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Mechanical cues guide the formation and patterning of 3D spheroids in fibrous environments

GSE284140 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2025/08/05 Platform GPL30173
Summary
Multicellular spheroids have shown great promise in 3D biology. Many techniques exist to form spheroids, but how cells take mechanical advantage of native fibrous extracellular matrix (ECM) to form spheroids remains incompletely understood. Here, we identify the role of fiber diameter, architecture, and contractility on spheroids' spontaneous formation, growth, and maintenance using ECM-mimicking fiber networks. We show that matrix deformability is critical to forming spheroids, with deformable aligned fiber networks promoting spheroid formation independent of fiber diameter, while larger-diameter crosshatched networks of low deformability abrogate spheroid formation. Thus, a mixture of diameters and architectures allowed spatial patterning of spheroids and monolayers simultaneously. We quantify the forces involved during spheroid formation and identify the contractile role of Rho-associated protein kinase (ROCK) in spheroid formation and maintenance. Interestingly, we observed spheroid-spheroid and multiple spheroid mergers, which we describe by spheroid-shape-driven morphogenic Boolean outcomes. We found that spheroid mergers were initiated by exchanging a few cells and forming a cellular bridge connecting the two spheroids. Unexpectedly, we found large pericyte spheroids contract rhythmically. Overall, we ascertained that contractility and network deformability work together to spontaneously form and pattern 3D spheroids, potentially connecting in vivo matrix biology with developmental, disease, and regenerative biology.
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Mechanical cues guide the formation and patterning of 3D spheroids in fibrous environments
Sharma S, Agashe A, Hill JC et al. · PNAS nexus 2025 · PMID 40978083 · doi:10.1093/pnasnexus/pgaf263
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Also filed as BioProject PRJNA1197430 and SRA study SRP551101. Searching any of these in the dataset finder brings you back here.

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