Cellular signalling networks articles from across Nature Portfolio

The physics-informed coevolution-learning algorithm-based framework CCCvelo infers the spatio-temporal dynamics of cell-state transitions driven by both intercellular communication and intracellular transcriptional regulation. CCCvelo is able to decode cell–cell communication dynamics as well as to process image-based spatial transcriptomics data.

Using DINOv2-derived batch-corrected phenotypic embeddings from 112k compounds, Phenoseeker enables structure-independent compound selection and systematic discovery of activity cliffs and pathway-consistent phenotypic convergence.

This Article introduces CCCvelo, a computational tool that uses spatial omics data to identify how cell–cell communication directs cell fate decisions, uncovering the spatiotemporal signaling dynamics that orchestrate development and diseases.

Xiao et al. present GITIII, a lightweight and interpretable graph transformer for inferring spatial single-cell-level interactions and quantifying the influence of neighbouring cells on the gene expression of receiver cells in spatial transcriptomics.

As single-cell datasets scale to millions of cells, analyzing cell-cell communication becomes computationally challenging. Here, authors present FastCCC, a fast, permutation-free framework that enables scalable CCC analysis and provides a comprehensive human CCC reference panel.

Serum serves as a principal time signal for circadian clocks throughout the body to synchronize them. It was found that the response of circadian clocks to serum is primarily mediated through steroid hormone receptors and integrated to the clockwork via Cry2.

The physics-informed coevolution-learning algorithm-based framework CCCvelo infers the spatio-temporal dynamics of cell-state transitions driven by both intercellular communication and intracellular transcriptional regulation. CCCvelo is able to decode cell–cell communication dynamics as well as to process image-based spatial transcriptomics data.

A longstanding challenge in the biology of extracellular vesicles has been to define their core protein and lipid composition in human plasma. A new study addresses this limitation by applying high-throughput multi-omics to establish a molecular blueprint of circulating extracellular vesicles, providing robust markers for translational applications.

CellPhoneDB infers cell–cell communication by integrating single-cell genomics data with a curated database of ligand–receptor interactions. Now, a Protocol Update describes the latest version of the tool including many new features.

We present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that simulates spatiotemporally detailed biochemical reaction networks within realistic cellular and subcellular geometries. This paper highlights the use of SMART in several biological test cases including cellular mechanotransduction, calcium signaling in neurons and cardiomyocytes, and adenosine triphosphate synthesis.

Single-cell RNA-sequencing and spatial transcriptomics data enable the inference of how information is transmitted from one cell to another and how it modulates gene expression within cells. Now, a learning method infers networks describing how the inflow of one signal, mediated by intracellular gene modules, drives the outflow of other signals for intercellular communication.