Evidence map›Paper›PMID 38310095›Full record

ArticleNature communications2024

Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications.

Manuel Matzinger, Anna Schmücker, Ramesh Yelagandula, Karel Stejskal, Gabriela Krššáková, Frédéric Berger, Karl Mechtler, Rupert L Mayer

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

8 authors.

Manuel Matzinger *Research Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria. manuel.matzinger@imp.ac.at.ORCID 0000-0002-9765-7951
Anna SchmückerGregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.ORCID 0000-0001-8409-761X
Ramesh YelagandulaGregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.ORCID 0000-0001-7418-4322
Karel StejskalResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.
Gabriela KrššákováResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria.
Frédéric BergerGregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.ORCID 0000-0002-3609-8260
Karl MechtlerResearch Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria. karl.mechtler@imp.ac.at.ORCID 0000-0002-3392-9946
Rupert L Mayer *Research Institute of Molecular Pathology (IMP), Vienna BioCenter, Vienna, Austria. rupert.mayer@imp.ac.at.ORCID 0000-0001-7104-0147

Funding

Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung) P32054Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung) P33380Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung) P35045-BVienna Science and Technology Fund (Wiener Wissenschafts-, Forschungs- und Technologiefonds) LS20-079
6 · The paper itself

Abstract

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4-12 with the CHIMERYS search engine identified +51-74% and +59-150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs <7% for low input bulk samples, and accuracy, with deviations <10% from expected fold changes for regular abundance two-proteome mixes. Compared to a conventional workflow, our entire optimized platform discovered 92% more potential interactors in a protein-protein interaction study on the chromatin remodeler Smarca5/Snf2h. These include previously described Smarca5 binding partners and undescribed ones including Arid1a, another chromatin remodeler with key roles in neurodevelopmental and malignant disorders.

Indexed as

PeptidesProteomicsArtificial IntelligenceChromatinProteomeChromatinPeptidesProteome

Identifiers

PMID38310095
PMCPMC10838342

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.