Evidence map›Paper›PMID 30618622›Full record

ArticleFrontiers in molecular neuroscience2018

Sample Pooling and Inflammation Linked to the False Selection of Biomarkers for Neurodegenerative Diseases in Top-Down Proteomics: A Pilot Study.

Nicolas Molinari, Stéphane Roche, Katell Peoc'h, Laurent Tiers, Martial Séveno, Christophe Hirtz, Sylvain Lehmann

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.5field-weighted citation impact, top 20% 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

14 citing papers in PubMed, 20 citations in OpenAlex.

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  6. Analysis of human brain tissue derived from DBS surgery.Translational neurodegeneration · 2022
    Article
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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 at 6 institutions in 1 country.

Nicolas MolinariDepartment of Statistics, CHU de Montpellier, University of Montpellier, Montpellier, France.
Stéphane RocheINSERM, UMR 1251, Aix-Marseille Université, Marseille, France.
Katell Peoc'hAPHP, HUPNVS, Hôpital Beaujon, UFR de Médecine Xavier Bichat, Clichy and Université Paris Diderot, Paris, France.
Laurent TiersLaboratoire et Plateforme de Biochimie Protéomique Clinique, CHU de Montpellier, Montpellier, France.
Martial SévenoCNRS, INSERM, BioCampus Montpellier, University of Montpellier, Montpellier, France.
Christophe HirtzLaboratoire et Plateforme de Biochimie Protéomique Clinique, CHU de Montpellier, Montpellier, France.
Sylvain LehmannLaboratoire et Plateforme de Biochimie Protéomique Clinique, CHU de Montpellier, Montpellier, France.
Institute for Regenerative Medicine & Biotherapy · FRAssistance Publique – Hôpitaux de Paris · FRCentre de Génétique Médicale de MarseilleCentre Hospitalier Universitaire de Montpellier · FRInserm · FRUniversité de Montpellier · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteomic technologies have been recently adapted to the new field of clinical proteomics. The origin of errors and biases has been well-identified in the pre-analytical steps, leading to the measurement of clinical analytes. One possible source of inadequacy in clinical proteomics is linked to sample pooling. This practice is usually related to low sample availability, variability, experiment time/cost. In this study, we first asked whether sample pooling in top-down proteomics is suitable to obtain a relevant biological average. Our second objective was to identify inflammatory biomarkers of outlier samples in our population of Creutzfeldt-Jakob disease patients. Our results demonstrated that, in a proteomics study, sample pooling as well as the inflammation status was an important source of errors: missed detection of biomarkers and false identification of others. Pooled samples were not equivalent to the average of biological values. In addition, this procedure reduced the statistical value of the identified biomarkers due to a stabilization of their standard deviation and rendered outlier samples difficult to detect. We identified serum amyloid A as a candidate biomarker of outlier samples. The presence of this protein, which could be explained by inflammatory processes, induced major modifications in the sample profiles.

Indexed as

clinical proteomicsCRPneurodegenerative diseaseSAAsample poolingserumtop–down

Identifiers

PMID30618622
PMCPMC6305369
OpenAlexW2904631466

What OpenQuestion holds

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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.