CUT&Tag is a recent method to profile epigenetic marks with reduced cell numbers and cost, but how does it compare to ChIP-seq?

Benchmarking CUT&Tag against ChIP-seq: https://www.biorxiv.org/content/10.1101/2022.03.30.486382v1

https://www.nature.com/articles/s41467-019-09982-5

Lovely blog post on CUT&Tag: https://www.activemotif.com/blog-cut-tag

The improved sensitivity of CUT&Tag compared to ChIP-seq is due to the use of pA-Tn5 to streamline library preparation through direct insertion of PCR sequencing adapters via in situ tagmentation. However, its sensitivity is inherently limited by PCR, since pA-Tn5 inserts its adapters in random orientations such that approximately half of the targets do not have adapters in the correct orientation to amplify. In addition, PCR library preparation is highly sensitive to size variations of amplicons. When two adjacent transposition events occur too far apart, they will not amplify efficiently during PCR or sequencing cluster generation. However, when they are too close, they will exponentially bias library coverage due to increased PCR amplification and clustering efficiency of shorter fragments.

One recent approach that may help overcome some of these issues is linear amplification by Targeted Insertion of Promoters (TIP-seq): https://rupress.org/jcb/article/220/12/e202103078/212821/High-throughput-single-cell-epigenomic-profiling

In TIP-seq, a pA-Tn5 fusion protein is used to insert a T7 RNA polymerase promoter near sites occupied by transcription factor or histone mark of interest. The promoter facilitates linear amplification of DNA in its vicinity using a T7 polymerase to create 1,000-fold RNA copies of insertion sites. The distance between two transposition sites does not bias library preparation since only one T7 promoter is needed to amplify the site of interest. Linear amplification generates greater fidelity and uniformity, as mistakes made during amplification do not themselves become templates to exponentially propagate errors – this results in higher mappability of single cell sequencing reads. TIP-seq was shown to generate single cell libraries with higher read coverage, greater library complexity, and contain lower background with a higher proportion of unique, non-duplicated reads per cell compared to CUT&Tag. Comprehensive optimisation and benchmarking of this novel technique will be essential moving forward. Stay tuned from yours truly;)


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