Inaugurating High-Throughput Profiling of Extracellular Vesicles for Earlier Ovarian Cancer Detection

Amplifying mutational profiling of extracellular vesicle mRNA with SCOPE

Sequencing of messenger RNA (mRNA) found in extracellular vesicles (EVs) in liquid biopsies can provide clinical information such as somatic mutations, resistance profiles and tumor recurrence. Despite this, EV mRNA remains underused due to its low abundance in liquid biopsies, and large sample volumes or specialized techniques for analysis are required. Here we introduce Self-amplified and CRISPR-aided Operation to Profile EVs (SCOPE), a platform for EV mRNA detection. SCOPE leverages CRISPR-mediated recognition of target RNA using Cas13 to initiate replication and signal amplification, achieving a sub-attomolar detection limit while maintaining single-nucleotide resolution. As a proof of concept, we designed probes for key mutations in KRAS, BRAF, EGFR and IDH1 genes, optimized protocols for single-pot assays and implemented an automated device for multi-sample detection. We validated SCOPE's ability to detect early-stage lung cancer in animal models, monitored tumor mutational burden in patients with colorectal cancer and stratified patients with glioblastoma. SCOPE can expedite readouts, augmenting the clinical use of EVs in precision oncology.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Addressing Heterogeneity in Direct Analysis of Extracellular Vesicles and Their Analogs by Membrane Sensing Peptides as Pan-Vesicular Affinity Probes

Extracellular vesicles (EVs), crucial mediators of cell-to-cell communication, hold significant diagnostic potential due to their ability to concentrate protein biomarkers in bodily fluids. However, challenges in isolating EVs from biological specimens hinder their widespread use. The preferred strategy involves direct analysis, integrating isolation and analysis solutions, with immunoaffinity methods currently dominating. Yet, the heterogeneous nature of EVs poses challenges, as proposed markers may not be as universally present as thought, raising concerns about biomarker screening reliability. This issue extends to EV-mimics, where conventional methods may lack applicability. Addressing these challenges, the study reports on Membrane Sensing Peptides (MSP) as pan-vesicular affinity ligands for both EVs and their non-canonical analogs, streamlining capture and phenotyping through Single Molecule Array (SiMoA). MSP ligands enable direct analysis of circulating EVs, eliminating the need for prior isolation. Demonstrating clinical translation, MSP technology detects an EV-associated epitope signature in serum and plasma, distinguishing myocardial infarction from stable angina. Additionally, MSP allow analysis of tetraspanin-lacking Red Blood Cell-derived EVs, overcoming limitations associated with antibody-based methods. Overall, the work underlines the value of MSP as complementary tools to antibodies, advancing EV analysis for clinical diagnostics and beyond, and marking the first-ever peptide-based application in SiMoA technology.

Integrated miRNA Profiling of Extracellular Vesicles from Uterine Aspirates, Malignant Ascites and Primary-Cultured Ascites Cells for Ovarian Cancer Screening

Extracellular vesicles (EVs) are of growing interest in the context of screening for highly informative cancer markers. We have previously shown that uterine aspirate EVs (UA EVs) are a promising source of ovarian cancer (OC) diagnostic markers. In this study, we first conducted an integrative analysis of EV-miRNA profiles from UA, malignant ascitic fluid (AF), and a conditioned medium of cultured ascites cells (ACs). Using three software packages, we identified 79 differentially expressed miRNAs (DE-miRNAs) in UA EVs from OC patients and healthy individuals. To narrow down this panel and select miRNAs most involved in OC pathogenesis, we aligned these molecules with the DE-miRNA sets obtained by comparing the EV-miRNA profiles from OC-related biofluids with the same control. We found that 76% of the DE-miRNAs from the identified panel are similarly altered (differentially co-expressed) in AF EVs, as are 58% in AC EVs. Interestingly, the set of miRNAs differentially co-expressed in AF and AC EVs strongly overlaps (40 out of 44 miRNAs). Finally, the application of more rigorous criteria for DE assessment, combined with the selection of miRNAs that are differentially co-expressed in all biofluids, resulted in the identification of a panel of 29 miRNAs for ovarian cancer screening.

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.

