Department of Chemistry , Université de Montréal , Case Postale 6128 Succursale Centre-Ville, Montreal , Quebec , Canada , H3C 3J7.
Department of Cellular Biophysics , Max Planck Institute for Medical Research , INF 253, D-69120 Heidelberg , Germany.
ACS Nano. 2019 Feb 26;13(2):1403-1411. doi: 10.1021/acsnano.8b07024. Epub 2019 Feb 12.
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.
细胞外环境是一个复杂的介质，细胞在此分泌和消耗代谢物。因此，分子梯度在细胞附近形成，引发各种生物和生理反应。然而，研究这些分子梯度仍然具有挑战性，因为目前的工具不适合，提供的时间和空间分辨率较差，同时还具有破坏性。在这里，我们报告了一种机器学习方法的开发和应用，该方法结合了表面增强拉曼光谱（SERS）纳米探针，用于测量体外不同细胞系附近至少八种代谢物的梯度。我们发现与细胞表面的主体相比，在距离细胞表面 20μm 以内的范围内，乳酸盐、葡萄糖、ATP、谷氨酰胺和尿素的分泌或消耗显著增加。我们还观察到与成纤维细胞（REF52）相比，癌细胞（HeLa）的糖酵解速率更高，这是这种表型所预期的。与对照相比，内皮细胞（HUVEC）和 HeLa 细胞的细胞外 ATP 显著增加，这揭示了细胞外 ATP 在癌症局部环境中的作用。基于机器学习的 SERS 光学生理学通常适用于涉及细胞过程的代谢物，为研究细胞生物学提供了一个通用平台。
