High-resolution genome-wide mapping of the primary structure of chromatin

Abstract

The genomic organization of chromatin is increasingly recognized as a key regulator of cell behavior, but deciphering its regulation mechanisms requires detailed knowledge of chromatin's primary structure-the assembly of nucleosomes throughout the genome. This Primer explains the principles for mapping and analyzing the primary organization of chromatin on a genomic scale. After introducing chromatin organization and its impact on gene regulation and human health, we then describe methods that detect nucleosome positioning and occupancy levels using chromatin immunoprecipitation in combination with deep sequencing (ChIP-Seq), a strategy that is now straightforward and cost efficient. We then explore current strategies for converting the sequence information into knowledge about chromatin, an exciting challenge for biologists and bioinformaticians.

Figure 1. Chromatin architecture

The primary structure of chromatin can be thought of as “beads-on-a-string” with uniformly-spaced arrays of nucleosomes at a fixed distance downstream of transcriptional start sites. With the exception of specific regulatory situations, intact nucleosomes generally avoid the core promoter region, where the transcription machinery assembles. These nucleosome-free regions provide an opportunity to regulate gene expression at steps beyond simple promoter access, for example through elongation control of RNA polymerase II (Core and Lis, 2008). The protein core of nucleosomes is composed of histones, which often contain posttranslational modifications on specific amino acids and can be replaced by transcription-linked histone variants (dark blue and purple. As depicted, chromatin also folds into more compact structures aided by certain histone modifications.

Figure 2. Nucleosomal properties measured by high-resolution mapping studies

A. Numerous properties of individual nucleosomes can be extracted from histone mapping studies, including the spacing between nucleosomes, the presence of histone variants and posttranslational modifications, nucleosome fuzziness, occupancy, and position relative to a genomic feature. Fuzziness is the degree to which a nucleosome deviates from its consensus position in a population measurement. Occupancy is a measure of nucleosome density. B. Even a small change in a nucleosome’s location can alter a sequence’s accessibility to regulatory proteins. This schematic illustrates the rotational accessibility and inaccessibility of the DNA major groove on the surface of the core histone complex.

Figure 3. Flow chart for nucleosome preparation, mapping, and analysis

Images exemplify or illustrate the type of material or data at each stage. Steps 5 and 6 may be performed in either order.

Figure 4. How multiplexing can influence tag production

Left: The standard practice in ChIP-Seq is to index or “barcode“ each genomic sample of nucleosomal DNA with a unique DNA sequence of 6–10 nucleotides. The barcode is ultimately sequenced and used to associate each tag with the sample from which it came, when many different samples are pooled together. One option is to PCR amplify each DNA sample, then pool equal mass proportions to generate equal number of tags for each nucleosomal sample. The problem with this approach is that a sample that might be expected to have a very low tag count, such as a negative control lacking an antibody or epitope, will yield approximately the same number of tags as real test samples. This could give the erroneous impression of high background in the test samples. Right: An alternatively strategy is to pool samples prior to PCR amplification, based upon mixing equivalent numbers of cells (or some other metric of equivalency between samples). Then, the proportionality of tags between samples will, to a first approximation, remain constant. The problem with this approach is that any loss of sample or excess DNA contamination in a sample at any stage prior to pooling (including cell harvesting, chromatin-immunoprecipitation, or library construction) will carry through to the end. As a result, tags from different test samples, which might be expected to have similar tag counts, could vary widely. Thus, the risk is that some samples may not yield enough tag counts to conduct the appropriate analysis.

Figure 5. Turning tags into nucleosomes

Solid red and blue lines represent nucleosomal DNA ready for sequencing. The nucleosomal DNA is produced as MNase-resistant DNA fragments. In a population of molecules, the DNA fragments will have heterogeneous ends due to biases in digestion efficiency at different sequences, as well as the nucleosome not residing at a single position (i.e. “fuzzy” positioning). The asterisk, representing a unique sequence, provides a frame of reference. A. In “fragment” or “single-read sequencing,” the DNA library is sequenced from only one of the adapters (green) (except in reading the barcode) and in the direction indicated by the blue or red arrows. Consequently, each nucleosome border is measured independently as a population. Tags can then be extended to 147 nucleotides or their 5’ ends shifted by 73 nucleotides, as indicated to the right. Either way, the resulting frequency distributions, while looking different, have exactly the same uncertainty. B. Paired-end sequencing allows both ends of the same DNA molecule to be sequenced. The midpoint of the pair defines the consensus nucleosome midpoint, which can be extended 73 nucleotides in both directions (right side).

Figure 6. Data normalization

This schematic depicts an immunoblot measuring the bulk levels of chromatin-associated histone H3 and a particular histone modification. The corresponding immunoblot signals in a reference sample are set to 100. Assuming total levels of H3 are constant between samples, the relative level of each modification can be assessed. Nucleosomal tags generated from MNase ChIP-Seq can then be proportionally adjusted to reflect the relative modification state. This does not preclude further normalization on a locus-by-locus basis in which modification densities are calculated for a given amount of core histone (typically, H3) present in the sample.