Evidence map›Paper›PMID 39974146›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Molecular profiling of neuronal extracellular vesicles reveals brain tissue specific signals.

Vrinda Kalia, Gabriela Jackson, Regina J Dominguez, Brismar Pinto-Pacheco, Tessa Bloomquist, Julia Furnari, Matei Banu, Olga Volpert, Katherine E Manz, Douglas I Walker and 6 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Vrinda KaliaDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.ORCID 0000-0003-2244-6663
Gabriela JacksonDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Regina J DominguezDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Brismar Pinto-PachecoDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Tessa BloomquistDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Julia FurnariDepartment of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA.
Matei BanuStanford Neuroscience Health Center, Stanford Medicine, Palo Alto, CA, USA.
Olga VolpertNeuroDex Inc., Natick, MA 01760, USA.
Katherine E ManzDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Douglas I WalkerGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Kurt D PennellSchool of Engineering, Brown University, Providence, RI, USA.
Peter D CanollDepartment of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
Jeffrey N BruceDepartment of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA.
Erez EitanNeuroDex Inc., Natick, MA 01760, USA.
Haotian WuDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Andrea A BaccarelliDepartment of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.

Funding

Extracellular vesicles in Environmental Epidemiology Studies of AgingR35ES031688 · NIEHS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Andrea Baccarelli · 2021 to 2026
$4.6M
Network biology-based markers of extreme weather-induced neurodegenerationP20AG093975 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BERHANE, KIROS T, KIOUMOURTZOGLOU, MARIANTHI-ANNA · 2024 to 2025
$2.8M
NIA NIH HHS P20 AG093975NIEHS NIH HHS R35 ES031688
6 · The paper itself

Abstract

Extracellular vesicles (EVs) released by neurons (nEVs) provide an opportunity to measure biomarkers from the brain circulating in the periphery. No study yet has directly compared molecular cargo in brain tissue to nEVs found in circulation in humans. We compared the levels microRNAs and environmental chemicals because microRNAs are one of the most studied nEV cargoes and offer great potential as biomarkers and environmental chemical load in nEVs is understudied and could reveal levels of chemicals in the brain. To do so, we leveraged matched sets of brain tissue and serum, and isolated serum total EVs and serum nEVs. We also generated and compared metabolomic profiles in a different set of matched serum, serum total EVs, and serum nEVs since metabolite cargo in nEVs is also understudied but could offer potential biomarkers. Highly expressed brain tissue miRNAs showed stronger correlations with nEVs than serum or total EVs. We detected several environmental chemical pollutant classes in nEVs. The chemical pollutant concentrations in nEVs were more strongly correlated with brain tissue levels than those observed between brain tissue and serum or total EVs. We also detected several endogenous metabolite classes in nEVs. Compared to serum and total EVs, there was enrichment of metabolites with known signaling roles, such as bile acids, oleic acid, phosphatidylserine, and isoprenoids. We provide evidence that nEV cargo is closely correlated to brain tissue content, further supporting their utility as a brain liquid biopsy.

Indexed as

environmental chemicalsExtracellular vesiclesmetabolomicsmicroRNAsneuronal extracellular vesicles

Identifiers

PMID39974146
PMCPMC11839008

What OpenQuestion holds

Textmetadata
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.