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 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.