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Updated: Jan 29, 2026

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

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

Félix Lussier¹, Dimitris Missirlis^2,3, Joachim P Spatz^2,3

¹Department of Chemistry , Université de Montréal , Case Postale 6128 Succursale Centre-Ville, Montreal , Quebec , Canada , H3C 3J7.

|February 7, 2019

Summary

This summary is machine-generated.

Researchers developed a new method using machine learning and SERS nanoprobes to measure metabolite gradients near cells. This technique reveals how cells consume and secrete molecules like glucose and ATP, offering insights into cell biology.

Keywords:

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

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Area of Science:

Cell Biology
Biochemistry
Spectroscopy

Background:

Cells interact with their environment by secreting and consuming metabolites.
Molecular gradients near cells influence biological responses.
Current methods for studying these gradients lack resolution and are destructive.

Purpose of the Study:

To develop a novel method for measuring extracellular metabolite gradients.
To investigate metabolite dynamics near different cell lines in vitro.
To understand the role of extracellular metabolites in cellular processes.

Main Methods:

Development of a machine learning approach combined with surface-enhanced Raman spectroscopy (SERS) nanoprobe.
Simultaneous measurement of at least eight metabolites.
In vitro analysis near various cell lines (HeLa, REF52, HUVEC).

Main Results:

Detected significant increases in lactate, glucose, ATP, glutamine, and urea within 20 μm of cell surfaces.
Observed a higher glycolytic rate in cancerous HeLa cells compared to fibroblasts (REF52).
Found increased extracellular ATP in endothelial (HUVEC) and HeLa cells, suggesting its role in cancer microenvironments.

Conclusions:

Machine-learning-driven SERS optophysiology provides a versatile platform for studying cell biology.
This method enables detailed investigation of cell-metabolite interactions.
The findings shed light on metabolic differences between cell types and the significance of extracellular ATP in cancer.