TY - JOUR T1 - Distinguishing States of Arrest: Genome-Wide Descriptions of Cellular Quiescence Using ChIP-Seq and RNA-Seq Analysis. JF - Methods Mol Biol Y1 - 2018 A1 - Srivastava, Surabhi A1 - Gala, Hardik P A1 - Mishra, Rakesh K A1 - Dhawan, Jyotsna AB -

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 G 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.

VL - 1686 ER - TY - JOUR T1 - C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search. JF - BMC Bioinformatics Y1 - 2017 A1 - Sowpati, Divya Tej A1 - Srivastava, Surabhi A1 - Dhawan, Jyotsna A1 - Mishra, Rakesh K KW - Algorithms KW - Embryonic Stem Cells KW - Epigenomics KW - Genes KW - Genomics KW - HeLa Cells KW - Humans KW - Internet KW - K562 Cells KW - Promoter Regions, Genetic KW - Software KW - Transcription, Genetic KW - Web Browser AB -

BACKGROUND: Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge.

RESULTS: We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge.

CONCLUSIONS: C-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

VL - 18 IS - Suppl 10 ER -