Back

Deaminase-assisted single-molecule and single-cell chromatin fiber sequencing

Swanson, E. G.; Mao, Y.; Mallory, B. J.; Vollger, M. R.; Ranchalis, J.; Bohaczuk, S. C.; Parmalee, N. L.; Bennett, J. T.; Stergachis, A. B.

2024-11-06 genomics
10.1101/2024.11.06.622310 bioRxiv
Show abstract

Gene regulation is mediated by the co-occupancy of numerous proteins along individual chromatin fibers. However, our tools for deeply profiling how proteins co-occupy individual fibers, especially at the single-cell level, remain limited. We present Deaminase-Assisted single-molecule chromatin Fiber sequencing (DAF-seq), which leverages a non-specific double-stranded DNA deaminase toxin A (SsDddA) to efficiently stencil protein occupancy along DNA molecules via selective deamination of accessible cytidines, which are preserved via C-to-T transitions upon DNA amplification. We demonstrate that DAF-seq enables [~]200,000-fold enrichment of target loci for single-molecule footprinting at near single-nucleotide resolution, enabling the precise delineation of the regulatory logic guiding neighboring proteins to cooperatively occupy chromatin fibers. Furthermore, DAF-seq enables the synchronous identification of single-molecule chromatin and genetic architectures - resolving the functional impact of rare somatic variants, as well as transitional chromatin states guiding haplotype-selective promoter actuation. Finally, we demonstrate that single-cell DAF-seq enables the accurate reconstruction of the diploid genome and epigenome from a single cell, revealing that a cells accessible regulatory landscape can diverge by as much as 63% while still retaining the cells identity. Overall, DAF-seq enables the comprehensive characterization of protein occupancy and chromatin accessibility across entire chromosomes with single-nucleotide, single-molecule, single-haplotype, and single-cell precision.

Published in Nature Biotechnology (predicted rank #4) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.