A High-Throughput Image Correlation Method for Rapid Analysis of Fluorophore Photoblinking and Photobleaching Rates
Bibliographic record
Abstract
Super-resolution fluorescence imaging based on localization microscopy requires tuning the photoblinking properties of fluorescent dyes employed. Missing is a rapid way to analyze the blinking rates of the fluorophore probes. Herein we present an ensemble autocorrelation technique for rapidly and simultaneously measuring photoblinking and bleaching rate constants from a microscopy image time series of fluorescent probes that is significantly faster than individual single-molecule trajectory analysis approaches. Our method is accurate for probe densities typically encountered in single-molecule studies as well as for higher density systems which cannot be analyzed by standard single-molecule techniques. We also show that we can resolve characteristic blinking times that are faster than camera detector exposure times, which cannot be accessed by threshold-based single-molecule approaches due to aliasing. We confirm this through computer simulation and single-molecule imaging data of DNA-Cy5 complexes. Finally, we demonstrate that with sufficient sampling our technique can accurately recover rates from stochastic optical reconstruction microscopy super-resolution data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".