Quantitative expansion microscopy for the characterization of the spectrin periodic skeleton of axons using fluorescence microscopy
Bibliographic record
Abstract
Fluorescent nanoscopy approaches have been used to characterize the periodic organization of actin, spectrin and associated proteins in neuronal axons and dendrites. This membrane-associated periodic skeleton (MPS) is conserved across animals, suggesting it is a fundamental component of neuronal extensions. The nanoscale architecture of the arrangement (190 nm) is below the resolution limit of conventional fluorescent microscopy. Fluorescent nanoscopy, on the other hand, requires costly equipment and special analysis routines, which remain inaccessible to most research groups. This report aims to resolve this issue by using protein-retention expansion microscopy (pro-ExM) to reveal the MPS of axons. ExM uses reagents and equipment that are readily accessible in most neurobiology laboratories. We first explore means to accurately estimate the expansion factors of protein structures within cells. We then describe the protocol that produces an expanded specimen that can be examined with any fluorescent microscopy allowing quantitative nanoscale characterization of the MPS. We validate ExM results by direct comparison to stimulated emission depletion (STED) nanoscopy. We conclude that ExM facilitates three-dimensional, multicolor and quantitative characterization of the MPS using accessible reagents and conventional fluorescent microscopes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".