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.
Keywords: ATP; HUVEC; HeLa; SERS optophysiology; TensorFlow; dynamic surface-enhanced Raman scattering; machine learning; nanobiosensor; plasmonics.