Dataset Information

HOTMAQ: A Multiplexed Absolute Quantification Method for Targeted Proteomics.

ABSTRACT: Absolute quantification in targeted proteomics is challenging due to a variety of factors, including low specificity in complex backgrounds, limited analytical throughput, and wide dynamic range. To address these problems, we developed a hybrid offset-triggered multiplex absolute quantification (HOTMAQ) strategy that combines cost-effective mass difference and isobaric tags to enable simultaneous construction of an internal standard curve in the MS1 precursor scan, real-time identification of peptides at the MS2 level, and mass offset-triggered accurate quantification of target proteins in synchronous precursor selection (SPS)-MS3 spectra. This approach increases the analytical throughput of targeted quantitative proteomics by up to 12-fold. The HOTMAQ strategy was employed to verify candidate protein biomarkers in preclinical Alzheimer's disease with high accuracy. The greatly enhanced throughput and quantitative performance, paired with sample flexibility, makes HOTMAQ broadly applicable to targeted peptidomics, proteomics, and phosphoproteomics.

SUBMITTER: Zhong X

PROVIDER: S-EPMC6379083 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

HOTMAQ: A Multiplexed Absolute Quantification Method for Targeted Proteomics.

Zhong Xiaofang X Yu Qinying Q Ma Fengfei F Frost Dustin C DC Lu Lei L Chen Zhengwei Z Zetterberg Henrik H Carlsson Cynthia C Okonkwo Ozioma O Li Lingjun L

Analytical chemistry 20190118 3

Absolute quantification in targeted proteomics is challenging due to a variety of factors, including low specificity in complex backgrounds, limited analytical throughput, and wide dynamic range. To address these problems, we developed a hybrid offset-triggered multiplex absolute quantification (HOTMAQ) strategy that combines cost-effective mass difference and isobaric tags to enable simultaneous construction of an internal standard curve in the MS<sup>1</sup> precursor scan, real-time identific ...[more]

PMID: 30608134

Similar Datasets

Project description:Sepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host-pathogen interactions.

Dataset Information

HOTMAQ: A Multiplexed Absolute Quantification Method for Targeted Proteomics.

Publications

HOTMAQ: A Multiplexed Absolute Quantification Method for Targeted Proteomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets