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Histone Modification

Histone Modification

The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
The enzyme histone acetyltransferase adds acetyl group to the histones. Another enzyme, histone...

Chromatin Immunoprecipitation- ChIP

Chromatin Immunoprecipitation- ChIP

Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...

Chromatin Modification in iPS Cells

Chromatin Modification in iPS Cells

Chromatin modification alters gene expression; therefore, scientists can add histone-modifying enzymes, histone variants, and chromatin remodeling complexes to somatic cells to aid reprogramming into pluripotent stem (iPS) cells.
Compact chromatin makes reprogramming difficult. Enzymes, such as histone demethylases and acetyltransferases, are often added during reprogramming to loosen the chromatin, making the DNA more accessible to transcription factors. Molecules that inhibit histone...

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Related Experiment Video

Updated: Jan 7, 2026

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Published on: December 19, 2025

A computational framework to dissect imputation strategies for single-cell histone modification data.

Marta Moreno-González^1,2, Jeroen de Ridder³, Jop Kind^1,2

¹Oncode Institute, Hubrecht Institute-KNAW and University Medical Center Utrecht, 3584 CT, Utrecht, The Netherlands.

NAR Genomics and Bioinformatics

|December 31, 2025

Summary

This summary is machine-generated.

This study benchmarks computational imputation methods for single-cell histone post-translational modification (scHPTM) data. Findings show method performance varies by task, guiding future algorithm development in single-cell epigenomics.

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Last Updated: Jan 7, 2026

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Published on: December 19, 2025

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Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Area of Science:

Epigenetics and Genomics
Computational Biology
Single-Cell Analysis

Background:

Single-cell profiling of histone post-translational modifications (scHPTMs) is crucial for understanding epigenetic regulation and cell identity.
scHPTM datasets face analytical challenges due to low read depth and inherent noise.

Purpose of the Study:

To introduce the first comprehensive computational framework for evaluating imputation strategies on scHPTM data.
To assess imputation methods originally developed for single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) on scHPTM data.

Main Methods:

Developed a modular R package implementing novel performance metrics.
Evaluated imputation strategies using both synthetic and published scHPTM datasets.
Assessed signal recovery, enrichment at genomic sites, and preservation of cell-to-cell similarities.

Main Results:

No single imputation method is optimal for all analytical tasks in scHPTM data.
Performance varied significantly across tasks such as signal denoising, peak detection, and clustering.
Identified strengths and limitations of current imputation approaches for scHPTM data.

Conclusions:

The study provides critical guidance for researchers and developers in single-cell epigenomics.
Lays the foundation for developing next-generation, task-aware imputation algorithms for scHPTM data.
Highlights unmet needs and current capabilities in computational analysis of scHPTM datasets.