<scp>3D</scp> mapping of subcellular structures with super‐resolution array tomography
Bibliographic record
Abstract
The combination of fluorescence light microscopy (FLM) and electron microscopy (EM) makes it possible to put molecular identity in its full ultrastructural context. With array tomography (AT) long ribbons of serial ultrathin sections are labelled via immunohistochemistry (IHC). After FLM imaging the same sections are processed for scanning electron microscopy (SEM) and re‐analyzed. The resulting images are correlated and superimposed. Due to the serial nature of this approach it is possible to obtain large volumes of correlated multi‐channel light and electron microscopic data. One drawback of this method is the large discrepancy of resolution between light and electron microscopy. To alleviate this we advanced AT for two super‐resolution light microscopy techniques, Structured Illumination Microscopy (SIM) and direct Stochastic Optical Reconstruction Microscopy ( d STORM). We also 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. Together, these advances make it possible to map even small subcellular structures with high precision and confidence. We applied our super‐resolution AT approach in C. elegans to address an important problem in connectomics research: the mapping of gap junctions at connectomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".