Massively Parallel Bead-Free Force Spectroscopy with Fluorescence
Bibliographic record
Abstract
Single-molecule force spectroscopy (SMFS) has transformed our understanding of biomolecular mechanics. However, current high-throughput implementations rely on beads to apply force, introducing size and surface chemistry variability, requiring per-bead calibration, and are prone to multitether artifacts. Long handles further complicate measurements by convolving target conformational changes with handle stretching. We introduce tether force spectroscopy (TFS), a bead-free SMFS platform in which a single DNA tether serves as both the force applicator and an internal calibrator. In TFS, shear flow acting on identical DNA tethers applies piconewton-scale forces directly to surface-anchored molecules whose conformational dynamics are simultaneously monitored by single-molecule fluorescence. This guarantees single-tether results with uniform, internally calibrated forces and is inherently compatible with single-molecule fluorescence. We achieved high-resolution, high-throughput measurements across hundreds of molecules, enabling both force-extension and rupture experiments without specialized instrumentation. The combination of simplicity and simultaneous force-fluorescence capability makes TFS broadly accessible for correlating structure and function in diverse biomolecular systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".