Dataset Information

Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization.

ABSTRACT: BACKGROUND:Chromatin remodeling, histone modifications and other chromatin-related processes play a crucial role in gene regulation. A very useful technique to study these processes is chromatin immunoprecipitation (ChIP). ChIP is widely used for a few model systems, including Arabidopsis, but establishment of the technique for other organisms is still remarkably challenging. Furthermore, quantitative analysis of the precipitated material and normalization of the data is often underestimated, negatively affecting data quality. RESULTS:We developed a robust ChIP protocol, using maize (Zea mays) as a model system, and present a general strategy to systematically optimize this protocol for any type of tissue. We propose endogenous controls for active and for repressed chromatin, and discuss various other controls that are essential for successful ChIP experiments. We experienced that the use of quantitative PCR (QPCR) is crucial for obtaining high quality ChIP data and we explain why. The method of data normalization has a major impact on the quality of ChIP analyses. Therefore, we analyzed different normalization strategies, resulting in a thorough discussion of the advantages and drawbacks of the various approaches. CONCLUSION:Here we provide a robust ChIP protocol and strategy to optimize the protocol for any type of tissue; we argue that quantitative real-time PCR (QPCR) is the best method to analyze the precipitates, and present comprehensive insights into data normalization.

SUBMITTER: Haring M

PROVIDER: S-EPMC2077865 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization.

Haring Max M Offermann Sascha S Danker Tanja T Horst Ina I Peterhansel Christoph C Stam Maike M

Plant methods 20070924

<h4>Background</h4>Chromatin remodeling, histone modifications and other chromatin-related processes play a crucial role in gene regulation. A very useful technique to study these processes is chromatin immunoprecipitation (ChIP). ChIP is widely used for a few model systems, including Arabidopsis, but establishment of the technique for other organisms is still remarkably challenging. Furthermore, quantitative analysis of the precipitated material and normalization of the data is often underestim ...[more]

PMID: 17892552

Similar Datasets

Project description:Chromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) has served as the central method for the study of histone modifications for the past decade. In ChIP-seq analyses, antibodies selectively capture nucleosomes bearing a modification of interest and the associated DNA is then mapped to the genome to determine the distribution of the mark. This approach has several important drawbacks: (i) ChIP interpretation necessitates the assumption of perfect antibody specificity, despite growing evidence that this is often not the case. (ii) Common methods for evaluating antibody specificity in other formats have little or no bearing on specificity within a ChIP experiment. (iii) Uncalibrated ChIP is reported as relative enrichment, which is biologically meaningless outside the experimental reference frame defined by a discrete immunoprecipitation (IP), thus preventing facile comparison across experimental conditions or modifications. (iv) Differential library amplification and loading onto next-generation sequencers, as well as computational normalization, can further compromise quantitative relationships that may exist between samples. Consequently, the researcher is presented with a series of potential pitfalls and is blind to nearly all of them. Here we provide a detailed protocol for internally calibrated ChIP (ICeChIP), a method we recently developed to resolve these problems by spike-in of defined nucleosomal standards within a ChIP procedure. This protocol is optimized for specificity and quantitative power, allowing for measurement of antibody specificity and absolute measurement of histone modification density (HMD) at genomic loci on a biologically meaningful scale enabling unambiguous comparisons. We provide guidance on optimal conditions for next-generation sequencing (NGS) and instructions for data analysis. This protocol takes between 17 and 18 h, excluding time for sequencing or bioinformatic analysis. The ICeChIP procedure enables accurate measurement of histone post-translational modifications (PTMs) genome-wide in mammalian cells as well as Drosophila melanogaster and Caenorhabditis elegans, indicating suitability for use in eukaryotic cells more broadly.

Project description:Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Dataset Information

Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization.

Publications

Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets