Dataset Information

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies.

ABSTRACT: Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is a powerful and widely used approach to profile chromatin DNA associated with specific histone modifications, such as H3K27ac, to help identify cis-regulatory DNA elements. The manual process to complete a ChIP-Seq is labor intensive, technically challenging, and often requires large-cell numbers (>100,000 cells). The method described here helps to overcome those challenges. A complete semiautomated, microscaled H3K27ac ChIP-Seq procedure including cell fixation, chromatin shearing, immunoprecipitation, and sequencing library preparation, for batch of 48 samples for cell number inputs less than 100,000 cells is described in detail. The semiautonomous platform reduces technical variability, improves signal-to-noise ratios, and drastically reduces labor. The system can thereby reduce costs by allowing for reduced reaction volumes, limiting the number of expensive reagents such as enzymes, magnetic beads, antibodies, and hands-on time required. These improvements to the ChIP-Seq method suit perfectly for large-scale epigenetic studies of clinical samples with limited cell numbers in a highly reproducible manner.

SUBMITTER: Cayford J

PROVIDER: S-EPMC7870284 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies.

Cayford Justin J Herrera-da la Mata Sara S Schmiedel Benjamin Joachim BJ Chandra Vivek V Vijayanand Pandurangan P Seumois Grégory G

Journal of visualized experiments : JoVE 20200813 162

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is a powerful and widely used approach to profile chromatin DNA associated with specific histone modifications, such as H3K27ac, to help identify cis-regulatory DNA elements. The manual process to complete a ChIP-Seq is labor intensive, technically challenging, and often requires large-cell numbers (>100,000 cells). The method described here helps to overcome those challenges. A complete semiautomated, microscaled H3K27ac ChIP-Seq p ...[more]

PMID: 32865528

Similar Datasets

Project description:ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ?25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

Project description:BackgroundDespite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.MethodsLarge-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants.ResultsThe proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 × 10-42; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms.ConclusionsHighlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.

Dataset Information

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies.

Publications

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets