Dynamically arrested condensate fusion creates complex structures with varying material properties
Bibliographic record
Abstract
Abstract The cell nucleus and cytosol contain numerous biomolecular condensates which dynamically reshape, fuse and split to accomplish precise compartmentalization of the cell material. While it has been observed that some condensates rapidly coalesce, some others only attach to each other, or do not establish persistent interactions over time. Here, we explain these observations through optical tweezers and Molecular Dynamics simulations focusing on two condensate-forming, RNA-binding proteins—FUS and G3BP1—strongly involved in RNA metabolism and stress responses. We find that the fusion of pure droplets formed by these proteins can give rise to multiphase single-component condensates exhibiting notably different densities, architectures, and material properties. Such behaviour is dictated by the relative timescales of condensate fusion and protein internal mixing. A critical parameter controlling this interplay is the extent of ageing that condensates display; e.g., their progressive hardening driven by the accumulation of inter-protein β -sheet assemblies over time. Strikingly, different degrees of ageing in fusing droplets can lead single-component condensates to form diverse architectures including concentric drops or two-sided condensates. Overall, our results highlight a mechanism, based on the temporal coupling between ageing, fusion, and mixing rate, by which biomolecular condensates form multiphasic structures with markedly different material properties, and hence potentially distinct biological roles.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".