Evidence map›Paper›PMID 41251160›Full record

ArticleNucleic acids research2026

SVAtlas: a comprehensive single extracellular vesicle omics resource.

Zhonghui Wei, Na Zhou, Ming Jing, Yanling Cai, Yingxin Zhang, Xinyu Wang, Linxinyu Wang, Di Wu, Fuzhong Xue, Qingzhen Hou

Abstract read
In one paragraph

Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhonghui WeiDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.
Na ZhouDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.
Ming JingDepartment of Software Engineering, School of Data and Computer Science, Shandong Women's University, Jinan 250300, China.
Yanling CaiShanghai Secretech Co., Ltd., Shanghai 201112, China.
Yingxin ZhangDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.
Xinyu WangDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.
Linxinyu WangDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.
Di WuShanghai Secretech Co., Ltd., Shanghai 201112, China.
Fuzhong XueDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.ORCID 0000-0003-0378-7956
Qingzhen HouDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250100, China.ORCID 0000-0002-7655-0899

Funding

National Natural Science Foundation Key Program 82330108National Natural Science Foundation of China 82473733
6 · The paper itself

Abstract

Extracellular vesicles (EVs) are nanoscale particles released by cells, carrying proteins, nucleic acids, lipids, and metabolites. These vesicles mediate intercellular communication and modulate disease progression across various conditions. Owing to their molecular heterogeneity and the stable bilayer protecting their cargo, EVs serve as valuable tools for early disease detection and personalized therapies. However, traditional bulk EV studies aggregate data, which can mask the distinct molecular profiles of individual EVs critical for targeted diagnostics and treatments, diminishing diagnostic precision. Recent advances in single-EV analysis, leveraging high-resolution sequencing and imaging technologies, have revealed unique molecular signatures. However, a comprehensive database integrating multi-omics data from single EVs remains lacking. To address this, we developed SVAtlas, the first database dedicated to integrating single-EV datasets (2015-2025). SVAtlas incorporates 8120 protein entries, 106 RNA entries (miRNA, mRNA, circRNA, and lncRNA), 2 DNA entries, and 8 lipid/metabolite entries across 276 EV projects, spanning 31 diseases, 32 tissues/organs, and 10 biofluids from five species. SVAtlas offers single-EV datasets with experimental parameters, heterogeneity analyses, disease-specific marker exploration, built-in analysis/clustering/visualization pipelines, and an LLM-based question-answering tool, empowering researchers to explore single-EV omics in detail. Free and accessible at https://www.svatlas.org/, SVAtlas accelerates the clinical translation of single-EV analysis and biomarker discovery.

Indexed as

Extracellular VesiclesAnimalsHumansSoftware

Identifiers

PMID41251160
PMCPMC12807711

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.