Evidence map›Paper›PMID 37873874›Full record

ArticleProteomes2023

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow.

Liang Jin, Fei Wang, Xue Wang, Bohdan P Harvey, Yingtao Bi, Chenqi Hu, Baoliang Cui, Anhdao T Darcy, John W Maull, Ben R Phillips and 4 more

Open access · goldAbstract read
In one paragraph

Article in Proteomes, 2023. 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
0.4field-weighted citation impact, top 41% 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

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

14 authors at 2 institutions in 1 country.

Liang JinResearch & Development, AbbVie, North Chicago, IL 60064, USA.ORCID 0000-0002-8659-0536
Fei WangResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Xue WangResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Bohdan P HarveyResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Yingtao BiResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Chenqi HuDMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA.
Baoliang CuiResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Anhdao T DarcyResearch & Development, AbbVie, North Chicago, IL 60064, USA.
John W MaullResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Ben R PhillipsResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Youngjae KimDMPK, Takeda Development Center Americas Inc., Cambridge, MA 02142, USA.
Gary J JenkinsResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Thierry R SornasseResearch & Development, AbbVie, North Chicago, IL 60064, USA.
Yu TianResearch & Development, AbbVie, North Chicago, IL 60064, USA.ORCID 0000-0003-3754-5875
AbbVie (United States) · USTakeda (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Indexed as

biomarkerDIAplasmaprotein isoformproteomicsrheumatoid arthritisSWATH

Identifiers

PMID37873874
PMCPMC10594463
OpenAlexW4387670454

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.