A microfluidic system with optical laser tweezers to study mechanotransduction and focal adhesion recruitment
Bibliographic record
Abstract
We present a new method to locally apply mechanical tensile and compressive force on single cells based on integration of a microfluidic device with an optical laser tweezers. This system can locate a single cell within customized wells exposing a square-like membrane segment to a functionalized bead. Beads are coated with extracellular matrix (ECM) proteins of interest (e.g. fibronectin) to activate specific membrane receptors (e.g. integrins). The functionalized beads are trapped and manipulated by optical tweezers to apply mechanical load on the ECM-integrin-cytoskeleton linkage. Activation of the receptor is visualized by accumulation of expressed fluorescent proteins. This platform facilitates isolation of single cells and excitation by tensile/compressive forces applied directly to the focal adhesion via specific membrane receptors. Protein assembly or recruitment in a focal adhesion can then be monitored and identified using fluorescent imaging. This platform is used to study the recruitment of vinculin upon the application of external tensile force to single endothelial cells. Vinculin appears to be recruited above the forced bead as an elliptical cloud, centered 2.1 ± 0.5 μm from the 2 μm bead center. The mechanical stiffness of the membrane patch inferred from this measurement is 42.9 ± 6.4 pN μm(-1) for a 5 μm × 5 μm membrane segment. This method provides a foundation for further studies of mechanotransduction and tensile stiffness of single cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".