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Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics

ABSTRACT: The rise of sample multiplexing in quantitative proteomics for the dissection of complex phenotypic comparisons has been advanced by the development of ever more sensitive and robust instrumentation. Here, we evaluated the utility of the Orbitrap Eclipse Tribrid mass spectrometer (advanced quadrupole filter, optimized FTMS scan overhead) and new instrument control software features (Precursor Fit filtering, TurboTMT and Real-time Peptide Search filtering). Multidimensional comparisons of these novel features increased total peptide identifications by 20% for SPS-MS3 methods and 14% for HRMS2 methods. Importantly Real-time Peptide Search filtering enabled a ~2X throughput improvement for quantification. Across the board, these sensitivity increases were attained without sacrificing quantitative accuracy. New hardware and software features enable more efficient characterization in pursuit of comparative whole proteome insights.

INSTRUMENT(S): Orbitrap Eclipse

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Qing Yu

LAB HEAD: Steven P Gygi

PROVIDER: PXD016766 | Pride | 2022-01-07

REPOSITORIES: Pride

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			Action	DRS
	416117_dq_00061_11cell_180min_sps_A11.mzIdentML	Mzid
	416118_dq_00060_11cell_180min_sps_A9.mzIdentML	Mzid
	416119_dq_00059_11cell_180min_sps_A7.mzIdentML	Mzid
	416120_dq_00058_11cell_180min_sps_A5.mzIdentML	Mzid
	416121_dq_00057_11cell_180min_sps_A3.mzIdentML	Mzid

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Project description:In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Dataset Information

Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics

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