Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data

Zidong Zhang, Frederique Ruf-Zamojski, Michel Zamojski, Daniel J. Bernard, Xi Chen, Olga G. Troyanskaya, Stuart C. Sealfon

Research output: Contribution to journalArticlepeer-review

Abstract

Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin ‘peaks’. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.

Original languageEnglish (US)
Pages (from-to)572-582
Number of pages11
JournalNucleic acids research
Volume52
Issue number2
DOIs
StatePublished - Jan 25 2024

All Science Journal Classification (ASJC) codes

  • Genetics

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