Assessing Discrepancies in miRNA Profiles of Mouse Blood‐Derived Extracellular Vesicles: A Systematic Review of Sequencing Data

We systematically reviewed published studies to assess reproducibility in miRNA expression profiles from extracellular vesicles (EVs) isolated from mouse serum. Our search specifically targeted mouse studies employing precipitation methods for EV isolation from blood and small RNA sequencing of EV-miRNAs in control groups. Out of 53 identified studies, approximately half lacked publicly available raw data, leaving four eligible studies containing sequencing data from a total of 11 mice. miRNA expression counts were standardized using z-scores for comparability. Within individual studies, miRNA profiles showed reasonable consistency; however, significant variability was observed across different studies. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Spearman correlation consistently demonstrated study-specific clustering rather than biological similarity. Methodological discrepancies in EV isolation, RNA extraction protocols, and unreported confounders such as platelet contamination or blood-handling procedures likely contributed to this variability. Our findings emphasize substantial reproducibility challenges in EV-miRNA research across murine studies, highlighting an urgent need for standardized methodologies and transparent reporting to improve reliability of miRNA biomarker discovery and facilitate meta-analytic integration in preclinical research.

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PROTEOMICS
Published
2025-12-30
DOI
https://doi.org/10.1002/pmic.70102
Primary Topic
Extracellular vesicles in disease
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article
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article

Assessing Discrepancies in miRNA Profiles of Mouse Blood‐Derived Extracellular Vesicles: A Systematic Review of Sequencing Data

Eisuke Dohi, K. Mogushi, Erika Hayakawa, Md Sorwer Alam Parvez et al.
PROTEOMICS
Extracellular vesicles in disease
article

Assessing Discrepancies in miRNA Profiles of Mouse Blood‐Derived Extracellular Vesicles: A Systematic Review of Sequencing Data

Eisuke Dohi, K. Mogushi, Erika Hayakawa, Md Sorwer Alam Parvez, Katsuyuki Takeuchi, Yoritsugu Iida, Takeshi Hase
article en

Abstract

We systematically reviewed published studies to assess reproducibility in miRNA expression profiles from extracellular vesicles (EVs) isolated from mouse serum. Our search specifically targeted mouse studies employing precipitation methods for EV isolation from blood and small RNA sequencing of EV-miRNAs in control groups. Out of 53 identified studies, approximately half lacked publicly available raw data, leaving four eligible studies containing sequencing data from a total of 11 mice. miRNA expression counts were standardized using z-scores for comparability. Within individual studies, miRNA profiles showed reasonable consistency; however, significant variability was observed across different studies. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Spearman correlation consistently demonstrated study-specific clustering rather than biological similarity. Methodological discrepancies in EV isolation, RNA extraction protocols, and unreported confounders such as platelet contamination or blood-handling procedures likely contributed to this variability. Our findings emphasize substantial reproducibility challenges in EV-miRNA research across murine studies, highlighting an urgent need for standardized methodologies and transparent reporting to improve reliability of miRNA biomarker discovery and facilitate meta-analytic integration in preclinical research.

PROTEOMICSVol. 26(2-3)
Hyogo Medical University (JP), Tokyo Health Care University (JP), University of Alabama at Birmingham (US), National Center of Neurology and Psychiatry (JP), Systems Biology Institute (JP), Hyogo University (JP)
Openalex Percentile: Top 35%
Extracellular vesicles in disease
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Assessing Discrepancies in miRNA Profiles of Mouse Blood‐Derived Extracellular Vesicles: A Systematic Review of Sequencing Data — Eisuke Dohi, K. Mogushi, et al. · PROTEOMICS (2025) | TGRS Research Map | TGRS