Quantum Dot‐Based FRET Nanosensors for Talin‐Membrane Assembly and Mechanosensing
Bibliographic record
Abstract
Abstract Understanding the mechanisms of assembly and disassembly of macromolecular structures in cells relies on solving biomolecular interactions. However, those interactions often remain unclear because tools to track molecular dynamics are not sufficiently resolved in time or space. In this study, we present a straightforward method for resolving inter‐ and intra‐molecular interactions in cell adhesive machinery, using quantum dot (QD) based Förster resonance energy transfer (FRET) nanosensors. Using a mechanosensitive protein, talin, one of the major components of focal adhesions, we are investigating the mechanosensing ability of proteins to sense and respond to mechanical stimuli. First, we quantified the distances separating talin and a giant unilamellar vesicle membrane for three talin variants. These variants differ in molecular length. Second, we investigated the mechanosensing capabilities of talin, i.e., its conformational changes due to mechanical stretching initiated by cytoskeleton contraction. Our results suggest that in early focal adhesion, talin undergoes stretching, corresponding to a decrease in the talin‐membrane distance of 2.5 nm. We demonstrate that QD‐FRET nanosensors can be applied for the sensitive quantification of mechanosensing with a sub‐nanometer accuracy.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".