Back

Single nucleus multi-omics regulatory atlas of the murine pituitary

Ruf-Zamojski, F. M.; Zhang, Z.; Zamojski, M.; Smith, G. R.; Mendelev, N.; Liu, H.; Nudelman, G.; Moriwaki, M.; Pincas, H.; Gomez Castanon, R.; Nair, V. D.; Seenarine, N.; Amper, M. A. S.; Zhou, X.; Ongaro, L.; Toufaily, C.; Schang, G.; Nery, J. R.; Bartlett, A.; Aldridge, A.; Jain, N.; Childs, G. V.; Troyanskaya, O. G.; Ecker, J. R.; Turgeon, J. L.; Welt, C. K.; Bernard, D. J.; Sealfon, S. C.

2020-06-07 cell biology
10.1101/2020.06.06.138024 bioRxiv
Show abstract

The pituitary regulates growth, reproduction and other endocrine systems. To investigate transcriptional network epigenetic mechanisms, we generated paired single nucleus (sn) transcriptome and chromatin accessibility profiles in single mouse pituitaries and genome-wide sn methylation datasets. Our analysis provided insight into cell type epigenetics, regulatory circuit and gene control mechanisms. Latent variable pathway analysis detected corresponding transcriptome and chromatin accessibility programs showing both inter-sexual and inter-individual variation. Multi-omics analysis of gene regulatory networks identified cell type-specific regulons whose composition and function were shaped by the promoter accessibility state of target genes. Co-accessibility analysis comprehensively identified putative cis-regulatory regions, including a domain 17kb upstream of Fshb that overlapped the fertility-linked rs11031006 human polymorphism. In vitro CRISPR-deletion at this locus increased Fshb levels, supporting this domains inferred regulatory role. The sn pituitary multi-omics atlas (snpituitaryatlas.princeton.edu) is a public resource for elucidating cell type-specific gene regulatory mechanisms and principles of transcription circuit control.

Matching journals

The top 3 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.