A nanobody-gold conjugate for the detection of GFP-tagged synaptic proteins by dual light-electron microscopy
Bibliographic record
Abstract
Abstract To meet the constantly improving spatial resolution offered by advanced microscopy techniques to study sub-cellular structures in biology, there is a need for small, monovalent probes that label proteins of interest with high specificity and minimal distance to the target, and are compatible with various imaging modalities. In this direction, we designed a strategy to generate minimal-size probes composed of a controlled 1:1 conjugate between a small domain binder and a 1.4 nm-gold nanoparticle with direct access to fluorescent labelling for dual light-electron microscopy. Our approach was applied to the widely used single-domain antibody against GFP (GBP). The modified GBP-gold conjugate retained normal binding to purified GFP in vitro, specifically labelled COS-7 cells and neurons expressing GFP-tagged synaptic membrane proteins, and penetrated readily into tight cell-cell contacts including neuronal synapses. The optional fluorescence labelling with a second ALFA nanobody allowed dSTORM imaging, while the silver-enhanced nanogold particle detected in TEM was used to characterize the number and nanoscale organization of individual proteins in the synaptic cleft. We counted a small number of endogenous neurexins in the pre-synapse and a larger number of AMPA receptors in the post-synapse, often aligned in nanodomains. This GBP-gold probe thus emerges as a potent tool to label an ever-increasing repertoire of GFP-tagged proteins in numerous biological organisms and models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".