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MicroRNA Protocols

Overview of attention for book
Cover of 'MicroRNA Protocols'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The MicroRNA
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    Chapter 2 Analysis of MicroRNA Length Variety Generated by Recombinant Human Dicer
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    Chapter 3 A User-Friendly Computational Workflow for the Analysis of MicroRNA Deep Sequencing Data
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    Chapter 4 The RNA Gene Information: Retroelement-MicroRNA Entangling as the RNA Quantum Code
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    Chapter 5 Setting Up an Intronic miRNA Database
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    Chapter 6 Identify Intronic MicroRNA with Bioinformatics
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    Chapter 7 Experimental miRNA Target Validation
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    Chapter 8 Enrichment Analysis of miRNA Targets
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    Chapter 9 MicroRNA Expression Profiling During Neural Differentiation of Mouse Embryonic Carcinoma P19 Cells.
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    Chapter 10 Evaluating the MicroRNA Targeting Sites by Luciferase Reporter Gene Assay.
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    Chapter 11 Expression Pattern Analysis of MicroRNAs in Caenorhabditis elegans
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    Chapter 12 Use of Viral Systems to Study miRNA-Mediated Regulation of Gene Expression in Human Cells.
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    Chapter 13 Cloning, Expression, and Functional Analysis of Genomic miRNA Using Retroviral System in Cancer Cells
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    Chapter 14 Preparing Synaptoneurosomes from Adult Mouse Forebrain
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    Chapter 15 Isolation of Total RNA and Detection Procedures for miRNA Present in Bovine-Cultured Adipocytes and Adipose Tissues
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    Chapter 16 miRNA and shRNA Expression Vectors Based on mRNA and miRNA Processing.
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    Chapter 17 Gene Silencing In Vitro and In Vivo Using Intronic MicroRNAs.
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    Chapter 18 Vector-Free Methods for Manipulating miRNA Activity In Vitro and In Vivo
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    Chapter 19 MicroRNA Expression Profiling of Human-Induced Pluripotent and Embryonic Stem Cells
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    Chapter 20 MicroRNA Expression During Neuronal Differentiation of Human Teratocarcinoma NTera2D1 and Mouse Embryonic Carcinoma P19 Cells
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    Chapter 21 Isolation and Identification of Gene-Specific MicroRNAs
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    Chapter 22 Transgene-Like Animal Models Using Intronic MicroRNAs.
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    Chapter 23 Mechanism and Method for Generating Tumor-Free iPS Cells Using Intronic MicroRNA miR-302 Induction.
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    Chapter 24 Salivary MicroRNAs and Oral Cancer Detection
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    Chapter 25 Application of Intronic MicroRNA Agents in Cosmetics
  27. Altmetric Badge
    Chapter 26 MicroRNAs in Skin and Wound Healing
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    Chapter 27 MiRNA Targets of Prostate Cancer.
Attention for Chapter 23: Mechanism and Method for Generating Tumor-Free iPS Cells Using Intronic MicroRNA miR-302 Induction.
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Chapter title
Mechanism and Method for Generating Tumor-Free iPS Cells Using Intronic MicroRNA miR-302 Induction.
Chapter number 23
Book title
MicroRNA Protocols
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-62703-083-0_23
Pubmed ID
Book ISBNs
978-1-62703-082-3, 978-1-62703-083-0
Authors

Lin SL, Ying SY, Shi-Lung Lin, Shao-Yao Ying, Lin, Shi-Lung, Ying, Shao-Yao

Abstract

Today's researchers generating induced pluripotent stem cells (iPS cells or iPSCs) usually consider the pluripotency first, then, the potential tumorigenicity. Oncogenic factors such as c-Myc and Klf4 were frequently used to boost the survival and proliferative rates of iPSCs, creating the inevitable problem of tumorigenicity that hindered the therapeutic usefulness of the iPSCs. To prevent tumorigenecity in stem cells, we have examined mechanism(s) by which the cell cycle genes of embryonic cells were regulated. Naturally occurring embryonic stem cells (ESCs) possess two unique stemness properties: pluripotent differentiation into almost all cell types and self-renewal with no risk of tumor formation. These two features are also important for the use of ESCs or iPSCs in therapy. Currently, despite overwhelming reports describing iPSC pluripotency, there have been no observations of tumor prevention mechanism(s) that suppresses tumor formation similar to that in naturally occurring ESCs. Our recent studies have revealed, for the first time, a ESC-specific microRNA (miRNA), miR-302, which was responsible for regulating human iPSC tumorigenicity through co-suppression of both cyclin E-CDK2 and cyclin D-CDK4/6 cell cycle pathways during G1-S phase transition. Additionally, miR-302 also silenced BMI-1, a cancer stem cell marker gene, to promote the expression of two senescence-associated tumor suppressor genes, p16Ink4a and p14/p19Arf. Together, the combinatory effect of reducing G1-S cell cycle transition and increasing p16/p14(p19) expression resulted in a relatively attenuated cell cycle rate similar to that of 2-8-cell-stage embryonic cells in early mammalian zygotes (20-24 h/cycle), as compared to the fast proliferation rate of iPSCs induced by four defined factors Oct4-Sox2-Klf4-c-Myc (12-16 h/cycle). These findings provide a means to control iPSC tumorigenicity and improve the safety of iPSCs in the therapeutic use. In this chapter, we reviewed the mechanism underlying miR-302-mediated tumor suppression and then applied this mechanism to generate tumor-free iPSCs. The same strategy can also be used to prevent ESC tumorigenicity.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 21%
Researcher 4 21%
Professor > Associate Professor 3 16%
Student > Doctoral Student 2 11%
Professor 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 37%
Biochemistry, Genetics and Molecular Biology 6 32%
Medicine and Dentistry 2 11%
Unknown 4 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 November 2015.
All research outputs
#14,151,903
of 22,679,690 outputs
Outputs from Methods in molecular biology
#4,150
of 13,038 outputs
Outputs of similar age
#167,396
of 280,619 outputs
Outputs of similar age from Methods in molecular biology
#156
of 339 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,038 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 64% 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 280,619 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 339 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.