Dynamic SERS nanosensor for neurotransmitter sensing near neurons
Bibliographic record
Abstract
Current electrophysiology and electrochemistry techniques have provided unprecedented understanding of neuronal activity. However, these techniques are suited to a small, albeit important, panel of neurotransmitters such as glutamate, GABA and dopamine, and these constitute only a subset of the broader range of neurotransmitters involved in brain chemistry. Surface-enhanced Raman scattering (SERS) provides a unique opportunity to detect a broader range of neurotransmitters in close proximity to neurons. Dynamic SERS (D-SERS) nanosensors based on patch-clamp-like nanopipettes decorated with gold nanoraspberries can be located accurately under a microscope using techniques analogous to those used in current electrophysiology or electrochemistry experiments. In this manuscript, we demonstrate that D-SERS can measure in a single experiment ATP, glutamate (glu), acetylcholine (ACh), GABA and dopamine (DA), among other neurotransmitters, with the potential for detecting a greater number of neurotransmitters. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method and time series of the neurotransmitter levels were constructed. The D-SERS nanosensor was then located near cultured mouse dopaminergic neurons. The detection of neurotransmitters was performed in response to a series of K + depolarisations, and allowed the detection of elevated levels of both ATP and dopamine. Control experiments were also performed near glial cells, showing only very low basal detection neurotransmitter events. This paper demonstrates the potential of D-SERS to detect neurotransmitter secretion events near living neurons, but also constitutes a strong proof-of-concept for the broad application of SERS to the detection of secretion events by neurons or other cell types in order to study normal or pathological cell functions.
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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".