Comparative Study of Targeted and Label-free Mass Spectrometry Methods for Protein Quantification

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

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Issues and Applications in Label-Free Quantitative Mass Spectrometry

International Journal of Proteomics ◽

10.1155/2013/756039 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 14

Author(s):

Xianyin Lai ◽

Lianshui Wang ◽

Frank A. Witzmann

Keyword(s):

Mass Spectrometry ◽

Protein Quantification ◽

Area Measurement ◽

Label Free ◽

Quantitative Mass Spectrometry ◽

Differential Protein Expression ◽

Membrane Proteomics ◽

Complex Protein ◽

Tissue Proteomics

To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

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LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors

10.1101/328864 ◽

2018 ◽

Cited By ~ 1

Author(s):

Cheng Chang ◽

Zhiqiang Gao ◽

Wantao Ying ◽

Yan Zhao ◽

Yan Fu ◽

...

Keyword(s):

Mass Spectrometry ◽

Physicochemical Properties ◽

Data Acquisition ◽

Large Scale ◽

Protein Quantification ◽

Label Free ◽

Crucial Issue ◽

Accuracy And Precision ◽

Quantification Accuracy ◽

Novel Algorithm

AbstractMass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.

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Quantitative analysis of genetically modified soya using multiple reaction monitoring mass spectrometry with endogenous peptides as internal standards

European Journal of Mass Spectrometry ◽

10.1177/1469066718802548 ◽

2018 ◽

Vol 25 (1) ◽

pp. 50-57 ◽

Cited By ~ 1

Author(s):

Shobha Devi ◽

Yi-Cheng Lin ◽

Yen-Peng Ho

Keyword(s):

Mass Spectrometry ◽

Genetically Modified ◽

Multiple Reaction Monitoring ◽

Linear Regression Analysis ◽

Exchange Chromatography ◽

Protein Quantification ◽

Reaction Monitoring ◽

Label Free ◽

Internal Standards ◽

Phosphate Synthase

A simple label-free method was developed for the quantification of the herbicide-resistant gene-related protein 5-enolpyruvylshikimate-3-phosphate synthase using multiple reaction monitoring liquid chromatography–mass spectrometry. Sample pretreatment procedures including ion exchange chromatography and CaCl2 precipitation were used to purify the 5-enolpyruvylshikimate-3-phosphate synthase protein. Quantification of various percentages of genetically modified soya (0.5–100%) was performed by selecting suitable endogenous soybean peptides as internal standards. Results indicated that Gly P (QGDVFVVPR) and Lec P (LQLNK) are useful internal standards for the quantification of low and high percentages of genetically modified soya, respectively. Linear regression analysis of both calibration curves yielded good linearity with R2 of 0.99. This approach is a convenient and accurate quantification method for genetically modified soya at a level as low as 0.5% (less than the current EU threshold for labeling genetically modified soya).

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