Gene regulatory networks articles from across Nature Portfolio

The authors give a method for learning causal gene networks using Perturb-seq data. In K562 cells, they find a network with small-world and scale-free properties. Analysis shows a relationship between gene centrality, essentiality and heritability.

PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.

Watt, Dauber, Szkop and colleagues find that H3K4me3 remodels 5′UTR selection in hypoxia and that this process is independent of HIF-1 transcriptional mechanisms.

After more than two decades of large-scale efforts to annotate the regulatory genome, Sushant Kumar and Mark Gerstein forecast how new technologies and experimental approaches will pave the way in mapping regulatory elements across cell types, developmental stages and genetically diverse individuals.

We created T-CellAnnoTator (TCAT), a computational method that helps to identify T cell subsets, activation states and functions. It does this using reproducible gene expression programs found across many disease contexts and tissues. TCAT outperforms conventional approaches for T cell subset prediction, is easy to use programmatically or through a website, and can be adapted for other cell types.

The discovery of plaque tertiary lymphoid organs (PTLOs) within human atherosclerotic plaques provides insights into the role of the immune system in atherosclerosis. The findings highlight the cellular and molecular features of PTLOs, and suggest a potential role of PTLOs in driving plaque instability by activating adaptive immune responses.

Despite only one chromosome differing between men and women, sex makes an enormous difference in clinical cardiovascular diseases. Recent multi-omic data expand our understanding of the underlying mechanisms that may contribute to this disparity in humans.

In this Tools of the Trade article, Yan Hu explains the development and application of PRINT and seq2PRINT, computational tools that together use single-cell ATAC-seq data to predict protein binding dynamics at detailed cellular resolution.