PCP is a versatile method that captures multiple levels of chromatin organization at the single-molecule level all in one experiment!
Soon to be out - Check the updated BioRxiv!

11/11
www.biorxiv.org/content/10.1...

All reads are pair-end sequenced, measuring precise footprint of chromatin components. We identified abundant Overlapping Di-Nucleosomes (OLDN) at a subset of divergent promoters. This is supported by a single-molecule analysis showing that adjacent nucleosomes and OLDN are mutually exclusive.
10/11

Something surprising!

Not all fuzziness is due to transcription.
Rather, the genomic distance between two consecutive NFRs (Nuc Free Region) predict fuzziness. If the size is a multiple of the optimal Nucleosome Repeat Length (NRL) => Positioned Array. If not => Delocalized Array.

9/11

On very highly expressed genes, long arrays disappear, and nucleosomes typically exist in pairs. Shutting down transcription resolves the fuzziness on some genes, re-establishing nucleosomes arrays.

8/11

On short range interaction, PCP will tag nucs from the same fiber with the same UMI. This reveals that “fuzzy” nucs in classical MNase-seq profiles correspond to well-spaced nucleosome arrays slightly offset from cell-to-cell.

7/11

Interaction matrix sometimes show signal between three consecutive cohesin peaks, suggesting multiway interaction. But Micro-C can only detect pairwise interactions. With PCP we show that cohesin tends to form hubs of multiway interactions.

6/11

We can bin the results in classical interaction matrices. On S. cerevisiae, PCP perform as well as Micro-C (i.e. detecting Cohesin loops in G2). At high-resolution PCP performs better and provide a more detailed picture of nucleosome interactions.

5/11