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Serum/Plasma Proteomics

Overview of attention for book
Serum/Plasma Proteomics
Springer New York

Table of Contents

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    Book Overview
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    Chapter 1 Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation
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    Chapter 2 A Protocol for the Preparation of Cryoprecipitate and Cryo-depleted Plasma for Proteomic Studies
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    Chapter 3 Preparation of Platelet Concentrates for Research and Transfusion Purposes
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    Chapter 4 Bead-Based and Multiplexed Immunoassays for Protein Profiling via Sequential Affinity Capture
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    Chapter 5 Affinity Proteomics for Fast, Sensitive, Quantitative Analysis of Proteins in Plasma
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    Chapter 6 Characterization of the Low-Molecular-Weight Human Plasma Peptidome
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    Chapter 7 In-Depth, Reproducible Analysis of Human Plasma Using IgY 14 and SuperMix Immunodepletion
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    Chapter 8 Low-Molecular-Weight Plasma Proteome Analysis Using Top-Down Mass Spectrometry
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    Chapter 9 Identification of Post-Translational Modifications from Serum/Plasma by Immunoaffinity Enrichment and LC-MS/MS Analysis Without Depletion of Abundant Proteins
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    Chapter 10 Identification of Core-Fucosylated Glycoproteome in Human Plasma
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    Chapter 11 Proteomic Analysis of Blood Extracellular Vesicles in Cardiovascular Disease by LC-MS/MS Analysis
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    Chapter 12 Targeted Approach for Proteomic Analysis of a Hidden Membrane Protein
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    Chapter 13 Red Blood Cells in Clinical Proteomics
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    Chapter 14 High-Throughput Quantitative Lipidomics Analysis of Nonesterified Fatty Acids in Plasma by LC-MS
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    Chapter 15 Simultaneous Enrichment of Plasma Extracellular Vesicles and Glycoproteome for Studying Disease Biomarkers
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    Chapter 16 Lipidomics of Human Blood Plasma by High-Resolution Shotgun Mass Spectrometry
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    Chapter 17 Proteomics Analysis of Circulating Serum Exosomes
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    Chapter 18 High-Density Serum/Plasma Reverse Phase Protein Arrays
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    Chapter 19 Antibody Colocalization Microarray for Cross-Reactivity-Free Multiplexed Protein Analysis
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    Chapter 20 Surface Profiling of Extracellular Vesicles from Plasma or Ascites Fluid Using DotScan Antibody Microarrays
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    Chapter 21 Serum Profiling for Identification of Autoantibody Signatures in Diseases Using Protein Microarrays
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    Chapter 22 Quantitative Comparisons of Large Numbers of Human Plasma Samples Using TMT10plex Labeling
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    Chapter 23 Efficient Quantitative Comparisons of Plasma Proteomes Using Label-Free Analysis with MaxQuant
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    Chapter 24 Blood and Plasma Proteomics: Targeted Quantitation and Posttranslational Redox Modifications
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    Chapter 25 SWATH Mass Spectrometry for Proteomics of Non-Depleted Plasma
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    Chapter 26 Shotgun and Targeted Plasma Proteomics to Predict Prognosis of Non-Small Cell Lung Cancer
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    Chapter 27 High-Throughput Parallel Proteomic Sample Preparation Using 96-Well Polyvinylidene Fluoride (PVDF) Membranes and C18 Purification Plates
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    Chapter 28 Targeted Quantification of the Glycated Peptides of Human Serum Albumin
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    Chapter 29 Absolute Quantification of Middle- to High-Abundant Plasma Proteins via Targeted Proteomics
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    Chapter 30 A Highly Automated Shotgun Proteomic Workflow: Clinical Scale and Robustness for Biomarker Discovery in Blood
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    Chapter 31 Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation
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    Chapter 32 Metabolomics Toward Biomarker Discovery
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    Chapter 33 Plasma Biomarker Identification and Quantification by Microparticle Proteomics
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    Chapter 34 Bronchoalveolar Lavage: Quantitative Mass Spectrometry-Based Proteomics Analysis in Lung Diseases
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    Chapter 35 Protein Multiplexed Immunoassay Analysis with R
Attention for Chapter 32: Metabolomics Toward Biomarker Discovery
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Chapter title
Metabolomics Toward Biomarker Discovery
Chapter number 32
Book title
Serum/Plasma Proteomics
Published in
Methods in molecular biology, July 2017
DOI 10.1007/978-1-4939-7057-5_32
Pubmed ID
Book ISBNs
978-1-4939-7056-8, 978-1-4939-7057-5
Authors

Peiyuan Yin, Guowang Xu

Editors

David W. Greening, Richard J. Simpson

Abstract

Metabolomics has been used as practical tool in the discovery of novel biomarkers in a broad area in the clinic. The analytical platforms including nuclear magnetic resonance (NMR) and mass spectrometry (MS) can cover thousands of metabolites. With the help of multivariate data analysis, many potential biomarkers can be defined in the studies. Since metabolites stand at the end point of metabolism, it remains difficult to find novel biomarkers with good diagnostic or prognostic performance. In this chapter, we will introduce a general protocol for biomarker discovery within the scope of metabolomics using MS.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 18%
Student > Ph. D. Student 3 18%
Researcher 2 12%
Student > Doctoral Student 1 6%
Student > Bachelor 1 6%
Other 4 24%
Unknown 3 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 29%
Agricultural and Biological Sciences 3 18%
Medicine and Dentistry 2 12%
Chemistry 2 12%
Unspecified 1 6%
Other 0 0%
Unknown 4 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 July 2017.
All research outputs
#18,135,049
of 23,298,349 outputs
Outputs from Methods in molecular biology
#7,411
of 13,337 outputs
Outputs of similar age
#225,932
of 314,419 outputs
Outputs of similar age from Methods in molecular biology
#140
of 264 outputs
Altmetric has tracked 23,298,349 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,337 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 314,419 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 264 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.