What type of spike-in strategy do you recommend for CUT&RUN?

There are three spike-in strategies available for the CUT&RUN assay. We recommend either the Drosophila spike-in strategy or the yeast spike-in DNA strategy, as both enable reliable and consistent normalization across samples and help account for technical variation during processing. Detailed instructions for implementing spike-in controls for both qPCR and NGS are provided in our CUT&RUN Assay Kit #86652 and CUT&RUN Assay Kit (with Drosophila Spike-In Control) #84647.

With the Drosophila spike-in strategy, Drosophila nuclei are added concurrently with the test cells at the start of the CUT&RUN workflow, ensuring they experience all the same steps and conditions as the test cells. The H2Av Rabbit Monoclonal Antibody, which specifically recognizes Drosophila chromatin, is included along with the test antibody. A normalization factor is then generated from the Drosophila enrichment signal and applied to the test genome to normalize signal across samples. While this approach normalizes all steps throughout the entire protocol, it is an added cost over the CUT&RUN Assay Kit #86652, which includes a yeast spike-in DNA.
With the yeast spike-in DNA strategy, a defined amount of pre-fragmented yeast genomic DNA (100–600 bp, peaking at ~250 bp) is added to each reaction after digestion and before DNA purification. This allows normalization for variation in DNA purification, qPCR, library preparation, and sequencing efficiency. Although this strategy only normalizes the second half of the workflow, it is simpler to perform and more economical. Note that the required yeast spike-in DNA amount differs substantially between qPCR and NGS applications, so please review the protocol carefully.
Some protocols rely on contaminating E. coli DNA present in pAG-MNase for normalization, but this can lead to inconsistencies when switching enzyme lots, as the amount of contaminating DNA varies. The Drosophila and yeast spike-in strategies provide better lot-to-lot consistency and more reliable normalization.