Visualizing Spatiotemporal Dynamics of Intercellular Mechanotransmission upon Wounding
Bibliographic record
Abstract
During cell-to-cell communications, the interplay between physical and biochemical cues is essential for informational exchange and functional coordination, especially in multicellular organisms. However, it remains a challenge to visualize intercellular signaling dynamics in single live cells. Here, we report a photonic approach, based on laser microscissors and Förster resonance energy transfer (FRET) microscopy, to study intercellular signaling transmission. First, using our high-throughput screening platform, we developed a highly sensitive FRET-based biosensor (SCAGE) for Src kinase, a key regulator of intercellular interactions and signaling cascades. Notably, SCAGE showed a more than 40-fold sensitivity enhancement than the original biosensor in live mammalian cells. Next, upon local severance of physical intercellular connections by femtosecond laser pulses, SCAGE enabled the visualization of a transient Src activation across neighboring cells. Lastly, we found that this observed transient Src activation following the loss of cell-cell contacts depends on the passive structural support of cytoskeleton but not on the active actomyosin contractility. Hence, by precisely introducing local physical perturbations and directly visualizing spatiotemporal transmission of ensuing signaling events, our integrated approach could be broadly applied to mimic and investigate the wounding process at single-cell resolutions. This integrated approach with highly sensitive FRET-based biosensors provides a unique system to advance our in-depth understanding of molecular mechanisms underlying the physical-biochemical basis of intercellular coupling and wounding processes.
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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 | 0.001 |
| 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".