Dynamic-SERS Optophysiology: A Nanosensor for Monitoring Cell Secretion Events
Bibliographic record
Abstract
We monitored metabolite secretion near living cells using a plasmonic nanosensor. The nanosensor created from borosilicate nanopipettes analogous to the patch clamp was decorated with Au nanoparticles and served as a surface-enhanced Raman scattering (SERS) substrate with addressable location. With this nanosensor, we acquired SERS locally near Madin-Darby canine kidney (MDCKII) epithelial cells, and we detected multiple metabolites, such as pyruvate, lactate, ATP, and urea simultaneously. These plasmonic nanosensors were capable of monitoring metabolites in the extracellular medium with enough sensitivity to detect an increase in metabolite concentration following the lyses of MDCKII cells with a nonionic surfactant. The plasmonic nanosensors also allowed a relative quantification of a chemical gradient for a metabolite near cells, as demonstrated with a decrease in relative lactate to pyruvate concentration further away from the MDCKII cells. This SERS optophysiology technique for the sensitive and nondestructive monitoring of extracellular metabolites near living cells is broadly applicable to different cellular and tissue models and should therefore provide a powerful tool for cellular studies.
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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 | 0.001 | 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".