Genomics & Bioinformatics Case Study

Developing a Better Chromatin Mapping Technique


Many cellular processes, including transcription and DNA replication, critically depend on chromatin structure, the three-dimensional organization of DNA that includes nucleosome positioning and specific post-translational modifications of histone proteins. The ability to map these features in different cell types and biological contexts can provide powerful insight into gene regulation, disease progression and potential therapeutic interventions.

The Challenge

Chromatin immunoprecipitation sequencing, or ChIP-seq, has long been the dominant method for mapping chromatin features. However, the standard ChIP-seq assay requires large amounts of material and provides only modest resolution, with low signal and high background that necessitate increased sequencing depth and cost. ChIP-seq may also lead to an incomplete survey due to the effects of cross-linking and protein solubility.

Dr. Steven Henikoff’s group at Fred Hutchinson Cancer Research Center developed a new method called Cleavage Under Targets and Release Using Nuclease, or CUT&RUN, to provide researchers with a chromatin-mapping technique that offers better resolution and higher signal from less starting material. CUT&RUN employs antibody-targeted controlled cleavage by a micrococcal nuclease to release specific protein-DNA complexes for paired-end DNA sequencing. The CUT&RUN approach is simple to perform and, due to its lower background, requires far less sequencing depth.

To develop CUT&RUN, the Henikoff Lab turned to the Genomics & Bioinformatics shared resource for help with rapid in-house sequencing, quality-control of numerous Illumina flow cells, and sequence alignment strategies. 

The Approach

The Genomics & Bioinformatics shared resource team worked with Henikoff’s group to provide a cost-effective development cycle, including customized sequencing read-lengths, extremely fast turn-around times, and data processing support that facilitated rapid feedback during the development and testing phase of this project. Additionally, the core supported efforts to automate CUT&RUN in a 96-well format for high-throughput processing, now offered as the AutoCUT&RUN service by the core’s genomics team.

The core’s bioinformatics team recommended data processing strategies during the development of CUT&RUN and provided customized sample demultiplexing for the related CUT&Tag method (Cleavage Under Targets and Tagmentation), which the Henikoff Lab developed to profile low-cell (or even single-cell) inputs.

Schematic of the CUT&RUN process.
Schematic of the CUT&RUN process for a transcription factor (TF). Cell nuclei are treated with a feature-specific antibody and tethered micrococcal nuclease (MNase) enzyme. On activation, MNase cleaves and releases chromatin fragments into the supernatant where this DNA is used to prepare libraries for paired-end sequencing. Schematic from Skene and Henikoff (2017)

The Outcome

There has been dramatic and rapid adoption of the CUT&RUN technique since its introduction. The original 2017 eLife paper has been cited over 250 times as of October 2020, and the Henikoff Lab has distributed materials to over 600 labs worldwide. The CUT&RUN protocol page on protocols.io has been viewed over 100,000 times and maintains an active community discussing applications of this transformative technique.

Read More

Read more about CUT&RUN and CUT&Tag in the team’s papers and related coverage:

Skene PJ and Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife. 2017;6:e21856. doi:10.7554/eLife.21856

Janssens DH, Wu SJ, Sarthy JF, et al. Automated in situ chromatin profiling efficiently resolves cell types and gene regulatory programs. Epigenetics Chromatin. 2018;11(1):74. doi:10.1186/s13072-018-0243-8

“The Genomics & Bioinformatics shared resource has been crucial for the success of CUT&RUN, both by providing outstanding DNA sequencing and bioinformatics support and by working closely with my group to extend the method for automation and single-cell profiling.”

– Dr. Steven Henikoff, Professor, Basic Sciences Division, Fred Hutch