Evidence map›Paper›PMID 39007500›Full record

ArticleJournal of proteome research2024

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry.

Yuming Jiang, Jesse G Meyer

Abstract 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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Yuming JiangDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States.
Jesse G MeyerDepartment of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90048, United States.ORCID 0000-0003-2753-3926

Funding

Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic PathwaysR35GM142502 · NIGMS · MEDICAL COLLEGE OF WISCONSIN · PI MEYER, JESSE · 2021 to 2025
$2.2M
NIGMS NIH HHS R35 GM142502
6 · The paper itself

Abstract

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Indexed as

Blood ProteinsNanoparticlesProtein CoronaProteomeProteomicsBiomarkersChromatography, LiquidHumansMass SpectrometryBiomarkersBlood ProteinsProtein CoronaProteomedirect infusionDISPAhigh throughpution mobilitynanoparticlespeptidesplasma proteomicsproteomics

Identifiers

PMID39007500
PMCPMC12478194

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.