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. 2015 Jan 15;72:57-64.
doi: 10.1016/j.ymeth.2014.10.033. Epub 2014 Nov 6.

Building the Connectivity Map of Epigenetics: Chromatin Profiling by Quantitative Targeted Mass Spectrometry

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Free PMC article

Building the Connectivity Map of Epigenetics: Chromatin Profiling by Quantitative Targeted Mass Spectrometry

Amanda L Creech et al. Methods. .
Free PMC article

Abstract

Epigenetic control of genome function is an important regulatory mechanism in diverse processes such as lineage commitment and environmental sensing, and in disease etiologies ranging from neuropsychiatric disorders to cancer. Here we report a robust, high-throughput targeted, quantitative mass spectrometry (MS) method to rapidly profile modifications of the core histones of chromatin that compose the epigenetic landscape, enabling comparisons among cells with differing genetic backgrounds, genomic perturbations, and drug treatments.

Keywords: Chromatin; Epigenetics; Histones; Mass spectrometry; Proteomics; SILAC.

Figures

Fig. 1
Fig. 1
Global chromatin profiling targeted proteomics workflow and quality control. (A) Process flow diagram, illustrating points of entry for internal standards and automated steps. In panels (B–E), we follow a peptide corresponding to H3K27me2K36me1 from data acquisition through final ratio determination in a set of samples. Data shown is derived from samples analyzed in Fig. 2A. (B) A single raw MS/MS spectrum targeting a peptide precursor of m/z 548.6579, corresponding to the z = 3 state of the fully derivatized endogenous peptide. (C) Extracted ion chromatograms of MS/MS fragment ions (transitions) for both the endogenous peptide and the internal standard for a single sample in the set (extracted using Skyline). (D) Retention time view of all samples in the set for both the endogenous peptides (red) and internal standards (blue). Consistency illustrates the reproducibility of the chromatographic dimension of the assay. (E) Calculated ion abundances for both the endogenous peptides (red) and internal standards (blue) of all samples in the set, as derived from areas under the curve in (C). Ratios are determined by dividing the ion abundances of the endogenous peptides (red) by the internal standards (blue).
Fig. 2
Fig. 2
Connectivity of cellular perturbations and states through chromatin signatures. Several use cases of molecular chromatin signature data are illustrated. (A) Connectivity of perturbations of gene expression by shRNAs. Genes knocked down and biological replicates are indicated across the top. Perturbations were hierarchically clustered using a Euclidian distance metric. (B) Calculation of site occupancy is possible with synthetic peptide standards. H3K9 site occupancy is calculated for selected shRNA knockdowns depicted in (A). (C) Conflation of shRNA knockdown profiles (from A) with profiles from cells of differing genetic backgrounds (from Ref. [2]) for a subset of H3 marks. This approach allows classification of genetic mutants by comparing with reference gold-standard perturbations. Samples were clustered using a Spearman rank-order metric. (D) Connectivity of knockdowns, knockouts, and drug treatments of cells in murine stem cells. The approach allows facile comparison of multiple modes of perturbations. Samples clustered by Euclidean distance.

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