Quantitative Single-Molecule Imaging with Statistical Machine Learning
Bibliographic record
Abstract
Abstract Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing SMLM into a quantitative method capable of determining the abundance and stoichiometry of macromolecular complexes. These methods often require knowledge of the complex photophysical properties of photoswitchable flourophores. We previously developed a simpler method built upon the observation that most photswitchable fluorophores emit an exponentially distributed number of blinks before photobleaching, but its utility was limited by the need to calibrate for the blinking distribution. Here we extend this method by incorporating a machine learning technique known as Expectation-Maximization (EM) and apply it to a statistical mixture model of monomers, dimers and trimers. We show that the protomer fractions and the underlying single-fluorophore blinking distributions can be inferred, simultaneously, from SMLM datasets, obviating the need for an additional calibration and greatly expanding the applicability of this technique. To illustrate the utility of our approach, we benchmark the method on both simulated datasets and experimental datasets assembled from dSTORM images of Alexa-647 labeled DNA nanostructures.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".