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Simple Summary  Formalin-fixed paraffin-embedded (FFPE) specimens, which are pathological specimens of human tissues, are of high clinical value because they are associated with clinical information such as drug sensitivity and side effects and exist in huge numbers worldwide. However, the quality of DNA and RNA extracted from FFPE specimens is generally poor, and it is still difficult to perform ChIP-seq. Here, we describe an experimental procedure for FFPE ChIP-seq called RCRA ChIP-seq that allows identification of the genome-wide distributions of key histone modifications and binding sites of the insulator transcription factor CTCF. We have also succeeded in obtaining accurate and stable results even for the analysis of a large number of FFPE samples by using an industrial robot. Thus, routine ChIP-seq analysis of FFPE specimens could lead to new epigenomic mechanisms in various diseases.    Abstract  Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data.

Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential