Evidence map›Paper›PMID 36413327›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Tandem Mass Tag-Based Phosphoproteomics in Plants.

Isabel Cristina Vélez-Bermúdez, Dharmesh Jain, Arya Ravindran, Chin-Wen Chen, Chuan-Chih Hsu, Wolfgang Schmidt

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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
2.5field-weighted citation impact, top 11% 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, 4 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

6 authors at 2 institutions in 1 country.

Isabel Cristina Vélez-BermúdezInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Dharmesh JainInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Arya RavindranInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Chin-Wen ChenInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Chuan-Chih HsuInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
Wolfgang SchmidtInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan. wosh@sinica.edu.tw.
Institute of Plant and Microbial Biology, Academia Sinica · TWAcademia Sinica · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry-based proteomics provide a powerful tool for plant research, allowing global detection of steady-state levels of proteins under a given experimental setup. Here, we provide an optimized protocol for proteomic profiling using tandem mass tag (TMT) labeling followed by liquid chromatography-mass spectrometry (LC-MS/MS) to quantitate phosphopeptides and non-phosphopeptides from the same samples. The outlined protocol comprises a series of successive steps, namely, SDS (sodium dodecyl sulfate) protein extraction, protein precipitation, digestion, TMT labeling, phosphopeptide enrichment, high pH reversed-phase fractionation, LC-MS/MS analysis, protein identification, and data analysis. Our proteome-scale protocol requires 0.1 mg protein per sample and allows for the reliable and accurate quantification of more than 8000 proteins in Arabidopsis plant samples across multiple conditions, including low abundant peptides.

Indexed as

ProteomicsTandem Mass SpectrometryChromatography, LiquidPhosphopeptidesProteomePhosphopeptidesProteomeMass spectrometryPhosphopeptide enrichmentProtein quantificationProteome analysisTandem mass tag

Identifiers

PMID36413327
OpenAlexW4309668700

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.