Emergent properties of collective gene expression patterns in multicellular systems

TL;DR

This paper presents a generalized model of multicellular gene expression that explains how cells coordinate gene activity to form stable, simple tissue types through intercellular signaling, despite complex interaction networks.

Contribution

It introduces a theoretical framework modeling tissue self-organization via intercellular signaling, revealing how simple stable tissues emerge from complex networks.

Findings

Signaling strength induces transitions from single-cell autonomy to collective states.

Stable tissue types are robust across different interaction networks and initial conditions.

The model aligns with spatial transcriptomics, enabling broad biological applications.

Abstract

Multicellular organisms comprise a diverse collection of stable tissues built from different cell types. It remains unclear how large numbers of interacting cells can precisely coordinate their gene expression during tissue self-organization. We develop a generalized model of multicellular gene expression that includes intracellular and intercellular gene interactions in tissue-like collectives. We show that tuning the intercellular signaling strength results in a cascade of transitions from single-cell autonomy towards different self-organized collective states. Despite an enormous number of possible tissue states, signaling tends to stabilize a small number of compositionally and spatially simple tissue types even for disordered interaction networks. Statistical properties of the stable phenotypes are preserved for different interaction networks and initial conditions. These results provide a theoretical framework to study how collections of cells in distinct organisms robustly self-organize into relatively simple tissues even for complex interaction networks mediated by large numbers of different molecules, and how different stable tissues are maintained in homeostasis and disease. The close alignment between this theoretical model of tissue self-organization and modern sequencing techniques, particularly spatial transcriptomics, will enable future applications in broad biological contexts.