<< Back to MOTIFvations Blog Home Page

CUT&Tag and CUT&RUN: Redefining the Standards for Protein–DNA Mapping in Epigenetics (Epigenetics Podcast Insights - Part 2)

By Stefan Dillinger, Ph.D.

May 13, 2026

Tweet

Introduction

Understanding how proteins interact with DNA is fundamental to decoding the regulatory logic of the genome. These interactions govern gene expression, chromatin structure, and ultimately, cell identity and function. For decades, chromatin immunoprecipitation followed by sequencing (ChIP-seq) has been the gold standard for mapping protein–DNA interactions. However, ChIP-seq comes with significant limitations as it requires large amounts of input material, involves harsh crosslinking and sonication steps, and often suffers from high background noise.

In recent years, two innovative techniques, CUT&RUN (Cleavage Under Targets and Release Using Nuclease) and CUT&Tag (Cleavage Under Targets and Tagmentation), have emerged as powerful alternatives. Developed by Steven Henikoff and refined by pioneers in the field, these methods offer higher resolution, lower background, and compatibility with low-input and even single-cell samples. Together, they are reshaping the landscape of epigenomic profiling.

 Back to Table of Contents

From ChIP-seq to CUT&RUN and CUT&Tag: A Paradigm Shift

ChIP-seq revolutionized epigenetics by enabling genome-wide mapping of transcription factors and histone modifications. Yet, its reliance on crosslinking, chromatin fragmentation, and immunoprecipitation introduces variability and technical noise. Moreover, ChIP-seq typically requires millions of cells, making it unsuitable for rare populations or clinical samples.

To overcome these challenges, Steven Henikoff and colleagues introduced CUT&RUN in 2017. This method uses a fusion of protein A (or A/G) with micrococcal nuclease (MNase) to cleave DNA at antibody-bound protein sites in situ. The resulting fragments are released into solution a library is prepared and subsequently sequenced, yielding high-resolution maps with minimal background. Crucially, CUT&RUN works under native conditions and requires as few as 50.000-100.000 cells. Some labs have shown that it can also be used with significantly less than that.

Building on this innovation, Henikoff’s lab later developed CUT&Tag, which replaces MNase with a hyperactive Tn5 transposase loaded with sequencing adapters. This allows simultaneous cleavage and tagging of DNA at protein binding sites, streamlining library preparation and further reducing input requirements. CUT&Tag is particularly well-suited for profiling histone modifications and has been adapted for automation and single-cell applications.

These methods not only improve data quality but also democratize epigenomic research by making it feasible with limited samples and standard lab equipment.

 Back to Table of Contents

Technical Innovations and Optimization

While CUT&RUN and CUT&Tag offer clear advantages, their implementation requires careful optimization, especially for challenging targets like non-DNA-binding proteins or large transcriptional complexes.

Dr. Claudio Cantu’s lab at Linköping University tackled this head-on with their development of CUT&RUN-LovU (Low Volume and Urea). In their work on β-catenin, a transcriptional co-activator that does not directly bind DNA, they found that standard CUT&RUN protocols failed to release DNA fragments effectively. The breakthrough came when they introduced an in situ protein denaturation step using urea, which liberated DNA trapped within large protein complexes. This modification significantly improved signal yield and peak detection, especially for difficult targets.

Meanwhile, Dr. Sarah Marzi’s group at King’s College London applied CUT&Tag to study histone acetylation in Alzheimer’s disease. Working with limited brain samples, they optimized antibody concentrations, PCR cycles, and DNA extraction methods. They also benchmarked CUT&Tag against ENCODE ChIP-seq datasets, revealing that while peak overlap was not perfect (~50–60%), the strongest and most biologically relevant peaks were consistently detected by both methods. Their work highlighted the importance of peak caller selection and parameter tuning, especially for acetylation marks, which tend to produce noisier signals than methylation marks.

Henikoff’s lab further advanced the field by automating CUT&Tag with robotic platforms (AutoCUT&Tag) and adapting it for clinical samples. They also introduced Pol2-CUTAC, a variant that maps accessible chromatin using RNA polymerase II as a proxy, offering an alternative to ATAC-seq with improved specificity. Together, these innovations underscore the flexibility and adaptability of CUT&RUN and CUT&Tag, making them indispensable tools for modern epigenetics.

 Back to Table of Contents

Reproducibility and the Iceberg Problem

One of the most persistent challenges in epigenomic profiling is reproducibility. Even with optimized protocols, researchers often observe that only a fraction of peaks overlap between biological replicates. This raises a fundamental question: how many of the non-overlapping peaks are false positives, and how many are real but missed due to technical or biological variability?

Dr. Claudio Cantu’s lab addressed this issue with a novel conceptual and computational framework called ICEBERG (Increased Capture of Enrichment by Exhaustive Replicate Aggregation). The idea is simple but powerful: by aggregating data from many replicates (up to 25 in their study), one can distinguish true biological signals from noise. Peaks that appear in only one, or a few replicates may still be real, especially if they are consistently detected across experiments.

Cantu’s team introduced the term peak concordance to describe the frequency with which a peak is observed across replicates. They argue that the traditional binary classification of peaks (present or absent) is too simplistic. Instead, concordance should be viewed as a probability distribution, some binding events are strong and ubiquitous, while others are rare or transient but still biologically meaningful.

