Evidence map›Paper›PMID 26280412›Full record

ArticleNature biotechnology2015

High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics.

Sean J Humphrey, S Babak Azimifar, Matthias Mann

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 papers.

0numbers the graph read from it
0cells of the map it votes in
224citing papers in PubMed
29.7field-weighted citation impact, top 1% 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

224 citing papers in PubMed, 481 citations in OpenAlex.

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  12. Cell cycle arrest enhances CD8Nature immunology · 2026
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  19. The insulin signalling network.Nature metabolism · 2025
    Review
  20. Article

164 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

3 authors at 1 institution in 1 country.

Sean J HumphreyDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
S Babak AzimifarDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Matthias MannDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Max Planck Institute of Biochemistry · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry has enabled the study of cellular signaling on a systems-wide scale, through the quantification of post-translational modifications, such as protein phosphorylation. Here we describe EasyPhos, a scalable phosphoproteomics platform that now allows rapid quantification of hundreds of phosphoproteomes in diverse cells and tissues at a depth of >10,000 sites. We apply this technology to generate time-resolved maps of insulin signaling in the mouse liver. Our results reveal that insulin affects ~10% of the liver phosphoproteome and that many known functional phosphorylation sites, and an even larger number of unknown sites, are modified at very early time points (<15 s after insulin delivery). Our kinetic data suggest that the flow of signaling information from the cell surface to the nucleus can occur on very rapid timescales of less than 1 min in vivo. EasyPhos facilitates high-throughput phosphoproteomics studies, which should improve our understanding of dynamic cell signaling networks and how they are regulated and dysregulated in disease.

Indexed as

AnimalsCells, CulturedGene Expression ProfilingHigh-Throughput Screening AssaysInsulinLiverMaleMass SpectrometryMiceMice, Inbred C57BLPhosphoproteinsProtein Interaction MappingProteomeSignal TransductionInsulinPhosphoproteinsProteome

Identifiers

PMID26280412
OpenAlexW2121103946

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.