Evidence map›Paper›PMID 32055910›Full record

ArticleAnalytical and bioanalytical chemistry2020

Lipid metabolism of leukocytes in the unstimulated and activated states.

Juan Carlos Alarcon-Barrera, Johannes H von Hegedus, Hilde Brouwers, Evelyne Steenvoorden, Andreea Ioan-Facsinay, Oleg A Mayboroda, Alejandro Ondo-Mendez, Martin Giera

Open access · hybridAbstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 39 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Metabolomics of airways disease in cystic fibrosis.Current opinion in pharmacology · 2022
    Review
  15. Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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 2 countries.

Juan Carlos Alarcon-BarreraCenter for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Johannes H von HegedusDepartment of Rheumatology, Leiden University Medical Center (LUMC), 2300 RC, Leiden, The Netherlands.
Hilde BrouwersDepartment of Rheumatology, Leiden University Medical Center (LUMC), 2300 RC, Leiden, The Netherlands.
Evelyne SteenvoordenCenter for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Andreea Ioan-FacsinayDepartment of Rheumatology, Leiden University Medical Center (LUMC), 2300 RC, Leiden, The Netherlands.
Oleg A MayborodaCenter for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Alejandro Ondo-MendezEscuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, 111221, Colombia.
Martin GieraCenter for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. M.A.Giera@lumc.nl.ORCID http://orcid.org/0000-0003-1684-1894
Leiden University Medical Center · NLUniversidad del Rosario · CO

Funding

Departamento Administrativo de Ciencia, Tecnología e Innovación Call 756/2016
6 · The paper itself

Abstract

Lipidomics has emerged as a powerful technique to study cellular lipid metabolism. As the lipidome contains numerous isomeric and isobaric species resulting in a significant overlap between different lipid classes, cutting-edge analytical technology is necessary for a comprehensive analysis of lipid metabolism. Just recently, differential mobility spectrometry (DMS) has evolved as such a technology, helping to overcome several analytical challenges. We here set out to apply DMS and the Lipidyzer™ platform to obtain a comprehensive overview of leukocyte-related lipid metabolism in the resting and activated states. First, we tested the linearity and repeatability of the platform by using HL60 cells. We obtained good linearities for most of the thirteen analyzed lipid classes (correlation coefficient > 0.95), and good repeatability (%CV < 15). By comparing the lipidome of neutrophils (PMNs), monocytes (CD14+), and lymphocytes (CD4+), we shed light on leukocyte-specific lipid patterns as well as lipidomic changes occurring through differential stimulation. For example, at the resting state, PMNs proved to contain higher amounts of triacylglycerides compared to CD4+ and CD14+ cells. On the other hand, CD4+ and CD14+ cells contained higher levels of phospholipids and ceramides. Upon stimulation, diacylglycerides, hexosylceramides, phosphatidylcholines, phosphoethanolamines, and lysophosphoethanolamines were upregulated in CD4+ cells and PMNs, whereas CD14+ cells did not show significant changes. By exploring the fatty acid content of the significantly upregulated lipid classes, we mainly found increased concentrations of very long and polyunsaturated fatty acids. Our results indicate the usefulness of the Lipidyzer™ platform for studying cellular lipid metabolism. Its application allowed us to explore the lipidome of leukocytes. Graphical abstract.

Indexed as

Cell Line, TumorFatty Acids, UnsaturatedHumansLeukocytesLipid MetabolismLipidomicsLipidsMass SpectrometryFatty Acids, UnsaturatedLipidsCD14+CD4+IPA extractionLipidomicsLipidyzer™Neutrophils

Identifiers

PMID32055910
PMCPMC7118052
OpenAlexW3006247487

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.