Nuclear size-regulated emergence of topological packing order on growing human lung alveolospheres
Bibliographic record
Abstract
Within multicellular living systems, cells coordinate their positions with spatiotemporal accuracy to form various structures, setting the clock to control developmental processes and trigger maturation. These arrangements can be regulated by tissue topology, biochemical cues, as well as mechanical perturbations. However, the fundamental rules of how local cell packing order is regulated in forming three-dimensional (3D) multicellular architectures remain unclear. Furthermore, how cellular coordination evolves during developmental processes, and whether this cell patterning behavior is indicative of more complex biological functions, is largely unknown. Here, using human lung alveolospheres as a model system, by combining experiments and numerical simulations, we find that, surprisingly, cell packing behavior on alveolospheres resembles hard-disk packing but with increased randomness; the stiffer cell nuclei act as the hard disks surrounded by deformable cell bodies. Interestingly, we observe the emergence of topological packing order during alveolosphere growth, as a result of increasing nucleus-to-cell size ratio. Specifically, we find more hexagon-concentrated cellular packing with increasing bond orientational order, indicating a topological gas-to-liquid transition. Additionally, by osmotically changing the compactness of cells on alveolospheres, we observe that the variations in packing order align with the change of nucleus-to-cell size ratio. Together, our findings reveal the underlying rules of cell coordination and topological phases during human lung alveolosphere growth. These static packing characteristics are consistent with cell dynamics, together suggesting that better cellular packing stabilizes local cell neighborhoods and may regulate more complex biological functions such as organ development and cellular maturation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".