Evidence map›Paper›PMID 35219905›Full record

ArticleMolecular & cellular proteomics : MCP2022

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State-Dependent Redox-Sensitive Cysteines.

Heta S Desai, Tianyang Yan, Fengchao Yu, Alexander W Sun, Miranda Villanueva, Alexey I Nesvizhskii, Keriann M Backus

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
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  6. Review
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  9. diaTracer enables spectrum-centric analysis of diaPASEF proteomics data.bioRxiv : the preprint server for biology · 2024
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  10. Article
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  19. Multi-omic stratification of the missense variant cysteinome.bioRxiv : the preprint server for biology · 2023
    Article
  20. 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

7 authors.

Heta S DesaiBiological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California, USA; Molecular Biology Institute, UCLA, Los Angeles, California, USA.
Tianyang YanBiological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California, USA; Department of Chemistry and Biochemistry, UCLA, Los Angeles, California, USA.
Fengchao YuDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
Alexander W SunBiological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California, USA.
Miranda VillanuevaBiological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California, USA; Molecular Biology Institute, UCLA, Los Angeles, California, USA.
Alexey I NesvizhskiiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
Keriann M BackusBiological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California, USA; Department of Chemistry and Biochemistry, UCLA, Los Angeles, California, USA; Molecular Biology Institute, UCLA, Los Angeles, California, USA; DOE Institute for Genomics and Proteomics, UCLA, Los Angeles, California, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, California, USA. Electronic address: kbackus@mednet.ucla.edu.

Funding

Virology CoreP30AI028697 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ZACK, JEROME A. · 1991 to 2018
$39.9M
COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATAR01GM094231 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 2010 to 2026
$5.4M
University of Michigan Proteogenomics Data Analysis CenterU24CA210967 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M, DHANASEKARAN, SARAVANA M · 2016 to 2020
$4.1M
NCI NIH HHS U24 CA210967NIAID NIH HHS P30 AI028697NIGMS NIH HHS R01 GM094231
6 · The paper itself

Abstract

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.

Indexed as

CysteineProteomicsMass SpectrometryOxidation-ReductionProteomeCysteineProteome

Identifiers

PMID35219905
PMCPMC9010637

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.