Regulation of the Function and Expression of EpCAM

The epithelial cell adhesion molecule (EpCAM) is a single transmembrane protein on the cell surface. Given its strong expression on epithelial cells and epithelial cell-derived tumors, EpCAM has been identified as a biomarker for circulating tumor cells (CTCs) and exosomes and a target for cancer therapy. As a cell adhesion molecule, EpCAM has a crystal structure that indicates that it forms a cis-dimer first and then probably a trans-tetramer to mediate intercellular adhesion. Through regulated intramembrane proteolysis (RIP), EpCAM and its proteolytic fragments are also able to regulate multiple signaling pathways, Wnt signaling in particular. Although great progress has been made, increasingly more findings have revealed the context-specific expression and function patterns of EpCAM and their regulation processes, which necessitates further studies to determine the structure, function, and expression of EpCAM under both physiological and pathological conditions, broadening its application in basic and translational cancer research.

Advanced Nanoencapsulation-Enabled Ultrasensitive Analysis: Unraveling Tumor Extracellular Vesicle Subpopulations for Differential Diagnosis of Hepatocellular Carcinoma via DNA Cascade Reactions

Tumor-derived extracellular vesicles (tEVs) hold immense promise as potential biomarkers for the precise diagnosis of hepatocellular carcinoma (HCC). However, their clinical translation is hampered by their inherent characteristics, such as small size and high heterogeneity and complex environment, including non-EV particles and normal cell-derived EVs, which prolong separation procedures and compromise detection accuracy. In this study, we devised a DNA cascade reaction-triggered individual EV nanoencapsulation (DCR-IEVN) strategy to achieve the ultrasensitive and specific detection of tEV subpopulations via routine flow cytometry in a one-pot, one-step fashion. DCR-IEVN enables the direct and selective packaging of multiple tEV subpopulations in clinical serum samples into flower-like particles exceeding 600 nm. This approach bypasses the need for EV isolation, effectively reducing interference from non-EV particles and nontumor EVs. Compared with conventional analytical technologies, DCR-IEVN exhibits superior efficacy in diagnosing HCC owing to its high selectivity for tEVs. Integration of machine learning algorithms with DCR-IEVN resulted in differential diagnosis accuracy of 96.7% for the training cohort (n = 120) and 93.3% for the validation cohort (n = 30), effectively distinguishing HCC, cirrhosis, and healthy donors. This strategy offers a streamlined workflow and rapid assay completion and requires only small-volume serum samples and routine clinical devices, facilitating the clinical translation of tEV-based tumor diagnosis.

Identification of potential biomarkers for aging diagnosis of mesenchymal stem cells derived from the aged donors

BACKGROUND

The clinical application of human bone-marrow derived mesenchymal stem cells (MSCs) for the treatment of refractory diseases has achieved remarkable results. However, there is a need for a systematic evaluation of the quality and safety of MSCs sourced from donors. In this study, we sought to assess one potential factor that might impact quality, namely the age of the donor.

METHODS

We downloaded two data sets from each of two Gene Expression Omnibus (GEO), GSE39035 and GSE97311 databases, namely samples form young (< 65 years of age) and old (> 65) donor groups. Through, bioinformatics analysis and experimental validation to these retrieved data, we found that MSCs derived from aged donors can lead to differential expression of gene profiles compared with those from young donors, and potentially affect the function of MSCs, and may even induce malignant tumors.

RESULTS

We identified a total of 337 differentially expressed genes (DEGs), including two upregulated and eight downregulated genes from the databases of both GSE39035 and GSE97311. We further identified 13 hub genes. Six of them, TBX15, IGF1, GATA2, PITX2, SNAI1 and VCAN, were highly expressed in many human malignancies in Human Protein Atlas database. In the MSCs in vitro senescent cell model, qPCR analysis validated that all six hub genes were highly expressed in senescent MSCs. Our findings confirm that aged donors of MSCs have a significant effect on gene expression profiles. The MSCs from old donors have the potential to cause a variety of malignancies. These TBX15, IGF1, GATA2, PITX2, SNAI1, VCAN genes could be used as potential biomarkers to diagnosis aging state of donor MSCs, and evaluate whether MSCs derived from an aged donor could be used for therapy in the clinic. Our findings provide a diagnostic basis for the clinical use of MSCs to treat a variety of diseases.

