Back

A closed feedback between tissue phase transitions and morphogen gradients drives patterning dynamics

Autorino, C.; Khoromskaia, D.; Harari, L.; Floris, E.; Booth, H.; Pallares-Cartes, C.; Petrasiunaite, V.; Dorrity, M.; Corominas-Murtra, B.; Hadjivasiliou, Z.; Petridou, N.

2025-06-06 developmental biology
10.1101/2025.06.06.658228 bioRxiv
Show abstract

During development mechanochemical cues in the cell microenvironment are translated into signalling to drive cell fate decisions. As cells differentiate collectively, it raises the question of how tissue-level properties affect instructive cues of decision-making. Here, we show that a tissue rigidity phase transition guides patterning by tuning the length-scales and time-scales of morphogen signalling. By combining rigidity percolation theory, reaction-diffusion modelling, quantitative imaging, optogenetics and single-cell transcriptomics in zebrafish, we uncover dynamical global tissue rigidity patterns that actively shape the Nodal morphogen gradient by restricting ligand dispersal and accelerating its signalling activity. In this self-generated mechanism, Nodal, besides driving meso-endoderm fate specification, increases cell-cell adhesion strength via regulating planar cell polarity genes. Once adhesion strength reaches a critical point, it triggers a rigidity transition which collapses tissue porosity. The abrupt tissue reorganisation negatively feeds back on Nodal signalling impacting both its length-scales, by limiting Nodal diffusivity, and its time-scales, by speeding up the expression of its antagonist Lefty, thereby ensuring timely signal termination and robust patterning. Overall, we reveal how emergent properties set the spatiotemporal dynamics of morphogen gradients, uncovering macroscopic mechanisms of pattern formation.

Published in Nature Cell Biology (predicted rank #4) · training set

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.