Improved Sample Preparation Workflows for Protein Discovery and Quantification in Pathway Analysis

2020-07-24 11:55 来源:赛默飞 作者:
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Xiaoyue Jiang1, Ryan Bomgarden2, Sergei Snovida2, Julian Saba1, Devin Drew1, John Rogers2, Rosa Viner1, and Andreas Huhmer1
1Thermo Fisher Scientific, San Jose, CA; 2Thermo Fisher Scientific, Rockford, IL

Key Words

TMT, identification, quantification, peptide concentration assay, reproducibility, high-PH reversed-phase fractionation, Proteome Discoverer 2.1

Goal

To develop a sensitive and reproducible sample preparation workflow for proteome discovery and quantification. The sample loss in the preparation is also discussed.

Introduction

New advances in mass spectrometry (MS) enable comprehensive characterization and accurate quantification of complete proteomes, facilitating the classification of the protein expressions and regulations in signaling pathways. However, careful consideration must be undertaken to ensure changes observed in the biological samples arise from the biology rather than the analytical techniques employed in the analysis. For example, in a liquid chromatography coupled to mass spectrometry (LC-MS) approach, the variations observed in peptide/ protein identifications and quantification measurements among replicates can come from both the sample preparation and the analysis itself. In recent years, advances in instrumentation have improved LC reproducibility and MS speed/sensitivity, greatly reducing the variations from the instrument platform. This leaves sample preparation as the main source of variation preventing reproducible protein identification and quantification.

Isobaric mass tagging (e.g., Tandem Mass Tag™ (TMT™) reagents1 ) has become a common technique for relative quantification of proteins.2–5 TMT-based multiplexed relative quantification has been shown to have lower experimental variance and fewer missing quantitative values among samples compared to label-free approaches.6  Because the labeling steps are straightforward, this has been widely and successfully applied to the deep quantification of complex proteomes. However, due to the high dynamic range of the protein concentration in some organism proteomes, low abundant proteins may only be identified by one or a few unique peptides, which typically have low signal-to-noise ratios, making quantification difficult. Precise quantification can be hindered from differences in sample handling, which results in higher variation among replicates.

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