This approach challenges the field’s long-standing preference for minimizing false positives at the expense of false negatives. As Cantu puts it, “We are okay with false negatives, but not okay with false positives.” ICEBERG offers a more nuanced view, allowing researchers to recover the “submerged” portion of the regulatory landscape.

 Back to Table of Contents

Applications in Neuroscience and Disease

The power of CUT&Tag and CUT&RUN is especially evident in fields like neuroscience, where sample availability is limited and cellular heterogeneity is high. Dr. Sarah Marzi’s work exemplifies this application. Her team used CUT&Tag to profile histone acetylation in postmortem brain tissue from Alzheimer’s disease patients. They discovered widespread dysregulation of H3K27ac, particularly in genes associated with Alzheimer’s risk.

Importantly, Marzi’s group demonstrated that CUT&Tag could detect epigenetic changes in specific brain cell types, such as oligodendrocytes, which are increasingly recognized as key players in neurodegeneration. By integrating CUT&Tag data with machine learning models and genome-wide association studies (GWAS), they uncovered novel regulatory networks and potential therapeutic targets.

Their work also highlighted the importance of benchmarking new methods against established datasets. While CUT&Tag did not perfectly replicate ENCODE ChIP-seq peaks, it consistently captured the most biologically relevant signals. This reinforces the idea that newer methods may offer complementary insights rather than direct replacements.

 Back to Table of Contents

Community Tools and Future Directions

As CUT&Tag and CUT&RUN gain traction, the research community is developing tools to support their adoption and standardization. One such tool is the CUT&RUN “suspect list” of problematic regions, proposed by Cantu’s lab. Modeled after the ENCODE blacklist for ChIP-seq, this list identifies genomic regions that frequently produce spurious signals in CUT&RUN experiments. By filtering out these regions, researchers can improve the specificity of their analyses.

Steven Henikoff’s lab has also played a key role in community engagement. They maintain detailed protocols on platforms like protocols.io and actively respond to user questions. Their commitment to open science has accelerated the dissemination and refinement of these methods.

Looking ahead, the future of protein–DNA mapping lies in single-cell and multimodal profiling. CUT&Tag has already been adapted for single-cell applications, enabling researchers to study chromatin landscapes at unprecedented resolution. Combining CUT&Tag with RNA-seq, ATAC-seq, or spatial transcriptomics could unlock new insights into cell fate, development, and disease.

Moreover, the simplicity and scalability of these methods make them attractive for clinical applications. As sequencing costs continue to fall, CUT&Tag could become a routine tool for diagnostics, biomarker discovery, and personalised medicine.

 Back to Table of Contents

References

1. Dillinger, S. (2021, April 15). Chromatin Profiling: From ChIP to CUT&RUN, CUT&Tag and CUTAC (Steven Henikoff). Epigenetics Podcast. https://www.activemotif.com/podcasts-steven-henikoff
2. Dillinger, S. (2024, April 18). Comparing CUT&Tag to ENCODE ChIP-Seq in Alzheimer’s Disease Samples (Sarah Marzi). Epigenetics Podcast. https://www.activemotif.com/podcasts-sarah-marzi
3. Dillinger, S. (2023, July 27). When is a Peak a Peak? (Claudio Cantù). Epigenetics Podcast. https://www.activemotif.com/podcasts-claudio-cantu
4. Abbasova, L., Urbanaviciute, P., Hu, D., Ismail, J. N., Schilder, B. M., Nott, A., Skene, N. G., & Marzi, S. J. (2025). CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks. Nature Communications, 16(1), 2993. https://doi.org/10.1038/s41467-025-58137-2
5. Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., Ahmad, K., & Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1), 1930. https://doi.org/10.1038/s41467-019-09982-5
6. Skene, P. J., & Henikoff, S. (2017). An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife, 6, e21856. https://doi.org/10.7554/eLife.21856
7. Zambanini, G., Nordin, A., Jonasson, M., Pagella, P., & Cantù, C. (2022). A new CUT&RUN low volume-urea (LoV-U) protocol optimized for transcriptional co-factors uncovers Wnt/β-catenin tissue-specific genomic targets. Development, 149(23), dev201124. https://doi.org/10.1242/dev.201124
8. Nordin, A., Zambanini, G., Pagella, P., & Cantù, C. (2023). The CUT&RUN suspect list of problematic regions of the genome. Genome Biology, 24(1), 185. https://doi.org/10.1186/s13059-023-03027-3
9. Nordin, A., Pagella, P., Zambanini, G., & Cantù, C. (2024). Exhaustive identification of genome-wide binding events of transcriptional regulators. Nucleic Acids Research, 52(7), e40–e40. https://doi.org/10.1093/nar/gkae180

 Back to Table of Contents

About the author

Stefan Dillinger, Ph.D.

Stefan was born in the Free State of Bavaria, Germany. After studying biochemistry in Ulm and Regensburg, he got his Ph.D. in the field of epigenetics, studying the distribution of heterochromatin around nucleoli during cellular senescence. As a graduate student he started his own German science podcast “The Random Scientist” and is now the host of Active Motif’s Epigenetics Podcast. When Stefan is not working at Active Motif or recording podcasts, he is a passionate runner (he finished the New York City Marathon in 3 hours 21 minutes!!) and loves to spend time with his wife and son.

Contact Stefan on LinkedIn with any questions, or to get running advice.

Related Articles

<< Back to MOTIFvations Blog Home Page