Frequent switching of Polycomb repressive marks and DNA hypermethylation in the PC3 prostate cancer cell line.

Epigenetic reprogramming is commonly observed in cancer, and is hypothesized to involve multiple mechanisms, including DNA methylation and Polycomb repressive complexes (PRCs). Here we devise a new experimental and analytical strategy using customized high-density tiling arrays to investigate coordinated patterns of gene expression, DNA methylation, and Polycomb marks which differentiate prostate cancer cells from their normal counterparts. Three major changes in the epigenomic landscape distinguish the two cell types. Developmentally significant genes containing CpG islands which are silenced by PRCs in the normal cells acquire DNA methylation silencing and lose their PRC marks (epigenetic switching). Because these genes are normally silent this switch does not cause de novo repression but might significantly reduce epigenetic plasticity. Two other groups of genes are silenced by either de novo DNA methylation without PRC occupancy (5mC reprogramming) or by de novo PRC occupancy without DNA methylation (PRC reprogramming). Our data suggest that the two silencing mechanisms act in parallel to reprogram the cancer epigenome and that DNA hypermethylation may replace Polycomb-based repression near key regulatory genes, possibly reducing their regulatory plasticity.

Genome-scale ChIP-chip analysis using 10,000 human cells.

The technique of chromatin immunoprecipitation (ChIP) is a powerful method for identifying in vivo DNA binding sites of transcription factors and for studying chromatin modifications. Unfortunately, the large number of cells needed for the standard ChIP protocol has hindered the analysis of many biologically interesting cell populations that are difficult to obtain in large numbers. New ChIP methods involving the use of carrier chromatin have been developed that allow the one-gene-at-a-time analysis of very small numbers of cells. However such methods are not useful if the resultant sample will be applied to genomic microarrays or used in ChIP-sequencing assays. Therefore, we have miniaturized the ChIP protocol such that as few as 10,000 cells (without the addition of carrier reagents) can be used to obtain enough sample material to analyze the entire human genome. We demonstrate the reproducibility of this MicroChIP technique using 2.1 million feature high-density oligonucleotide arrays and antibodies to RNA polymerase II and to histone H3 trimethylated on lysine 27 or lysine 9.

Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets.

The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated.