Evidence map›Paper›PMID 36005613›Full record

ArticleMetabolites2022

A Sample Preparation Method for the Simultaneous Profiling of Signaling Lipids and Polar Metabolites in Small Quantities of Muscle Tissues from a Mouse Model for Sarcopenia.

Yupeng He, Marlien van Mever, Wei Yang, Luojiao Huang, Rawi Ramautar, Yvonne Rijksen, Wilbert P Vermeij, Jan H J Hoeijmakers, Amy C Harms, Peter W Lindenburg and 1 more

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

11 authors at 3 institutions in 2 countries.

Yupeng HeMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Marlien van MeverMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Wei YangMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.ORCID 0000-0002-3394-7570
Luojiao HuangMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.ORCID 0000-0001-5825-5127
Rawi RamautarMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Yvonne RijksenPrincess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands.
Wilbert P VermeijPrincess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands.ORCID 0000-0002-9690-1385
Jan H J HoeijmakersPrincess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands.
Amy C HarmsMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.ORCID 0000-0002-2931-4295
Peter W LindenburgMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
Thomas HankemeierMetabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.ORCID 0000-0001-7871-2073
Leiden University · NLOncode Institute · NLUniversity of Cologne · DE

Funding

THE IMPACT OF CELLULAR DEFENSE ON THE ROLE OF Ku80 IN GENOME MAINTENANCE AND LONGP01AG017242 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR SAN ANT · PI VIJG, JAN · 1999 to 2023
$46.1M
China Scholarship Council 201706320322Dutch Research Council 737.016.015EJP-RD TC-NER RD20-113European Research Council Advanced Grant Dam2Age NIH grant (PO1 AG017242)Netherlands X-omics InitiativeNIA NIH HHS P01 AG017242NWO 184.034.019ONCODE Dutch Cancer Societythe Deutsche Forschungsgemeinschaft Project-ID 73111208 - SFB 829ZonMW Memorabel 733050810
6 · The paper itself

Abstract

The metabolic profiling of a wide range of chemical classes relevant to understanding sarcopenia under conditions in which sample availability is limited, e.g., from mouse models, small muscles, or muscle biopsies, is desired. Several existing metabolomics platforms that include diverse classes of signaling lipids, energy metabolites, and amino acids and amines would be informative for suspected biochemical pathways involved in sarcopenia. The sample limitation requires an optimized sample preparation method with minimal losses during isolation and handling and maximal accuracy and reproducibility. Here, two developed sample preparation methods, BuOH-MTBE-Water (BMW) and BuOH-MTBE-More-Water (BMMW), were evaluated and compared with previously reported methods, Bligh-Dyer (BD) and BuOH-MTBE-Citrate (BMC), for their suitability for these classes. The most optimal extraction was found to be the BMMW method, with the highest extraction recovery of 63% for the signaling lipids and 81% for polar metabolites, and an acceptable matrix effect (close to 1.0) for all metabolites of interest. The BMMW method was applied on muscle tissues as small as 5 mg (dry weight) from the well-characterized, prematurely aging, DNA repair-deficient

Indexed as

metabolomics extractionmuscle ageing and sarcopeniamuscle tissuepolar metabolitessignaling lipids

Identifiers

PMID36005613
PMCPMC9413361
OpenAlexW4291511976

What Socratic 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 Socratic graph.