Evidence map›Paper›PMID 38594897›Full record

ArticleJournal of proteome research2024

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

Steven R Shuken, Qing Yu, Steven P Gygi

Open access · greenAbstract read
In one paragraph

Article in Journal of proteome research, 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
0.7field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Steven R ShukenDepartment of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States.ORCID 0000-0002-2782-2165
Qing YuDepartment of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States.ORCID 0000-0003-0468-5353
Steven P GygiDepartment of Cell Biology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States.ORCID 0000-0001-7626-0034
Harvard University · US

Funding

New Sample Multiplexing Technologies to Identify Chemical Probes and Illuminate Ubiquitin BiologyR01GM067945 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI GYGI, STEVEN P · 2003 to 2024
$10.5M
NIGMS NIH HHS R01 GM067945
6 · The paper itself

Abstract

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Indexed as

ProteomicsSoftwareTandem Mass SpectrometryAutophagyChromatography, LiquidHumansPeptidesPeptideschromatographyinstrument APIintelligent data acquisitionisobaric labelingmacroautophagypathway proteomicsquantitative mass spectrometryreal-time searchtandem mass tagstargeted proteomics

Identifiers

PMID38594897
PMCPMC11068481
OpenAlexW4394690681

What OpenQuestion holds

Textmetadata
LicenceTDM
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.