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 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 | 0.002 |
| 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 | 0.001 |
| 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 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".