Hyperspectral multiplex single-particle tracking of different receptor subtypes labeled with quantum dots in live neurons
Bibliographic record
Abstract
The efficacy of existing therapies and the discovery of innovative treatments for central nervous system (CNS) diseases have been limited by the lack of appropriate methods to investigate complex molecular processes at the synaptic level. To improve our capability to investigate complex mechanisms of synaptic signaling and remodeling, we designed a fluorescence hyperspectral imaging platform to simultaneously track different subtypes of individual neurotransmitter receptors trafficking in and out of synapses. This imaging platform allows simultaneous image acquisition of at least five fluorescent markers in living neurons with a high-spatial resolution. We used quantum dots emitting at different wavelengths and functionalized to specifically bind to single receptors on the membrane of living neurons. The hyperspectral imaging platform enabled the simultaneous optical tracking of five different synaptic proteins, including subtypes of glutamate receptors (mGluR and AMPAR) and postsynaptic signaling proteins. It also permitted the quantification of their mobility after treatments with various pharmacological agents. This technique provides an efficient method to monitor several synaptic proteins at the same time, which could accelerate the screening of effective compounds for treatment of CNS disorders.
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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 | 0.000 | 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 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".