Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells
Bibliographic record
Abstract
The extracellular environment is a complex medium in which cells secrete and consume metabolites. Molecular gradients are thereby created near cells, triggering various biological and physiological responses. However, investigating these molecular gradients remains challenging because the current tools are ill-suited and provide poor temporal and special resolution while also being destructive. Herein, we report the development and application of a machine learning approach in combination with a surface-enhanced Raman spectroscopy (SERS) nanoprobe to measure simultaneously the gradients of at least eight metabolites in vitro near different cell lines. We found significant increase in the secretion or consumption of lactate, glucose, ATP, glutamine, and urea within 20 μm from the cells surface compared to the bulk. We also observed that cancerous cells (HeLa) compared to fibroblasts (REF52) have a greater glycolytic rate, as is expected for this phenotype. Endothelial (HUVEC) and HeLa cells exhibited significant increase in extracellular ATP compared to the control, shining light on the implication of extracellular ATP within the cancer local environment. Machine-learning-driven SERS optophysiology is generally applicable to metabolites involved in cellular processes, providing a general platform on which to study cell biology.
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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".