Evidence map›Paper›PMID 41876845›Full record

ReviewPlanta2026

Future perspectives in mass spectrometry of plant lipids.

Katharina Gutbrod, Peter Dörmann

Abstract readReview
In one paragraph

Review in Planta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Katharina GutbrodInstitute of Molecular Physiology and Biotechnology of Plants (IMBIO), University of Bonn, Karlrobert-Kreiten-Strasse 13, 53115, Bonn, Germany.
Peter DörmannInstitute of Molecular Physiology and Biotechnology of Plants (IMBIO), University of Bonn, Karlrobert-Kreiten-Strasse 13, 53115, Bonn, Germany. doermann@uni-bonn.de.ORCID http://orcid.org/0000-0002-5845-9370

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MAIN

conclusionImportant topics of plant lipidomic research include the standardization of protocols for quantification, and the analysis of subcellular distribution of common and unusual lipids, both in Arabidopsis and non-model species. Plant lipid research has seen a tremendous progress in the last decades, particularly in the area of lipid analytics by mass spectrometry. This includes the characterization of the different lipid classes involved in the establishment of the membrane bilayer, in carbon storage, and signaling. Advances in mass spectrometry have transformed the landscape of plant lipid research, enabling large scale studies of complex lipids at the level of individual molecular species, with minimal efforts of sample preparation. Lipidomic technologies employ targeted approaches to analyze known lipid molecular species as well as non-targeted methods to identify lipids that accumulate differentially in specific sample sets. Lipid quantification requires the availability of appropriate standards and highly sensitive methods of mass spectrometry. Additional technologies have been developed to study the spatial distribution of lipids in plant tissues, as well as to identify and characterize unusual lipids in plant cells. Finally, the large amounts of data generated in plant lipid research require sophisticated databases that connect the 'omics' data with data on growth, development, and adaptive responses to stress conditions at the tissue, cellular, and subcellular levels.

Indexed as

LipidomicsLipidsMass SpectrometryPlantsLipid MetabolismLipidsBiosynthesisLipidomicsMass spectrometryOrganelleSubcellular distributionTransport

Identifiers

PMID41876845
PMCPMC13013322

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.