Single-molecule fluorescence imaging of processive myosin with enhanced background suppression using linear zero-mode waveguides (ZMWs) and convex lens induced confinement (CLIC)
Bibliographic record
Abstract
Resolving single fluorescent molecules in the presence of high fluorophore concentrations remains a challenge in single-molecule biophysics that limits our understanding of weak molecular interactions. Total internal reflection fluorescence (TIRF) imaging, the workhorse of single-molecule fluorescence microscopy, enables experiments at concentrations up to about 100 nM, but many biological interactions have considerably weaker affinities, and thus require at least one species to be at micromolar or higher concentration. Current alternatives to TIRF often require three-dimensional confinement, and thus can be problematic for extended substrates, such as cytoskeletal filaments. To address this challenge, we have demonstrated and applied two new single-molecule fluorescence microscopy techniques, linear zero-mode waveguides (ZMWs) and convex lens induced confinement (CLIC), for imaging the processive motion of molecular motors myosin V and VI along actin filaments. Both technologies will allow imaging in the presence of higher fluorophore concentrations than TIRF microscopy. They will enable new biophysical measurements of a wide range of processive molecular motors that move along filamentous tracks, such as other myosins, dynein, and kinesin. A particularly salient application of these technologies will be to examine chemomechanical coupling by directly imaging fluorescent nucleotide molecules interacting with processive motors as they traverse their actin or microtubule tracks.
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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 | 0.001 |
| 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".