Collective Mechanical Memory Encoded by Long-Lasting Supracellular Cytoskeletal Structures in Multicellular Spheroids
Bibliographic record
Abstract
Animal cells can sense and "remember" the stiffness of their extracellular environment, resulting in sustained changes in form and function. Such "mechanical memory" has been previously explored using individual cells and attributed to epigenetic changes and transcriptional activity. However, it is unclear whether such memory is retained across collective cells. Here, we report that collective cells sustain mechanical memory through self-organized actin-CK18 networks spanning multiple cell lengths, even under dramatically changing mechanical environments, such as those encountered during cancer metastasis. As a case study, we modeled ovarian cancer metastasis and found that cells initially cultured on different stiffness retained distinct migratory phenotypes throughout the environmental transitions of the metastasis model. Notably, soft-primed cells, in particular, demonstrated stronger cell-cell adhesions than stiff-primed cells. Upon aggregation into multicellular spheroids, mimicking malignant spheroids found in patient ascites, the soft-primed spheroids exclusively developed a dense cage-like supracellular actin-CK18 structure at their peripheral surfaces. Furthermore, these soft-primed spheroids exhibited impeded collective invasion, instead becoming confined by the long-lasting cytoskeletal cage. Inhibition of gap junctions attenuated the formation of cytoskeletal cages, indicating that dynamic intercellular communication via gap junctions is essential for maintaining collective mechanical memory. This work demonstrates a collective mechanism of mechanical memory that is not solely dependent on epigenetic and transcriptional activation, advancing our understanding of the elevated metastatic potential of tumor cell clusters originating in stiffened matrices.
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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) | 0.000 | 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".