Machine Learning Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells.

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 remain challenging since current tools are ill-suited, 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 nearby different cell lines. We found significant increase in secretion, or consumption of lactate, glucose, ATP, glutamine and urea within 20 microns from the cells surface compared to the bulk. We also observed that cancerous cells (HeLa) compared to fibroblast (REF52) have a greater glycolytic rate, expected by 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 to study cell biology.