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The Surfaceome

Overview of attention for book
Cover of 'The Surfaceome'

Table of Contents

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    Book Overview
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    Chapter 1 Surfaceome Analysis Protocol for the Identification of Novel Bordetella pertussis Antigens
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    Chapter 2 “Shaving” Live Bacterial Cells with Proteases for Proteomic Analysis of Surface Proteins
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    Chapter 3 Methods for Mapping the Extracellular and Membrane Proteome in the Avian Embryo, and Identification of Putative Vascular Targets or Endothelial Genes
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    Chapter 4 Mass Spectrometry-Based Identification of Extracellular Domains of Cell Surface N-Glycoproteins: Defining the Accessible Surfaceome for Immunophenotyping Stem Cells and Their Derivatives
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    Chapter 5 Application of Higher Density Iron Oxide Nanoparticle Pellicles to Enrich the Plasma Membrane and Its Proteome from Cells in Suspension
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    Chapter 6 Proteomic Profiling of Secreted Proteins, Exosomes, and Microvesicles in Cell Culture Conditioned Media
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    Chapter 7 Cloning, Expression, and Purification of the Glycosylated Transmembrane Protein, Cation-Dependent Mannose 6-Phosphate Receptor, from Sf9 Cells Using the Baculovirus System
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    Chapter 8 Bispecific Antibody Armed T Cells to Target Cancer Cells
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    Chapter 9 Immunophenotyping of Live Human Pluripotent Stem Cells by Flow Cytometry
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    Chapter 10 Detecting Cell Surface Expression of the G Protein-Coupled Receptor CXCR4
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    Chapter 11 NaV Channels: Assaying Biosynthesis, Trafficking, Function
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    Chapter 12 High-Content Electrophysiological Analysis of Human Pluripotent Stem Cell-Derived Cardiomyocytes (hPSC-CMs)
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    Chapter 13 Methods for Evaluation of Vascular Endothelial Cell Function with Transient Receptor Potential (TRP) Channel Drugs
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    Chapter 14 Methods to Study the Signal Transduction of the Surface Receptor Tyrosine Kinase TrkB in Neurons
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    Chapter 15 Polarized Human Retinal Pigment Epithelium Exhibits Distinct Surface Proteome on Apical and Basal Plasma Membranes
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    Chapter 16 Extracellular Matrix Molecule-Based Capture of Mesenchymal Stromal Cells Under Flow
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    Chapter 17 Generation of Induced Pluripotent Stem Cells from Patients with COL3A1 Mutations and Differentiation to Smooth Muscle Cells for ECM-Surfaceome Analyses
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    Chapter 18 Fabrication and Mechanical Properties Measurements of 3D Microtissues for the Study of Cell–Matrix Interactions
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    Chapter 19 Discovery of Surface Target Proteins Linking Drugs, Molecular Markers, Gene Regulation, Protein Networks, and Disease by Using a Web-Based Platform Targets-search
Attention for Chapter 17: Generation of Induced Pluripotent Stem Cells from Patients with COL3A1 Mutations and Differentiation to Smooth Muscle Cells for ECM-Surfaceome Analyses
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Chapter title
Generation of Induced Pluripotent Stem Cells from Patients with COL3A1 Mutations and Differentiation to Smooth Muscle Cells for ECM-Surfaceome Analyses
Chapter number 17
Book title
The Surfaceome
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7553-2_17
Pubmed ID
Book ISBNs
978-1-4939-7551-8, 978-1-4939-7553-2
Authors

Jiaozi He, Zhihui Weng, Stanley Chun Ming Wu, Kenneth R. Boheler

Abstract

Use of experimentally derived induced pluripotent stem cells (iPSCs) has led to the development of cell models for differentiation, drug testing and understanding disease pathogenesis. For these models to be informative, reprogrammed cell lines need to be adequately characterized and shown to preserve all of the critical characteristics of pluripotency and differentiation. Here, we report a detailed protocol for the generation of iPSCs from human fibroblasts containing mutations in COL3A1 using a Sendai virus mediated integration-free reprogramming approach. We describe how to characterize the putative iPSCs in vivo and in vitro to ensure potency and differentiation potential. As an example of how these mutations may affect cell surface and extracellular matrix (ECM) interactions, we provide protocols for the differentiation of these cells into smooth muscle cells to illustrate how different cell types may display cell autonomous differences in collagen receptors that may affect their phenotype. These cells, when applied to mechanical model systems (see Chapter 18 by Bose et al.) facilitate an assessment of stiffness and stress-strain relationships useful for understanding how extracellular matrix dysfunction and its interactions with surface proteins contribute to disease processes.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 2 15%
Student > Master 2 15%
Other 1 8%
Unknown 4 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Medicine and Dentistry 3 23%
Agricultural and Biological Sciences 1 8%
Social Sciences 1 8%
Psychology 1 8%
Other 0 0%
Unknown 4 31%
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 22 December 2017.
All research outputs
#15,486,175
of 23,012,811 outputs
Outputs from Methods in molecular biology
#5,388
of 13,156 outputs
Outputs of similar age
#269,775
of 442,345 outputs
Outputs of similar age from Methods in molecular biology
#596
of 1,498 outputs
Altmetric has tracked 23,012,811 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,156 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 442,345 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.