Comparison of Extracellular Vesicle Isolation Methods for miRNA Sequencing

Llorens-Revull, Meritxell; Martínez-González, Brenda; Quer, Josep; Esteban, Juan Ignacio; Núñez‐Moreno, Gonzalo; Mínguez, Pablo; Burgui, Idoia; Ramos-Ruíz, Ricardo; Soria, María Eugenia; Rico, Angie; Riveiro‐Barciela, Mar; Sauleda, Sílvia; Pirón, María; Corrales, Irene; Borràs, Francesc E.; Rodríguez‐Frías, Francisco; Rando-Segura, Ariadna; Ramírez-Serra, Clara; Camós, Sílvia; Domingo, Esteban; Bes, Marta; Perales, Celia; Costafreda, María Isabel

doi:10.3390/ijms241512183

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…In the present study, we evaluated the expression profile of miRNAs encapsulated in EVs because EVs are relatively stable in the bloodstream, whereas miRNAs have a short half-life in circulation [ 6 ]. However, discrepancies in EV-miRNA and EV proteomic patterns are frequent among studies, which may be partly due to differences in the sample type from which EVs are derived (i.e., plasma or serum) and methodological differences in EV isolation, miRNA profiling, and expression normalization [ 20 , 21 , 22 , 23 ]. It is important to develop reproducible methods for isolating EVs from biological samples with high yield and purity.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs

Bang,

Kim,

Kim

et al. 2024

IJMS

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Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs

Bang,

Kim,

Kim

et al. 2024

IJMS

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

Wang,

Zhou,

Kong

et al. 2024

Advanced Science

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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.

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Circulating extracellular vesicular microRNA signatures in early gestation show an association with subsequent clinical features of pre-eclampsia

Ghosh,

Thamotharan,

Fong

et al. 2024

Sci Rep

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In a prospective cohort of subjects who subsequently developed preeclampsia (PE, n = 14) versus remaining healthy (NORM, n = 12), early gestation circulating extracellular vesicles (EVs) containing a panel of microRNA signatures were characterized and their biological networks of targets deciphered. Multiple microRNAs of which some arose from the placenta (19MC and 14MC) demonstrated changes in association with advancing gestation, while others expressed were pathognomonic of the subsequent development of characteristic clinical features of PE which set in as a late-onset subtype. This panel of miRNAs demonstrated a predictability with an area under the curve of 0.96 using leave-one-out cross-validation training in a logistic regression model with elastic-net regularization and precautions against overfitting. In addition, this panel of miRNAs, some of which were previously detected in either placental tissue or as maternal cell-free non-coding transcripts, lent further validation to our EV studies and the observed association with PE. Further, the identified biological networks of targets of these detected miRNAs revealed biological functions related to vascular remodeling, cellular proliferation, growth, VEGF, EGF and the PIP3/Akt signaling pathways, all mediating key cellular functions. We conclude that we have demonstrated a proof-of-principle by detecting a panel of EV packaged miRNAs in the maternal circulation early in gestation with possibilities of biological function in the placenta and other maternal tissues, along with the probability of predicting the subsequent clinical appearance of PE, particularly the late-onset subtype.

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Comparison of Extracellular Vesicle Isolation Methods for miRNA Sequencing

Cited by 3 publications

References 48 publications

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs

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

Circulating extracellular vesicular microRNA signatures in early gestation show an association with subsequent clinical features of pre-eclampsia

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