Evidence map›Paper›PMID 38959888›Full record

ArticleCell reports methods2024

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

Weiwei Lin, Fatemeh Mousavi, Benjamin C Blum, Christian F Heckendorf, Matthew Lawton, Noah Lampl, Ryan Hekman, Hongbo Guo, Mark McComb, Andrew Emili

Abstract read
In one paragraph

Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Weiwei LinCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA; Department of Pharmacology, Physiology & Biophysics, Boston University School of Medicine, Boston, MA, USA. Electronic address: linww@bu.edu.
Fatemeh MousaviCenter for Network Systems Biology, Boston University, Boston, MA, USA.
Benjamin C BlumCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Christian F HeckendorfCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Matthew LawtonCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Noah LamplCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Ryan HekmanCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Hongbo GuoCenter for Network Systems Biology, Boston University, Boston, MA, USA.
Mark McCombCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA.
Andrew EmiliCenter for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA. Electronic address: emili@ohsu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Indexed as

MetabolomicsProteomicsTandem Mass SpectrometryAnimalsChromatography, LiquidHumansLiquid Chromatography-Mass SpectrometryNanotechnologyCP: biotechnologyCP: metabolismintegrated omicsmetabolomicsnLC-MSproteomicsSPME

Identifiers

PMID38959888
PMCPMC11294829

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LicenceCC BY-NC-ND
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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.