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.

Human milk EV-miRNAs: a novel biomarker for air pollution exposure during pregnancy.

Exposure to ambient and near-roadway air pollution during pregnancy has been linked with several adverse health outcomes for pregnant women and their babies. Emerging research indicates that microRNA (miRNA) expression can be altered by exposure to air pollutants in a variety of tissues. Additionally, miRNAs from breast tissue and circulating miRNAs have previously been proposed as a biomarker for breast cancer diagnosis and prognosis. Therefore, this study sought to evaluate the associations between pregnancy exposures to ambient (PM10, PM2.5, NO2, O3) and near-roadway air pollution (total NOx, freeway NOx, non-freeway NOx) with breast milk extracellular vesicle miRNA (EV-miRNA), measured at 1-month postpartum, in a cohort of 108 Latina women living in Southern California. We found that PM10 exposure during pregnancy was positively associated with hsa-miR-200c-3p, hsa-miR-200b-3p, and hsa-let-7c-5p, and was negatively associated with hsa-miR-378d. We also found that pregnancy PM2.5 exposure was positively associated with hsa-miR-200c-3p and hsa-miR-200b-3p. First and second trimester exposure to PM10 and PM2.5 was associated with several EV-miRNAs with putative messenger RNA targets related to cancer. This study provides preliminary evidence that air pollution exposure during pregnancy is associated with human milk EV-miRNA expression.

Direct Detection of Extracellular Vesicle miRNAs Using a Single-Step RT-qPCR Assay.

Extracellular vesicles (EVs) are biological carriers, and EV-associated miRNAs (EV-miRNAs) are considered as a novel biomarker in multiple diseases. Currently, the column-based purification method is used to purify miRNAs from EVs. However, this method of purification is complex, time-consuming, and expensive. Therefore, a simple and cost-effective single-step quantitative reverse transcription-polymerase chain reaction (RT-qPCR) method is required to detect the expression of EV-miRNAs. This chapter describes a protocol for directly analyzing the EV-miRNAs expression from mouse bronchoalveolar lavage fluid (BALF) and serum without going for an RNA isolation and purification step from EVs. It is an efficient method in several terms such as cost-wise, time, low expertise, and accuracy in results. This method may be helpful in diagnostic blood tests used in medical centers or research laboratories.

The potential diagnostic value of extracellular vesicle miRNA for human non-small cell lung cancer: a systematic review and meta-analysis.

Background: This meta-analysis aimed to evaluate the diagnostic accuracy of extracellular vesicles (EV) miRNAs for non-small cell lung cancer (NSCLC).Methods: All eligible studies were searched in an online database. Stata 15.0, Meta-disc 14.0 and Review Manager 5.2 software packages were used to perform all statistical analysis.Results: The analysis included 16 articles and 70 studies. Pooled sensitivity (SEN) and specificity (SPE), positive predictive value and negative predictive value were 0.77 (95% CI: 0.72-0.80), 0.83 (95% CI: 0.78-0.86), 0.88 (95% CI: 0.86-0.90) and 0.63 (95% CI: 0.58-0.68), respectively. The overall diagnostic odds ratio (DOR) was 16 (95% CI: 11-21) and the area under the curve (AUC) was 0.86 (95% CI: 0.83-0.89). 3 EV miRNAs could identify metastatic NSCLC from healthy, and 10 distinguish early-stage NSCLC. The respective targets of EV miR-21, miR-210, and miR-1290 could activate PI3K/AKT-related pathway.Conclusion: EV miRNAs had high diagnostic accuracy (AUC = 0.86) for NSCLC, especially metastatic NSCLC (AUC = 0.90), and early-stage NSCLC (AUC = 0.88). Besides, multitudinous EV miRNAs combined showed higher diagnostic value than alone. EV miR-21, miR-210, and miR-1290 might be associated with PI3K/AKT-related pathway and the valuable diagnostic biomarkers for NSCLC.