Mapping genome-wide transcription factor binding sites in frozen tissues

Abstract

Background: Genome-wide maps of transcription factor binding sites in primary tissues can expand our understanding of genome function, transcriptional regulation, and genetic alterations that contribute to disease risk. However, almost all genome-wide studies of transcription factors have been in cell lines, and performing these experiments in tissues has been technically challenging and limited in throughput.

Results: Here we outline a simple strategy for mapping transcription factor binding sites in frozen tissues that utilizes dry pulverization of samples and is scalable for high-throughput analyses. We show that the method leads to accurate and reproducible chromatin immunoprecipitation next-generation sequencing (ChIP-seq) data, and is highly sensitive, identifying high-quality transcription factor binding sites from chromatin corresponding to only 5 mg of liver tissue.

Conclusions: The enhanced reproducibility, robustness, and sensitivity of the dry pulverization method, in addition to the ease of implementation and scalability, makes ChIP-seq in primary tissues a widely accessible assay.

Figure 1

Dry pulverization method overview. Tissue is placed in Covaris tissueTUBES with adapters and attached glass vials (step 1). To keep samples both cold and brittle to facilitate pulverization, tissues are briefly submerged in liquid nitrogen (step 2) between successive rounds of pulverization (step 3). Following the pulverization of samples to a powder, the tissueTUBEs are inverted and tissue powder is collected into attached Covaris glass vials (step 4). Tissue powder is fixed, washed and stored as a pellet at −80°C.

Figure 2

ChIP-seq in murine liver. (A) ChIP raw sequencing read enrichments for RNA polymerase II (Rnap2), Retinoid X receptor α (Rxrα), CCAAT/enhancer-binding protein α (Cebpα), and the CCCTC-binding factor (Ctcf) at an apolipoprotein cluster on mouse chromosome 7. (B) Images of the canonical motifs identified by multiple expectation maximization for motif elicitation (MEME) in liver. (C) Venn diagrams illustrate the degree of shared binding sites between liver biological replicates.

Figure 3

ChIP-seq analyses across diverse murine tissues. (A) Percentage of Rxrα binding sites shared with Rnap2 (blue), Ctcf (red) or both Rnap2 and Ctcf (purple) in liver (L), brain (B), small intestine (I) and skeletal muscle (M). Rxrα binding sites that do not colocalize with Rnap2 and Ctcf are shown in yellow. (B) Analysis of shared binding sites for Rnap2, Rxrα, and Ctcf between all pairwise tissue comparisons. The two tissues utilized for each comparison are given on the x axis. Shared binding sites are shown in black while tissue-specific sites are in green. (C) ChIP-seq raw sequencing read enrichments for Rnap2 at distinct genes illustrate tissue specificity of gene expression. Gene names and window sizes are given above. (D) Canonical motif genomic evolutionary rate profiling (GERP) scores at tissue-specific binding sites (dark red) and binding sites shared by two (red) or more (orange) tissues. The corresponding motif sequence is shown above the graph. GERP scores are significantly higher within bound Rxrα motifs relative to positions that are not within a motif but are within 250 bp of a binding site summit (P < 2.2 × 10^-16, one-sided t test); further, there is a highly significant correlation between GERP score and position-specific motif dependencies on a particular nucleotide, with less degenerate positions being more highly conserved (P < 2.2 × 10^-16, simple linear regression between GERP scores and the maximum individual nucleotide score at each position in the Rxrα motif position-specific weight matrix).

Figure 4

Correlation matrix between ChIP-seq experiments. Heat map displaying Spearman rank correlations between all pairwise comparisons for all tissues and ChIPs. Spearman correlations were calculated using the normalized read depth across the entire set of binding sites identified for all ChIP-seq experiments. RNA polymerase II, Pol2; CCCTC-binding factor, Ctcf; Retinoid X receptor α; Rxrα; CCAAT/enhancer-binding protein α, CEBPα.

Figure 5

ChIP-seq liver titration. (A) Images of the canonical Rxrα motif identified by multiple expectation maximization for motif elicitation (MEME) for all tissue inputs. (B) Spearman rank correlations between pairwise comparisons using one replicate from each liver input amount. The upper-right section of the diagram gives the smoothed scatter plot data while the lower-left section displays the rank correlation values, with the font size corresponding to the strength of correlation. (C) Bar graph displays the total number of binding sites (y axis) shared between biological replicates for each tissue input amount (x axis).