Evidence map›Paper›PMID 36465531›Full record

ArticleJACS Au2022

Decoding Distinct Ganglioside Patterns of Native and Differentiated Mesenchymal Stem Cells by a Novel Glycolipidomics Profiling Strategy.

Katharina Hohenwallner, Nina Troppmair, Lisa Panzenboeck, Cornelia Kasper, Yasin El Abiead, Gunda Koellensperger, Leonida M Lamp, Jürgen Hartler, Dominik Egger, Evelyn Rampler

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Article in JACS Au, 2022. 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
1.5field-weighted citation impact, top 18% 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, 18 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Katharina HohenwallnerDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.ORCID https://orcid.org/0000-0002-4957-1344
Nina TroppmairDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.
Lisa PanzenboeckDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.
Cornelia KasperInstitute of Cell and Tissue Culture Technologies, University of Natural Resources and Life Sciences, Vienna 1190, Austria.
Yasin El AbieadDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.ORCID https://orcid.org/0000-0003-4392-7706
Gunda KoellenspergerDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.ORCID https://orcid.org/0000-0002-1460-4919
Leonida M LampInstitute of Pharmaceutical Sciences, University of Graz, Graz 8010, Austria.
Jürgen HartlerInstitute of Pharmaceutical Sciences, University of Graz, Graz 8010, Austria.ORCID https://orcid.org/0000-0002-1095-6458
Dominik EggerInstitute of Cell and Tissue Culture Technologies, University of Natural Resources and Life Sciences, Vienna 1190, Austria.
Evelyn RamplerDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.ORCID https://orcid.org/0000-0002-9429-7663
University of Vienna · ATBOKU University · ATUniversity of Graz · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gangliosides are an indispensable glycolipid class concentrated on cell surfaces with a critical role in stem cell differentiation. Nonetheless, owing to the lack of suitable methods for scalable analysis covering the full scope of ganglioside molecular diversity, their mechanistic properties in signaling and differentiation remain undiscovered to a large extent. This work introduces a sensitive and comprehensive ganglioside assay based on liquid chromatography, high-resolution mass spectrometry, and multistage fragmentation. Complemented by an open-source data evaluation workflow, we provide automated in-depth lipid species-level and molecular species-level annotation based on decision rule sets for all major ganglioside classes. Compared to conventional state-of-the-art methods, the presented ganglioside assay offers (1) increased sensitivity, (2) superior structural elucidation, and (3) the possibility to detect novel ganglioside species. A major reason for the highly improved sensitivity is the optimized spectral readout based on the unique capability of two parallelizable mass analyzers for multistage fragmentation. We demonstrated the high-throughput universal capability of our novel analytical strategy by identifying 254 ganglioside species. As a proof of concept, 137 unique gangliosides were annotated in native and differentiated human mesenchymal stem cells including 78 potential cell-state-specific markers and 38 previously unreported gangliosides. A general increase of the ganglioside numbers upon differentiation was observed as well as cell-state-specific clustering based on the ganglioside species patterns. The combination of the developed glycolipidomics assay with the extended automated annotation tool enables comprehensive in-depth ganglioside characterization as shown on biological samples of interest. Our results suggest ganglioside patterns as a promising quality control tool for stem cells and their differentiation products. Additionally, we believe that our analytical workflow paves the way for probing glycolipid-based biochemical processes shedding light on the enigmatic processes of gangliosides and glycolipids in general.

Identifiers

PMID36465531
PMCPMC9709940
OpenAlexW4307391040

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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.