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URL: https://dx.doi.org/DOI:10.17504/protocols.io.vp5e5q6

Authors: Lorane Texari, Carlos Guzman, Sven Heinz

Summary: Understanding the precise regulation of transcriptional programs in human health and disease requires the accurate identification and characterization of genomic regulatory networks. Next-generation sequencing (NGS) technologies are powerful, and widely applied tools to map the in vivo genome-wide location of transcription factors (TFs), histone modifications, chromatin accessibility, and nascent transcription that make up these regulatory networks. While chromatin immunoprecipitation followed by sequencing (ChIP-seq) is one of the oldest, and most-utilized experimental techniques to study the location and abundance of TFs, experiments still frequently require optimization to reproducibly yield good data with high signal-to-noise ratios due to the massive variability between possible antibody-antigen combinations and commercial reagents. .justify:after { content: ""; display:inline-block; width: 100%; } To overcome these obstacles, we systematically carried out well over 500 ChIP-seq experiments designed to test every aspect of typical ChIP-seq experiments and developed rev-ChIP, a novel ChIP-seq method that is optimized for scalability, robustness, low-input, speed, cost efficiency and data quality. We find that rev-ChIP can be scaled to work for cell numbers ranging from millions to under a thousand, and from a single sample to 500 samples a week in a non-automated fashion with minimal hands-on time. Additionally, rev-ChIP has been tested on a variety of sample types ranging from cell lines to sorted primary cells and solid tissues. .justify:after { content: ""; display:inline-block; width: 100%; }

Affiliations: University of California, San Diego, University of California, San Diego, University of California, San Diego

Version: 1

Publication Date: 2018

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