Silent, fluorescent labeling of native neuronal receptors
Bibliographic record
Abstract
We have developed a minimally-perturbing strategy that enables labeling and subcellular visualization of endogenous dendritic receptors on live, wild-type neurons. Specifically, calcium-permeable non-NMDA glutamate receptors expressed in hippocampal neurons can be targeted with this novel synthetic tri-functional molecule. This ligand-directed probe was targeted towards AMPA receptors and bears an electrophilic group for covalent bond formation with an amino acid side chain on the extracellular side of the ion channel. This molecule was designed in such a way that the use-dependent, polyamine-based ligand accumulates the chemically-reactive group at the extracellular side of these polyamine-sensitive receptors, thereby allowing covalent bond formation between an electrophilic moiety on the nanoprobe and a nucleophilic amino acid sidechain on the receptor. Bioconjugation of this molecule results in a stable covalent bond between the nanoprobe and the target receptor. Subsequent photolysis of a portion of the nanoprobe may then be employed to effect ligand release allowing the receptor to re-enter the non-liganded state, all the while retaining the fluorescent beacon for visualization. This technology allows for rapid fluorescent labeling of native polyamine-sensitive receptors and further advances the field of fluorescent labeling of native biological molecules.
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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 | 0.000 | 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".