, we obtain close phenotype connections between EVs, cell types, and multiple tissues based on their specific proteomes and identify the serum amyloid A (SAA) proteins on EVs as potential biomarkers that are differentially expressed from AP patients significantly. We accomplish the quantitative analysis of EVs fingerprints using MALDI-TOF MS and find the SAA proteins (SAA1-1, desR-SAA1-2, SAA2, SAA1-2) with areas under the curve (AUCs) from 0.92 to 0.97, which allows us to detect AP within 30 min. We further realize that SAA1-1 and SAA2, combined with two protein peaks (5290.19, 14032.33 m/z), can achieve an AUC of 0.83 for classifying the severity of AP. The RAPIDx platform will facilitate timely diagnosis and treatment of AP before severity development and persistent organ failure and promote precision diagnostics and the early diagnosis of pancreatic cancer.