Dynamic actin cross-linking governs the cytoplasm’s transition to fluid-like behavior
Bibliographic record
Abstract
Cells precisely control their mechanical properties to organize and differentiate into tissues. The architecture and connectivity of cytoskeletal filaments change in response to mechanical and biochemical cues, allowing the cell to rapidly tune its mechanics from highly cross-linked, elastic networks to weakly cross-linked viscous networks. While the role of actin cross-linking in controlling actin network mechanics is well-characterized in purified actin networks, its mechanical role in the cytoplasm of living cells remains unknown. Here, we probe the frequency-dependent intracellular viscoelastic properties of living cells using multifrequency excitation and in situ optical trap calibration. At long timescales in the intracellular environment, we observe that the cytoskeleton becomes fluid-like. The mechanics are well-captured by a model in which actin filaments are dynamically connected by a single dominant cross-linker. A disease-causing point mutation (K255E) of the actin cross-linker α-actinin 4 (ACTN4) causes its binding kinetics to be insensitive to tension. Under normal conditions, the viscoelastic properties of wild-type (WT) and K255E+/- cells are similar. However, when tension is reduced through myosin II inhibition, WT cells relax 3× faster to the fluid-like regime while K255E+/- cells are not affected. These results indicate that dynamic actin cross-linking enables the cytoplasm to flow at long timescales.
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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".