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Quantitative Proteomics by Mass Spectrometry

Overview of attention for book
Cover of 'Quantitative Proteomics by Mass Spectrometry'

Table of Contents

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    Book Overview
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    Chapter 1 Increased Depth and Breadth of Plasma Protein Quantitation via Two-Dimensional Liquid Chromatography/Multiple Reaction Monitoring-Mass Spectrometry with Labeled Peptide Standards.
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    Chapter 2 Quantitative Analysis of the Sirt5-Regulated Lysine Succinylation Proteome in Mammalian Cells.
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    Chapter 3 Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy.
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    Chapter 4 Label-Free Quantitation for Clinical Proteomics.
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    Chapter 5 Proteogenomic Methods to Improve Genome Annotation
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    Chapter 6 Mass Spectrometry-Based Quantitative O-GlcNAcomic Analysis.
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    Chapter 7 Isolating and Quantifying Plasma HDL Proteins by Sequential Density Gradient Ultracentrifugation and Targeted Proteomics.
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    Chapter 8 A Method for Label-Free, Differential Top-Down Proteomics. - PubMed - NCBI
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    Chapter 9 Multiplexed Immunoaffinity Enrichment of Peptides with Anti-peptide Antibodies and Quantification by Stable Isotope Dilution Multiple Reaction Monitoring Mass Spectrometry.
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    Chapter 10 High-Throughput Quantitative Proteomics Enabled by Mass Defect-Based 12-Plex DiLeu Isobaric Tags
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    Chapter 11 Isotopic N,N-Dimethyl Leucine (iDiLeu) for Absolute Quantification of Peptides Using a Standard Curve Approach.
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    Chapter 12 Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.
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    Chapter 13 Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays
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    Chapter 14 Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS
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    Chapter 15 Multiple and Selective Reaction Monitoring Using Triple Quadrupole Mass Spectrometer: Preclinical Large Cohort Analysis.
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    Chapter 16 Methods for SWATH™: Data Independent Acquisition on TripleTOF Mass Spectrometers. - PubMed - NCBI
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    Chapter 17 Measurement of Phosphorylated Peptides with Absolute Quantification.
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    Chapter 18 Proteomic Analysis of Protein Turnover by Metabolic Whole Rodent Pulse-Chase Isotopic Labeling and Shotgun Mass Spectrometry Analysis
Attention for Chapter 14: Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Chapter title
Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS
Chapter number 14
Book title
Quantitative Proteomics by Mass Spectrometry
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3524-6_14
Pubmed ID
Book ISBNs
978-1-4939-3522-2, 978-1-4939-3524-6
Authors

Gritsenko, Marina A, Xu, Zhe, Liu, Tao, Smith, Richard D, Marina A. Gritsenko, Zhe Xu, Tao Liu, Richard D. Smith

Editors

Salvatore Sechi

Abstract

Comprehensive, quantitative information on abundances of proteins and their posttranslational modifications (PTMs) can potentially provide novel biological insights into diseases pathogenesis and therapeutic intervention. Herein, we introduce a quantitative strategy utilizing isobaric stable isotope-labeling techniques combined with two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) for large-scale, deep quantitative proteome profiling of biological samples or clinical specimens such as tumor tissues. The workflow includes isobaric labeling of tryptic peptides for multiplexed and accurate quantitative analysis, basic reversed-phase LC fractionation and concatenation for reduced sample complexity, and nano-LC coupled to high resolution and high mass accuracy MS analysis for high confidence identification and quantification of proteins. This proteomic analysis strategy has been successfully applied for in-depth quantitative proteomic analysis of tumor samples and can also be used for integrated proteome and PTM characterization, as well as comprehensive quantitative proteomic analysis across samples from large clinical cohorts.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 20%
Student > Bachelor 2 20%
Student > Ph. D. Student 1 10%
Student > Doctoral Student 1 10%
Researcher 1 10%
Other 2 20%
Unknown 1 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 20%
Chemistry 2 20%
Engineering 2 20%
Computer Science 1 10%
Neuroscience 1 10%
Other 0 0%
Unknown 2 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 February 2016.
All research outputs
#3,357,794
of 7,187,728 outputs
Outputs from Methods in molecular biology
#1,295
of 4,948 outputs
Outputs of similar age
#151,681
of 320,712 outputs
Outputs of similar age from Methods in molecular biology
#225
of 957 outputs
Altmetric has tracked 7,187,728 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 4,948 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,712 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 957 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.