Dataset Information

PROMIS-Quan: a novel proteomic method for plasma biomarker quantification

ABSTRACT: Data from ProteomeXchange, PXD ID: PXD001194. File: Animal_20141209_MH_MPs_PC_9.mzml. Published as part of Mol Cell Proteomics. 2015 Jan 26 . From the Abstract: {{i}} To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3,000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan PROteomics of MIcroparticles with Super-SILAC Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification ... {{/i}}

INSTRUMENT(S): Instrument

ORGANISM(S): Homo_sapiens_viruses, Human

DISEASE(S): Not Available

SUBMITTER: Harel M, et al.

PROVIDER: GPM32320007536 | GPMDB |

REPOSITORIES: GPMDB

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Proteomics of microparticles with SILAC Quantification (PROMIS-Quan): a novel proteomic method for plasma biomarker quantification.

Harel Michal M Oren-Giladi Pazit P Kaidar-Person Orit O Shaked Yuval Y Geiger Tamar T

Molecular & cellular proteomics : MCP 20150126 4

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan-PROteomics of MIcroparticles with Super-St ...[more]

PMID: 25624350

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Project description:Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Plasma data is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'>MTBLS234</a>.Simulated data is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'>MTBLS235</a>.

Dataset Information

PROMIS-Quan: a novel proteomic method for plasma biomarker quantification

Publications

Proteomics of microparticles with SILAC Quantification (PROMIS-Quan): a novel proteomic method for plasma biomarker quantification.

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