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Patient-Specific Induced Pluripotent Stem Cell Models

Overview of attention for book
Cover of 'Patient-Specific Induced Pluripotent Stem Cell Models'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 157 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 165 Patient-Specific Induced Pluripotent Stem Cell Models: Characterization of iPS Cell-Derived Cardiomyocytes.
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    Chapter 166 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 167 Modeling Axonal Phenotypes with Human Pluripotent Stem Cells.
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    Chapter 168 In Vitro Modeling of Alcohol-Induced Liver Injury Using Human-Induced Pluripotent Stem Cells.
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    Chapter 169 Modeling Genomic Imprinting Disorders Using Induced Pluripotent Stem Cells.
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    Chapter 170 Generation of Patient-Specific induced Pluripotent Stem Cell from Peripheral Blood Mononuclear Cells by Sendai Reprogramming Vectors.
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    Chapter 171 Using Human Induced Pluripotent Stem Cells to Model Skeletal Diseases
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    Chapter 172 Patient-Specific Induced Pluripotent Stem Cell Models: Generation and Characterization of Cardiac Cells.
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    Chapter 173 Generation of Cardiomyocytes from Pluripotent Stem Cells.
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    Chapter 178 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 179 A Doxycycline-Inducible System for Genetic Correction of iPSC Disease Models.
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    Chapter 194 Multisystemic Disease Modeling of Liver-Derived Protein Folding Disorders Using Induced Pluripotent Stem Cells (iPSCs).
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    Chapter 195 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 196 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 204 Generation of Integration-Free Patient Specific iPS Cells Using Episomal Plasmids Under Feeder Free Conditions.
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    Chapter 205 Generation of Human Induced Pluripotent Stem Cells Using RNA-Based Sendai Virus System and Pluripotency Validation of the Resulting Cell Population.
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    Chapter 225 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 257 Directed Myogenic Differentiation of Human Induced Pluripotent Stem Cells.
  21. Altmetric Badge
    Chapter 258 Generation and Characterization of Induced Pluripotent Stem Cells from Patients with mtDNA Mutations.
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    Chapter 267 Patient-Specific Induced Pluripotent Stem Cell Models
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    Chapter 273 Generation and Characterization of Patient-Specific iPSC Model for Cardiovascular Disease.
  24. Altmetric Badge
    Chapter 278 Transgene-Free Disease-Specific iPSC Generation from Fibroblasts and Peripheral Blood Mononuclear Cells.
Attention for Chapter 179: A Doxycycline-Inducible System for Genetic Correction of iPSC Disease Models.
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Chapter title
A Doxycycline-Inducible System for Genetic Correction of iPSC Disease Models.
Chapter number 179
Book title
Patient-Specific Induced Pluripotent Stem Cell Models
Published in
Methods in molecular biology, January 2015
DOI 10.1007/7651_2014_179
Pubmed ID
Book ISBNs
978-1-4939-3033-3, 978-1-4939-3034-0
Authors

Xiuli Sim, Fabian L Cardenas-Diaz, Deborah L French, Paul Gadue, Fabian L. Cardenas-Diaz, Deborah L. French, Sim, Xiuli, Cardenas-Diaz, Fabian L., French, Deborah L., Gadue, Paul

Editors

Andras Nagy, Kursad Turksen

Abstract

Patient-derived induced pluripotent stem cells (iPSCs) are valuable tools for the study of developmental biology and disease modeling. In both applications, genetic correction of patient iPSCs is a powerful method to understand the specific contribution of a gene(s) in development or diseased state(s). Here, we describe a protocol for the targeted integration of a doxycycline-inducible transgene expression system in a safe harbor site in iPSCs. Our gene targeting strategy uses zinc finger nucleases (ZFNs) to enhance homologous recombination at the AAVS1 safe harbor locus, thus increasing the efficiency of the site-specific integration of the two targeting vectors that make up the doxycycline-inducible system. Importantly, the use of dual-drug selection in our system increases the efficiency of positive selection for double-targeted clones to >50 %, permitting a less laborious screening process. If desired, this protocol can also be adapted to allow the use of tissue-specific promoters to drive gene expression instead of the doxycycline-inducible promoter (TRE). Additionally, this protocol is also compatible with the use of Transcription-Activator-Like Effector Nucleases (TALENs) or Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system in place of ZFNs.

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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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 37%
Student > Ph. D. Student 11 19%
Student > Bachelor 7 12%
Student > Master 4 7%
Student > Postgraduate 2 4%
Other 5 9%
Unknown 7 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 49%
Agricultural and Biological Sciences 12 21%
Business, Management and Accounting 1 2%
Nursing and Health Professions 1 2%
Computer Science 1 2%
Other 5 9%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 April 2022.
All research outputs
#7,204,203
of 23,493,900 outputs
Outputs from Methods in molecular biology
#2,187
of 13,367 outputs
Outputs of similar age
#98,416
of 357,081 outputs
Outputs of similar age from Methods in molecular biology
#163
of 1,009 outputs
Altmetric has tracked 23,493,900 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 13,367 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 83% 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 357,081 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 71% of its contemporaries.
We're also able to compare this research output to 1,009 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.