Evidence map›Paper›PMID 42838962›Full record

ArticleNature communications2026

Correlating metabolites, lipids, and N-glycans in murine kidney microstructures via integrative mass spectrometry imaging.

Nina Ogrinc, Rick Ursem, Hans Dalebout, Jesper Kers, Martin Giera, Manfred Wuhrer, Noortje de Haan

Abstract read
In one paragraph

Article in Nature communications, 2026. 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

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

7 authors.

Nina Ogrinc *Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands. n.ogrinc@lumc.nl.ORCID 0000-0002-0773-0095
Rick Ursem *Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Hans DaleboutCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Jesper KersLeiden Transplant Center, Leiden University Medical Center, Leiden, The Netherlands.
Martin GieraCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.ORCID 0000-0003-1684-1894
Manfred WuhrerCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.ORCID 0000-0002-0814-4995
Noortje de HaanCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands. n.de_Haan@lumc.nl.ORCID 0000-0001-7026-6750

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101136552
6 · The paper itself

Abstract

Understanding how metabolites, lipids and glycans interact within tissues at the same cellular layer is key to uncovering their roles in health and disease. Herein, we present an integrative multi-omic mass spectrometry imaging (MSI) approach correlating metabolites, lipids and N-glycans, including their linkage-specific sialic acid information, in a single renal tissue section. Our protocol enables the sequential detection of hundreds of metabolites and lipids species alongside diverse glycosylation traits in small cell clusters across the kidney. Importantly, our pixel-to-pixel co-registration and clustering correlates glycans (recorded in positive mode MS), metabolites, and lipids (both in negative mode MS) across kidney microstructures. This analysis reveals strong association between taurine, TCA metabolites, sulfatides, and polyunsaturated phospholipids, and specific sialic acid linkages on N-glycans in the thick ascending limb and collecting ducts. The robust strategy maximizes molecular insights from scarcely available samples and offers a powerful tool for exploring tissue complexity in both normal and disease states.

Indexed as

KidneyLipidsPolysaccharidesAnimalsGlycosylationLipid MetabolismMass SpectrometryMetabolomicsMiceMultiomicsLipidsPolysaccharides

Identifiers

PMID42838962
PMCPMC13642259

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Registered trials

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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.