Dataset Information

Stop trashing your spectra! Use a “Quantify then Identify” pipeline based on machine learning to maximize your isobaric tagging data

ABSTRACT: Being inspired by metabolomic data processing, we have developed a bioinformatic pipeline that optimizes the processing of mass spectral data obtained from isobaric Tandem Mass Tag (TMT) experiments. Our method focuses on the tandem mass spectral level by first quantifying and then identifying (QtI), while preserving unidentified spectra for further investigations. The raw datasets were previously generated [1, 2]. Two-proteome model experiments were considered where identical pools of human CSF or plasma samples were mixed with E. coli samples at different concentrations. E. coli protein extract was spiked in 400 µL of CSF at amounts of 0, 2, 3, 5, 6.25, and 7.5 µg. Such sets of 6 spiked CSF samples were prepared in triplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on two independent but identical LC MS/MS, for a total of 18 raw files [1]; this experiment is called “CSF-E.coli”. E. coli protein extract was spiked at 0, 2.5, 5, 6.25, 12.5, and 25 µg in 30 µL human plasma. Such sets of 6 spiked plasma samples were prepared in quadruplicate for comparison using sixplex isobaric tagging and analyzed in triplicates on one LC MS/MS, for a total of 12 raw files [2]; this experiment is referred as “Plasma-E.coli”.The so-called “96samples-CSF” experiment consists of 16 replicate TMT sixplex experiments measuring identical CSF samples from the pool described above [2], analyzed in triplicates on one LCMS/MS for a total of 48 raw files. The so-called “96samples-plasma” experiment consists of 16 replicate TMT sixplex experiments measuring identical plasma samples from the pool described before, analyzed in triplicates on one LC MS/MS for a total of 48 raw files [1]. References: [1] Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. & Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 13, 3837-3845 (2014). [2] Núñez Galindo, A., Kussmann, M. & Dayon, L. Proteomics of Cerebrospinal Fluid: Throughput and Robustness Using a Scalable Automated Analysis Pipeline for Biomarker Discovery. Anal. Chem. 87, 10755-10761 (2015).

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

TISSUE(S): Blood Plasma, Cerebrospinal Fluid, Cell Culture

SUBMITTER: John Corthésy

LAB HEAD: Loïc Dayon

PROVIDER: PXD005206 | Pride | 2018-05-01

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	96samples_CSF.7z	Other
	96samples_CSF_QTI.sf3	Other
	96samples_CSF_standard_approach.sf3	Other
	96samples_Plasma.7z	Other
	96samples_csf_QTI_report.tsv	Tabular

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Publications

An Adaptive Pipeline To Maximize Isobaric Tagging Data in Large-Scale MS-Based Proteomics.

Corthésy John J Theofilatos Konstantinos K Mavroudi Seferina S Macron Charlotte C Cominetti Ornella O Remlawi Mona M Ferraro Francesco F Núñez Galindo Antonio A Kussmann Martin M Likothanassis Spiridon S Dayon Loïc L

Journal of proteome research 20180504 6

Isobaric tagging is the method of choice in mass-spectrometry-based proteomics for comparing several conditions at a time. Despite its multiplexing capabilities, some drawbacks appear when multiple experiments are merged for comparison in large sample-size studies due to the presence of missing values, which result from the stochastic nature of the data-dependent acquisition mode. Another indirect cause of data incompleteness might derive from the proteomic-typical data-processing workflow that ...[more]

PMID: 29695160

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