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Kinase Signaling Networks

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Cover of 'Kinase Signaling Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Optogenetic Control of Ras/Erk Signaling Using the Phy–PIF System
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    Chapter 2 Dissecting Kinase Effector Signaling Using the RapRTAP Methodology
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    Chapter 3 Single-Cell Imaging of ERK Signaling Using Fluorescent Biosensors
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    Chapter 4 Quantification of Cell Signaling Networks Using Kinase Activity Chemosensors
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    Chapter 5 Expression of Recombinant Phosphoproteins for Signal Transduction Studies
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    Chapter 6 Allosteric Modulation of Src Family Kinases with ATP-Competitive Inhibitors
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    Chapter 7 Characterization of Ligand Binding to Pseudokinases Using a Thermal Shift Assay
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    Chapter 8 Proteomic Profiling of Protein Kinase Inhibitor Targets by Mass Spectrometry
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    Chapter 9 Utilizing the Luminex Magnetic Bead-Based Suspension Array for Rapid Multiplexed Phosphoprotein Quantification
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    Chapter 10 High-Content Imaging and RNAi Screens for Investigating Kinase Network Plasticity
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    Chapter 11 Analysis of Drug Resistance Using Kinome-Wide Functional Screens
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    Chapter 12 Identification and Validation of Driver Kinases from Next-Generation Sequencing Data
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    Chapter 13 Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis
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    Chapter 14 Cell-Specific Labeling for Analyzing Bidirectional Signaling by Mass Spectrometry
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    Chapter 15 Characterization of the Phospho-Adhesome by Mass Spectrometry-Based Proteomics
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    Chapter 16 Analysis of Phosphotyrosine Signaling Networks in Lung Cancer Cell Lines
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    Chapter 17 Targeted Analysis of Phosphotyrosine Signaling by Multiple Reaction Monitoring Mass Spectrometry
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    Chapter 18 Phosphoproteomic Analysis of Isolated Mitochondria in Yeast
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    Chapter 19 A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages
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    Chapter 20 Absolute Phosphorylation Stoichiometry Analysis by Motif-Targeting Quantitative Mass Spectrometry
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    Chapter 21 Identification of Plant Kinase Substrates Based on Kinase Assay-Linked Phosphoproteomics
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    Chapter 22 Mass Spectrometry Analysis of Spatial Protein Networks by Colocalization Analysis (COLA)
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    Chapter 23 Development of Selected Reaction Monitoring Methods to Systematically Quantify Kinase Abundance and Phosphorylation Stoichiometry in Human Samples
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    Chapter 24 Analysis of Signaling Networks at the Single-Cell Level Using Mass Cytometry
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    Chapter 25 Magnetic Resonance Spectroscopy (MRS)-Based Methods for Examining Cancer Metabolism in Response to Oncogenic Kinase Drug Treatment
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    Chapter 26 Deconstructing the Metabolic Networks of Oncogenic Signaling Using Targeted Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)
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    Chapter 27 Modeling of Receptor Tyrosine Kinase Signaling: Computational and Experimental Protocols
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    Chapter 28 An Interdisciplinary Approach for Designing Kinetic Models of the Ras/MAPK Signaling Pathway
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    Chapter 29 Databases and Computational Tools for Evolutionary Analysis of Protein Phosphorylation
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    Chapter 30 Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations
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    Chapter 31 Target Inhibition Maps Based on Responses to Kinase Inhibitors
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    Chapter 32 Partial Least Squares Regression Models for the Analysis of Kinase Signaling
Attention for Chapter 13: Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis
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Chapter title
Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis
Chapter number 13
Book title
Kinase Signaling Networks
Published in
Methods in molecular biology, July 2017
DOI 10.1007/978-1-4939-7154-1_13
Pubmed ID
Book ISBNs
978-1-4939-7152-7, 978-1-4939-7154-1
Authors

Edmund Wilkes, Pedro R. Cutillas

Abstract

Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.

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 > Ph. D. Student 6 35%
Student > Master 2 12%
Other 1 6%
Lecturer 1 6%
Student > Doctoral Student 1 6%
Other 3 18%
Unknown 3 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 29%
Agricultural and Biological Sciences 2 12%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Unspecified 1 6%
Business, Management and Accounting 1 6%
Other 3 18%
Unknown 4 24%