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FACT-seq: profiling histone modifications in formalin-fixed paraffin-embedded samples with low cell numbers.

ABSTRACT: The majority of biopsies in both basic research and translational cancer studies are preserved in the format of archived formalin-fixed paraffin-embedded (FFPE) samples. Profiling histone modifications in archived FFPE tissues is critically important to understand gene regulation in human disease. The required input for current genome-wide histone modification profiling studies from FFPE samples is either 10-20 tissue sections or whole tissue blocks, which prevents better resolved analyses. But it is desirable to consume a minimal amount of FFPE tissue sections in the analysis as clinical tissues of interest are limited. Here, we present FFPE tissue with antibody-guided chromatin tagmentation with sequencing (FACT-seq), the first highly sensitive method to efficiently profile histone modifications in FFPE tissues by combining a novel fusion protein of hyperactive Tn5 transposase and protein A (T7-pA-Tn5) transposition and T7 in vitro transcription. FACT-seq generates high-quality chromatin profiles from different histone modifications with low number of FFPE nuclei. We proved a very small piece of FFPE tissue section containing ∼4000 nuclei is sufficient to decode H3K27ac modifications with FACT-seq. H3K27ac FACT-seq revealed disease-specific super enhancers in the archived FFPE human colorectal and human glioblastoma cancer tissue. In summary, FACT-seq allows decoding the histone modifications in archival FFPE tissues with high sensitivity and help researchers to better understand epigenetic regulation in cancer and human disease.

SUBMITTER: Zhao L

PROVIDER: S-EPMC8643707 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most widely available biological sample format in medical research and patient care. Normalization is an essential step in RNA-seq data analysis. A number of normalization methods, though developed for RNA-seq data from fresh frozen (FF) samples, can be used with FFPE samples as well. The only extant normalization method specifically designed for FFPE RNA-seq data, MIXnorm, which has been shown to outperform the normalization methods, but at the cost of a complex mixture model and a high computational burden. It is therefore important to adapt MIXnorm for simplicity and computational efficiency while maintaining superior performance. Furthermore, it is critical to develop an integrated tool that performs commonly used normalization methods for both FF and FFPE RNA-seq data. We developed a new normalization method for FFPE RNA-seq data, named SMIXnorm, based on a simplified two-component mixture model compared to MIXnorm to facilitate computation. The expression levels of expressed genes are modeled by normal distributions without truncation, and those of non-expressed genes are modeled by zero-inflated Poisson distributions. The maximum likelihood estimates of the model parameters are obtained by a nested Expectation-Maximization algorithm with a less complicated latent variable structure, and closed-form updates are available within each iteration. Real data applications and simulation studies show that SMIXnorm greatly reduces computing time compared to MIXnorm, without sacrificing the performance. More importantly, we developed a web-based tool, RNA-seq Normalization (RSeqNorm), that offers a simple workflow to compute normalized RNA-seq data for both FFPE and FF samples. It includes SMIXnorm and MIXnorm for FFPE RNA-seq data, together with five commonly used normalization methods for FF RNA-seq data. Users can easily upload a raw RNA-seq count matrix and select one of the seven normalization methods to produce a downloadable normalized expression matrix for any downstream analysis. The R package is available at https://github.com/S-YIN/RSEQNORM. The web-based tool, RSeqNorm is available at http://lce.biohpc.swmed.edu/rseqnorm with no restriction to use or redistribute.

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FACT-seq: profiling histone modifications in formalin-fixed paraffin-embedded samples with low cell numbers.

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