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Cellular Quiescence

Overview of attention for book
Cover of 'Cellular Quiescence'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular Regulation of Cellular Quiescence: A Perspective from Adult Stem Cells and Its Niches
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    Chapter 2 An In Vitro Model of Cellular Quiescence in Primary Human Dermal Fibroblasts
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    Chapter 3 Flow Cytometric Detection of G0 in Live Cells by Hoechst 33342 and Pyronin Y Staining
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    Chapter 4 Using Carboxy Fluorescein Succinimidyl Ester (CFSE) to Identify Quiescent Glioblastoma Stem-Like Cells
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    Chapter 5 Isolation of Neural Stem and Progenitor Cells from the Adult Brain and Live Imaging of Their Cell Cycle with the FUCCI System
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    Chapter 6 Determination of Histone 2B–Green Fluorescent Protein (GFP) Retention in Intestinal Stem Cells
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    Chapter 7 Detecting Hematopoietic Stem Cell Proliferation Using BrdU Incorporation
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    Chapter 8 Cell Cycle Analysis by Mass Cytometry
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    Chapter 9 Preparation and Analysis of Saccharomyces cerevisiae Quiescent Cells
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    Chapter 10 Identifying Quiescent Stem Cells in Hair Follicles
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    Chapter 11 Single EDL Myofiber Isolation for Analyses of Quiescent and Activated Muscle Stem Cells
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    Chapter 12 Investigating Cellular Quiescence of T Lymphocytes and Antigen-Induced Exit from Quiescence
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    Chapter 13 Retroviral Transduction of Quiescent Murine Hematopoietic Stem Cells
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    Chapter 14 Analysis of Murine Hematopoietic Stem Cell Proliferation During Inflammation
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    Chapter 15 A Facile, In Vitro 384-Well Plate System to Model Disseminated Tumor Cells in the Bone Marrow Microenvironment
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    Chapter 16 Distinguishing States of Arrest: Genome-Wide Descriptions of Cellular Quiescence Using ChIP-Seq and RNA-Seq Analysis
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    Chapter 17 Analysis of lncRNA-Protein Interactions by RNA-Protein Pull-Down Assays and RNA Immunoprecipitation (RIP)
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    Chapter 18 Analysis of MicroRNA-Mediated Translation Activation of In Vitro Transcribed Reporters in Quiescent Cells
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    Chapter 19 Genome-Wide Identification of Transcription Factor-Binding Sites in Quiescent Adult Neural Stem Cells
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    Chapter 20 Study Quiescence Heterogeneity by Coupling Single-Cell Measurements and Computer Modeling
Attention for Chapter 16: Distinguishing States of Arrest: Genome-Wide Descriptions of Cellular Quiescence Using ChIP-Seq and RNA-Seq Analysis
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  • High Attention Score compared to outputs of the same age and source (81st percentile)

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Chapter title
Distinguishing States of Arrest: Genome-Wide Descriptions of Cellular Quiescence Using ChIP-Seq and RNA-Seq Analysis
Chapter number 16
Book title
Cellular Quiescence
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7371-2_16
Pubmed ID
Book ISBNs
978-1-4939-7370-5, 978-1-4939-7371-2
Authors

Surabhi Srivastava, Hardik P. Gala, Rakesh K. Mishra, Jyotsna Dhawan

Abstract

Regenerative potential in adult stem cells is closely associated with the establishment of-and exit from-a temporary state of quiescence. Emerging evidence not only provides a rationale for the link between lineage determination programs and cell cycle regulation but also highlights the understanding of quiescence as an actively maintained cellular program, encompassing networks and mechanisms beyond mitotic inactivity or metabolic restriction. Interrogating the quiescent genome and transcriptome using deep-sequencing technologies offers an unprecedented view of the global mechanisms governing this reversibly arrested cellular state and its importance for cell identity. While many efforts have identified and isolated pure target stem cell populations from a variety of adult tissues, there is a growing appreciation that their isolation from the stem cell niche in vivo leads to activation and loss of hallmarks of quiescence. Thus, in vitro models that recapitulate the dynamic reversibly arrested stem cell state in culture and lend themselves to comparison with the activated or differentiated state are useful templates for genome-wide analysis of the quiescence network.In this chapter, we describe the methods that can be adopted for whole genome epigenomic and transcriptomic analysis of cells derived from one such established culture model where mouse myoblasts are triggered to enter or exit quiescence as homogeneous populations. The ability to synchronize myoblasts in G0 permits insights into the genome in "deep quiescence." The culture methods for generating large populations of quiescent myoblasts in either 2D or 3D culture formats are described in detail in a previous chapter in this series (Arora et al. Methods Mol Biol 1556:283-302, 2017). Among the attractive features of this model are that genes isolated from quiescent myoblasts in culture mark satellite cells in vivo (Sachidanandan et al., J Cell Sci 115:2701-2712, 2002) providing a validation of its approximation of the molecular state of true stem cells. Here, we provide our working protocols for ChIP-seq and RNA-seq analysis, focusing on those experimental elements that require standardization for optimal analysis of chromatin and RNA from quiescent myoblasts, and permitting useful and revealing comparisons with proliferating myoblasts or differentiated myotubes.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 27%
Student > Master 3 27%
Student > Ph. D. Student 1 9%
Student > Doctoral Student 1 9%
Researcher 1 9%
Other 1 9%
Unknown 1 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Agricultural and Biological Sciences 2 18%
Chemical Engineering 1 9%
Medicine and Dentistry 1 9%
Neuroscience 1 9%
Other 0 0%
Unknown 2 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 June 2018.
All research outputs
#12,862,254
of 23,006,268 outputs
Outputs from Methods in molecular biology
#3,233
of 13,160 outputs
Outputs of similar age
#201,848
of 442,254 outputs
Outputs of similar age from Methods in molecular biology
#270
of 1,498 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,160 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 442,254 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 53% of its contemporaries.
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 has done well, scoring higher than 81% of its contemporaries.