Evidence map›Paper›PMID 33936570›Full record

ArticleJournal of extracellular vesicles2021

Quantitative proteomic analysis of extracellular vesicle subgroups isolated by an optimized method combining polymer-based precipitation and size exclusion chromatography.

Juan A Martínez-Greene, Karina Hernández-Ortega, Ricardo Quiroz-Baez, Osbaldo Resendis-Antonio, Israel Pichardo-Casas, David A Sinclair, Bogdan Budnik, Alfredo Hidalgo-Miranda, Eileen Uribe-Querol, María Del Pilar Ramos-Godínez and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of extracellular vesicles, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed
7.1field-weighted citation impact, top 2% of its field
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

70 citing papers in PubMed, 112 citations in OpenAlex.

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10 more citing papers are in PubMed but not listed here.

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

11 authors at 5 institutions in 2 countries.

Juan A Martínez-GreeneLaboratory of Cell Communication & Extracellular Vesicles Instituto Nacional de Medicina Genómica Mexico City Mexico.ORCID 0000-0003-0934-040X
Karina Hernández-OrtegaDepartamento de Biología Facultad de Química Universidad Nacional Autónoma de México Ciudad de México México.
Ricardo Quiroz-BaezDepartamento de Investigación Básica Instituto Nacional de Geriatría Mexico City Mexico.ORCID 0000-0001-6365-5977
Osbaldo Resendis-AntonioHuman Systems Biology Laboratory Instituto Nacional de Medicina Genómica Mexico City Mexico.ORCID 0000-0001-5220-541X
Israel Pichardo-CasasDepartment of Genetics Paul F. Glenn Labs for the Biology of Aging Harvard Medical School Boston Massachusetts USA.
David A SinclairDepartment of Genetics Paul F. Glenn Labs for the Biology of Aging Harvard Medical School Boston Massachusetts USA.ORCID 0000-0002-9936-436X
Bogdan BudnikMass Spectrometry and Proteomics Resource Laboratory Division of Science Harvard University Cambridge Massachusetts USA.
Alfredo Hidalgo-MirandaLaboratorio de Genómica del Cáncer Instituto Nacional de Medicina Genómica Mexico City Mexico.ORCID 0000-0003-2315-3977
Eileen Uribe-QuerolLaboratorio de Biología del Desarrollo División de Estudios de Posgrado e Investigación Facultad de Odontología Universidad Nacional Autónoma de México Mexico City Mexico.ORCID 0000-0002-3502-7831
María Del Pilar Ramos-GodínezElectron Microscopy Laboratory Instituto Nacional de Cancerología Mexico City Mexico.
Eduardo Martínez-MartínezLaboratory of Cell Communication & Extracellular Vesicles Instituto Nacional de Medicina Genómica Mexico City Mexico.ORCID 0000-0002-9020-559X
National Institute of Genomic Medicine · MXHarvard University · USInstituto Nacional de Cancerología · MXInstituto Nacional de Pediatria · MXUniversidad Nacional Autónoma de México · MX

Funding

Uncovering the Human SecretomeDP1AG058605 · NIA · HARVARD MEDICAL SCHOOL · PI SINCLAIR, DAVID A. · 2017 to 2021
$5.9M
NIA NIH HHS DP1 AG058605
6 · The paper itself

Abstract

The molecular characterization of extracellular vesicles (EVs) has revealed a great heterogeneity in their composition at a cellular and tissue level. Current isolation methods fail to efficiently separate EV subtypes for proteomic and functional analysis. The aim of this study was to develop a reproducible and scalable isolation workflow to increase the yield and purity of EV preparations. Through a combination of polymer-based precipitation and size exclusion chromatography (Pre-SEC), we analyzed two subsets of EVs based on their CD9, CD63 and CD81 content and elution time. EVs were characterized using transmission electron microscopy, nanoparticle tracking analysis, and Western blot assays. To evaluate differences in protein composition between the early- and late-eluting EV fractions, we performed a quantitative proteomic analysis of MDA-MB-468-derived EVs. We identified 286 exclusive proteins in early-eluting fractions and 148 proteins with a differential concentration between early- and late-eluting fractions. A density gradient analysis further revealed EV heterogeneity within each analyzed subgroup. Through a systems biology approach, we found significant interactions among proteins contained in the EVs which suggest the existence of functional clusters related to specific biological processes. The workflow presented here allows the study of EV subtypes within a single cell type and contributes to standardizing the EV isolation for functional studies.

Indexed as

AnimalsBlotting, WesternChromatography, GelExtracellular VesiclesHumansMicroscopy, Electron, TransmissionPolymersProteinsProteomicsPolymersProteinsbreast cancerexosomesextracellular vesiclesproteomicssize‐exclusion chromatographyultracentrifugation

Identifiers

PMID33936570
PMCPMC8077108
OpenAlexW3157316684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.