Evidence map›Paper›PMID 23222455›Full record

ArticleNature protocols2013

Global metabolic profiling of animal and human tissues via UPLC-MS.

Elizabeth J Want, Perrine Masson, Filippos Michopoulos, Ian D Wilson, Georgios Theodoridis, Robert S Plumb, John Shockcor, Neil Loftus, Elaine Holmes, Jeremy K Nicholson

Abstract read
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In one paragraph

Article in Nature protocols, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 707 papers.

0numbers the graph read from it
0cells of the map it votes in
707citing 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

707 citing papers in PubMed.

  1. Science advances · 2026
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  5. A targeted lipidomics workflow for high-throughput phenotyping and mechanism-based in vitro hepatotoxicity studies.Toxicology in vitro : an international journal published in association with BIBRA · 2026
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  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Molecular Insights intoPlants (Basel, Switzerland) · 2026
    Article

647 more citing papers are in PubMed but not listed here.

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.

Elizabeth J WantBiomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.
Perrine Masson
Filippos Michopoulos
Ian D Wilson
Georgios Theodoridis
Robert S Plumb
John Shockcor
Neil Loftus
Elaine Holmes
Jeremy K Nicholson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Indexed as

MetabolomeAnimalsChromatography, LiquidHumansLiverMass SpectrometryMultivariate AnalysisTandem Mass Spectrometry

Identifiers

PMID23222455

What OpenQuestion holds

Textmetadata
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.