Multiplexed Microenvironment-Responsive Probes Enable Rapid Glioblastoma Cell Line Analysis.

Area of Science:

• Biomedical Engineering
• Cancer Biology
• Analytical Chemistry

Background:

• Accurate glioblastoma (GBM) cell line phenotyping is crucial for clinical diagnostics but challenging due to tumor heterogeneity and limitations of conventional methods.
• The tumor microenvironment (TME) significantly influences tumor progression, necessitating its analysis for precise cancer characterization.
• Existing diagnostic methods often fail to capture the functional cellular variations essential for distinguishing GBM phenotypes.

Purpose of the Study:

• To develop a multiplexed optical sensing platform for discriminating GBM cell lines.
• To leverage the metabolic and physicochemical heterogeneities within the TME for enhanced cell identification.
• To integrate multiparameter sensing with deep learning for accurate GBM cell line classification.

Main Methods:

• Development of a multiplexed optical sensing platform integrating five microenvironment-responsive fluorescent probes (HBTPB, CTCYS, BDPI, KLVIS, BIDOH).
• Simultaneous monitoring of key TME parameters: hydrogen peroxide, cysteine, peroxynitrite, viscosity, and pH.
• Application of a ResNet-based deep learning model to analyze the multiparameter data from the sensing platform.

Main Results:

• The platform successfully discriminated between six cell lines, including four phenotypically diverse GBM cell lines, normal human astrocytes, and a central nervous system tumor cell line.
• Multiparameter analysis of TME properties provided distinct signatures for different cell lines.
• Integration with the ResNet model achieved highly accurate identification of the tested cell lines.

Conclusions:

• The developed multiplexed optical sensing platform offers a promising approach for accurate GBM cell line identification.
• Exploiting TME heterogeneities combined with deep learning can overcome limitations of conventional diagnostic methods.
• This technology has the potential to improve intraoperative assessment and clinical diagnostics for glioblastoma.