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Related Experiment Video

Updated: Mar 7, 2026

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
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Multiplexed Microenvironment-Responsive Probes Enable Rapid Glioblastoma Cell Line Analysis.

Qian Wu1, Yi Ren2, Guoyang Zhang3

  • 1State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China.

Analytical Chemistry
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

Accurate glioblastoma (GBM) cell identification is improved using a novel optical sensing platform. This technology analyzes the tumor microenvironment (TME) to distinguish GBM cell phenotypes, aiding clinical diagnostics.

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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.