Dataset Information

Selection of reference peptides for absolute quantification of budding yeast proteome

ABSTRACT: Absolute quantification of proteome is one of the most important tasks in proteomic research. The aim in this analysis is selection of reference tryptic peptides used for stable isotope-labeled standard, based on their spectral peak intensities. Approximate abundance is also calculated by label-free quantitative method, in which identification frequency (counts of peptide spectral matchings) in each protein is normalized by observability of peptide ions to calculate relative copy number of proteins. For proteins with higher relative copy number, peptide ions that fulfill following criteria — 2- or 3-charge state, without methionine residues and well known post-translational modification sites — are selected as reference tryptic peptides.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

TISSUE(S): Cell Culture

SUBMITTER: Keiji Kito

LAB HEAD: Keiji Kito

PROVIDER: PXD003085 | Pride | 2016-10-06

REPOSITORIES: Pride

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Publications

A strategy for absolute proteome quantification with mass spectrometry by hierarchical use of peptide-concatenated standards.

Kito Keiji K Okada Mitsuhiro M Ishibashi Yuko Y Okada Satoshi S Ito Takashi T

Proteomics 20160428 10

The accurate and precise absolute abundance of proteins can be determined using mass spectrometry by spiking the sample with stable isotope-labeled standards. In this study, we developed a strategy of hierarchical use of peptide-concatenated standards (PCSs) to quantify more proteins over a wider dynamic range. Multiple primary PCSs were used for quantification of many target proteins. Unique "ID-tag peptides" were introduced into individual primary PCSs, allowing us to monitor the exact amounts ...[more]

PMID: 27030420

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Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.

Dataset Information

Selection of reference peptides for absolute quantification of budding yeast proteome

Publications

A strategy for absolute proteome quantification with mass spectrometry by hierarchical use of peptide-concatenated standards.

Similar Datasets

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