Filling the gap: adding super-resolution to array tomography for correlated ultrastructural and molecular identification of electrical synapses at the C. elegans connectome
Bibliographic record
Abstract
Correlating molecular labeling at the ultrastructural level with high confidence remains challenging. Array tomography (AT) allows for a combination of fluorescence and electron microscopy (EM) to visualize subcellular protein localization on serial EM sections. Here, we describe an application for AT that combines near-native tissue preservation via high-pressure freezing and freeze substitution with super-resolution light microscopy and high-resolution scanning electron microscopy (SEM) analysis on the same section. We established protocols that combine SEM with structured illumination microscopy (SIM) and direct stochastic optical reconstruction microscopy (dSTORM). We devised a method for easy, precise, and unbiased correlation of EM images and super-resolution imaging data using endogenous cellular landmarks and freely available image processing software. We demonstrate that these methods allow us to identify and label gap junctions in Caenorhabditis elegans with precision and confidence, and imaging of even smaller structures is feasible. With the emergence of connectomics, these methods will allow us to fill in the gap-acquiring the correlated ultrastructural and molecular identity of electrical synapses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".