Sensitive, direct detection of non-coding off-target base editor unwinding and editing in primary cells [new]
Direct seq. quantifies off-target base editor & DNA unwinding.

Sensitive, direct detection of non-coding off-target base editor unwinding and editing in primary cells https://www.biorxiv.org/content/10.1101/2025.09.25.678665v1

8/ This was a group effort drawing expertise from different parts of the Greenleaf Lab and the larger Stanford Genetics community. A big thank you to @selinjessa.bsky.social, Georgi, Sandy, and @anshulkundaje.bsky.social I am especially grateful to Will for encouraging me to take on this project.

7/ There’s much more in the preprint (deep learning of novel T-cell non-coding off-targets, SpRY editors, and comparisons to other methods to name a few), but I won’t give it all away here. Base editors hold immense promise, and we hope our insights help to improve them.

6/ We also show how deep learning can predict the effects of non-coding edits. Here, an erythroid but not T-cell model predicts loss of DNA accessibility upon editing. The model learns the GATA motif driving this prediction. This site is of course the intended target of Casgevy.

5/ We extensively optimized beCasKAS to be compatible with primary human T cells and 5moU mRNA delivery. Transient mRNA delivery limits off-target formation, which can be further mitigated by carefully selecting mRNA dose.

4/ We are able to see the known strand-specific editing patterns of two contrasting editors: eBE (using hAPOBEC3A) and ABE8e. Using plasmid delivery in HEK293Ts, we find these two editors have surprisingly similar absolute editing frequencies.

3/ beCasKAS makes two measurements at each off-target site. The unwound R-loop appears as a peak, and individual edits appear as mismatched nucleotides.

2/ We use an N3-kethoxal pulldown probe because it has the same selectivity for ssDNA as the deaminases used in base editing. The cell permeable kethoxal allows us to enrich for unwound Cas9 R-loops that must occur before DNA editing. This selectivity underlies our sensitivity.

1/ Happy to share our preprint from the Greenleaf Lab: beCasKAS, our method to directly detect CRISPR base editor off-targets in primary cells. We additionally show how non-coding edits can be triaged for epigenetic dysregulation using deep learning.

www.biorxiv.org/content/10.1...

Delighted to share our latest work deciphering the landscape of chromatin accessibility and modeling the DNA sequence syntax rules underlying gene regulation during human fetal development! www.biorxiv.org/content/10.1... Read on for more: 🧵 1/16 #GeneReg 🧬🖥️

www.biorxiv.org/content/10.1...
Our study published today on #bioRxiv describe the identification of a deaminase that converts 5mC to T, enabling direct sequencing of the human methylome and genome. This achievement was made possible through a collaborative effort across all departments at #NEB.

(1st post @BlueSky) Preprint alert🚨a long thread. Cautions in the use of @nanopore sequencing to map DNA modifications: officially reported “accuracy” ≠ reliable mapping in real applications. We performed a critical assessment of nanopore sequencing (across different versions of models) for the 1/n

who knew it would be easier to get everyone to switch social media platforms than getting them to use hg38