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Subcellular Proteomics

Overview of attention for book
Subcellular Proteomics
Springer Netherlands

Table of Contents

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    Book Overview
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    Chapter 1 Keynotes on Membrane Proteomics
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    Chapter 2 Two-Dimensional BAC/SDS-PAGE for Membrane Proteomics
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    Chapter 3 Microparticles: A New Tool for Plasma Membrane Sub-cellular Proteomic
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    Chapter 4 Lipid Raft Proteomics: More than Just Detergent-Resistant Membranes
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    Chapter 5 Organelle proteome variation among different cell types: lessons from nuclear membrane proteins.
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    Chapter 6 Synaptosome proteomics.
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    Chapter 7 Proteomic Analysis of Secreted Exosomes
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    Chapter 8 From Protein—Protein Complexes to Interactomics
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    Chapter 9 Supramolecular signalling complexes in the nervous system.
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    Chapter 10 Protein Networks and Complexes in Photoreceptor Cilia
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    Chapter 11 Systems Biology and the Reconstruction of the Cell: From Molecular Components to Integral Function
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    Chapter 12 Automated, systematic determination of protein subcellular location using fluorescence microscopy.
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    Chapter 13 Systems Biology of the Endoplasmic Reticulum Stress Response
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    Chapter 14 Systems Nanobiology: From Quantitative Single Molecule Biophysics to Microfluidic-Based Single Cell Analysis
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    Chapter 15 Biophotonics Applied to Proteomics
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    Chapter 16 Differential Epitope Identification of Antibodies Against Intracellular Domains of Alzheimer's Amyloid Precursor Protein Using High Resolution Affinity-mass Spectrometry
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    Chapter 17 LC-MALDI MS and MS/MS — An Efficient Tool in Proteome Analysis
Attention for Chapter 5: Organelle proteome variation among different cell types: lessons from nuclear membrane proteins.
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Chapter title
Organelle proteome variation among different cell types: lessons from nuclear membrane proteins.
Chapter number 5
Book title
Subcellular Proteomics
Published in
Sub cellular biochemistry, January 2007
DOI 10.1007/978-1-4020-5943-8_5
Pubmed ID
Book ISBNs
978-1-4020-5942-1, 978-1-4020-5943-8
Authors

Kavanagh, Deirdre M, Powell, William E, Malik, Poonam, Lazou, Vassiliki, Schirmer, Eric C, Deirdre M. Kavanagh, William E. Powell, Poonam Malik, Vassiliki Lazou, Eric C. Schirmer, Kavanagh, Deirdre M., Powell, William E., Schirmer, Eric C.

Abstract

Most subcellular organelles are expected to be similar among different cell types; however, a recent study suggests a surprising amount of variation in the protein composition at the nuclear envelope. Therefore, to comprehensively identify proteins in subcellular organelles proteomics datasets may need to be generated from multiple cell types. In this chapter we describe a proteomics study that expanded the number of nuclear membrane proteins by 5-fold using a "subtractive" methodology in which a subcellular organelle is partially purified biochemically and partially purified in silico. The biochemical fraction of interest and a separate fraction enriched in proteins known to contaminate it, in this case nuclear envelopes and microsomes respectively, are first isolated and separately analyzed by mass spectrometry. For in silico purification, proteins appearing in both fractions are subtracted from the dataset in order to identify proteins that are unique to the organelle being investigated. This approach identified 67 novel putative nuclear envelope transmembrane proteins in rodent liver. Further analysis of their expression levels in other tissues indicates that several are preferentially expressed in liver cell types, which in turn predicts considerable variation in the nuclear envelope proteome among different cell types. Finally, we discuss several issues associated with confirming that these peptide-based identifications represent proteins that truly localize to the nuclear envelope. These studies have complicated rather than simplified our view of the nuclear envelope, but proteomics has set the stage for beginning to understand this highly complex subnuclear organelle.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 33%
Student > Ph. D. Student 5 24%
Student > Doctoral Student 2 10%
Professor > Associate Professor 2 10%
Other 1 5%
Other 3 14%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 48%
Medicine and Dentistry 5 24%
Biochemistry, Genetics and Molecular Biology 4 19%
Engineering 1 5%
Unknown 1 5%