CONCLUSIONS

Therefore, our findings not only provide guidance for the safe and standardized use of MSCs in the clinic for the treatment of various diseases, but also provide insights into the use of cell regeneration approaches to reverse aging and support rejuvenation.

Thermophoretic glycan profiling of extracellular vesicles for triple-negative breast cancer management

Triple-negative breast cancer (TNBC) is a highly metastatic and heterogeneous type of breast cancer with poor outcomes. Precise, non-invasive methods for diagnosis, monitoring and prognosis of TNBC are particularly challenging due to a paucity of TNBC biomarkers. Glycans on extracellular vesicles (EVs) hold the promise as valuable biomarkers, but conventional methods for glycan analysis are not feasible in clinical practice. Here, we report that a lectin-based thermophoretic assay (EVLET) streamlines vibrating membrane filtration (VMF) and thermophoretic amplification, allowing for rapid, sensitive, selective and cost-effective EV glycan profiling in TNBC plasma. A pilot cohort study shows that the EV glycan signature reaches 91% accuracy for TNBC detection and 96% accuracy for longitudinal monitoring of TNBC therapeutic response. Moreover, we demonstrate the potential of EV glycan signature for predicting TNBC progression. Our EVLET system lays the foundation for non-invasive cancer management by EV glycans.

Proteomic analysis of ascitic extracellular vesicles describes tumour microenvironment and predicts patient survival in ovarian cancer

High-grade serous carcinoma of the ovary, fallopian tube and peritoneum (HGSC), the most common type of ovarian cancer, ranks among the deadliest malignancies. Many HGSC patients have excess fluid in the peritoneum called ascites. Ascites is a tumour microenvironment (TME) containing various cells, proteins and extracellular vesicles (EVs). We isolated EVs from patients' ascites by orthogonal methods and analyzed them by mass spectrometry. We identified not only a set of 'core ascitic EV-associated proteins' but also defined their subset unique to HGSC ascites. Using single-cell RNA sequencing data, we mapped the origin of HGSC-specific EVs to different types of cells present in ascites. Surprisingly, EVs did not come predominantly from tumour cells but from non-malignant cell types such as macrophages and fibroblasts. Flow cytometry of ascitic cells in combination with analysis of EV protein composition in matched samples showed that analysis of cell type-specific EV markers in HGSC has more substantial prognostic potential than analysis of ascitic cells. To conclude, we provide evidence that proteomic analysis of EVs can define the cellular composition of HGSC TME. This finding opens numerous avenues both for a better understanding of EV's role in tumour promotion/prevention and for improved HGSC diagnostics.

An immune-related exosome signature predicts the prognosis and immunotherapy response in ovarian cancer

BACKGROUND

Cancer-derived exosomes contribute significantly in intracellular communication, particularly during tumorigenesis. Here, we aimed to identify two immune-related ovarian cancer-derived exosomes (IOCEs) subgroups in ovarian cancer (OC) and establish a prognostic model for OC patients based on immune-related IOCEs.

METHODS

The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq data, as well as clinical and prognostic information. Consensus clustering analysis was performed to identify two IOCEs-associated subgroups. Kaplan-Meier analysis was used to compare the overall survival (OS) between IOCEs-high and IOCEs-low subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the mechanisms and biological effects of differentially expressed genes (DEGs) between the two subtypes. Besides, an IOCE-related prognostic model of OC was constructed by Lasso regression analysis, and the signature was validated using GSE140082 as the validation set.

RESULTS

In total, we obtained 21 differentially expressed IOCEs in OC, and identified two IOCE-associated subgroups by consensus clustering. IOCE-low subgroup showed a favorable prognosis while IOCE-high subgroup had a higher level of immune cell infiltration and immune response. GSEA showed that pathways in cancer and immune response were mainly enriched in IOCE-high subgroup. Thus, IOCE-high subgroup may benefit more in immunotherapy treatment. In addition, we constructed a risk model based on nine IOCE-associated genes (CLDN4, AKT2, CSPG5, ALDOC, LTA4H, PSMA2, PSMA5, TCIRG1, ANO6).

CONCLUSION

We developed a novel stratification system for OV based on IOCE signature, which could be used to estimate the prognosis as well as immunotherapy for OC patient.