Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds
Bibliographic record
Abstract
Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".