Evidence map›Paper›PMID 33210411›Full record

ArticleJournal of mass spectrometry : JMS2021

Multiple reaction monitoring profiling as an analytical strategy to investigate lipids in extracellular vesicles.

Madison E Edwards, Thomas De Luca, Christina R Ferreira, Kimberly S Collins, Michael T Eadon, Eric A Benson, Tiago J P Sobreira, Robert Graham Cooks

Open access · greenAbstract read
In one paragraph

Article in Journal of mass spectrometry : JMS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Suspect screening of exogenous compounds using multiple reaction screening (MRM) profiling in human urine samples.Journal of chromatography. B, Analytical technologies in the biomedical and life sciences · 2022
    Article
  7. Article
  8. Article
  9. Pulmonary iron oxide (FeHuman & experimental toxicology
    Article
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

8 authors at 2 institutions in 1 country.

Madison E EdwardsDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, USA.
Thomas De LucaDivision of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Christina R FerreiraDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, USA.ORCID https://orcid.org/0000-0003-3988-6454
Kimberly S CollinsDivision of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Michael T EadonDivision of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.ORCID https://orcid.org/0000-0003-3066-2876
Eric A BensonDivision of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA.
Tiago J P SobreiraBindley Bioscience Center, Purdue University, West Lafayette, Indiana, 47907, USA.
Robert Graham CooksDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, USA.ORCID https://orcid.org/0000-0002-9581-9603
Indiana University School of MedicinePurdue University West Lafayette · US

Funding

Transgenic Mouse Core Facility Shared Resource (TMCF-SR)P30CA023168 · NCI · PURDUE UNIVERSITY WEST LAFAYETTE · PI ANDREW D MESECAR · 1985 to 2026
$43.4M
Acute inhibition of renal gene expression to prevent nephrotoxicity.K08DK107864 · NIDDK · INDIANA UNIVERSITY INDIANAPOLIS · PI EADON, MICHAEL THOMAS · 2016 to 2020
$772k
Exosome kinetics in vivo: cell type and drug specific effectsK08GM119006 · NIGMS · INDIANA UNIVERSITY INDIANAPOLIS · PI BENSON, ERIC · 2016 to 2020
$724k
NCI NIH HHS P30 CA023168NIDDK NIH HHS K08 DK107864NIGMS NIH HHS K08 GM119006NIH HHS K08-DK107864NIH HHS K08GM119006NIH HHS P30 CA023168
6 · The paper itself

Abstract

Extracellular vesicles (EVs) convey information used in cell-to-cell interactions. Lipid analysis of EVs remains challenging because of small sample amounts available. Lipid discovery using traditional mass spectrometry platforms based on liquid chromatography and high mass resolution typically employs milligram sample amounts. We report a simple workflow for lipid profiling of EVs based on multiple reaction monitoring (MRM) profiling that uses microgram amounts of sample. After liquid-liquid extraction, individual EV samples were injected directly into the electrospray ionization (ESI) ion source at low flow rates (10 μl/min) and screened for 197 MRM transitions chosen to be a characteristic of several classes of lipids. This choice was based on a discovery experiment, which applied 1,419 MRMs associated with multiple lipid classes to a representative pooled sample. EVs isolated from 12 samples of human lymphocytes and 16 replicates from six different rat cells lines contained an estimated amount of total lipids of 326 to 805 μg. Samples showed profiles that included phosphatidylcholine (PC), sphingomyelin (SM), cholesteryl ester (CE), and ceramide (Cer) lipids, as well as acylcarnitines. The lipid profiles of human lymphocyte EVs were distinguishable using principal component and cluster analysis in terms of prior antibody and drug exposure. Lipid profiles of rat cell lines EV's were distinguishable by their tissue of origin.

Indexed as

AnimalsCell LineCell Line, TumorExtracellular VesiclesFemaleHumansLipidsLiquid-Liquid ExtractionLymphocytesPrincipal Component AnalysisRatsSpectrometry, Mass, Electrospray IonizationLipidsdirect injectionexploratory lipidomicsextracellular vesicleslipid profilingMRM profiling

Identifiers

PMID33210411
PMCPMC7941191
OpenAlexW3103795318

What OpenQuestion holds

Textmetadata
LicenceTDM
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.