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Related Experiment Video

Updated: Sep 16, 2025

Imaging Cleared Intact Biological Systems at a Cellular Level by 3DISCO
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Three-Dimensional Visualisation of Blood Vessels in Human Gliomas Using Tissue Clearing and Deep Learning.

Xiaodu Yang1,2,3,4,5, Xinyue Wang1,2, Dian He1,2

  • 1Clinical Biobank Center, Microbiome Medicine Center, Department of Laboratory Medicine, Provincial Clinical Research Centre for Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

Neuropathology and Applied Neurobiology
|July 8, 2025
PubMed
Summary

This study introduces a novel 3D imaging technique combining tissue clearing and deep learning for detailed visualization of glioma vasculature. The method accurately maps tumor blood vessels, revealing key differences between low- and high-grade gliomas.

Keywords:
3D visualisationdeep learningfluorescent labellinghuman gliomatissue clearingvascular heterogeneity

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Area of Science:

  • Neuroscience
  • Biomedical Imaging
  • Computational Biology

Background:

  • Gliomas are aggressive brain tumors requiring detailed vascular visualization.
  • Traditional 2D imaging fails to capture the spatial complexity of tumor vasculature.
  • Understanding glioma vascular heterogeneity is crucial for diagnosis and treatment.

Purpose of the Study:

  • To develop and validate a comprehensive 3D visualization method for human glioma vasculature.
  • To compare deep learning-based vessel extraction with traditional methods.
  • To investigate morphological differences in vasculature between low- and high-grade gliomas.

Main Methods:

  • Combined tissue clearing (OPTIClear) with 3D confocal microscopy on human glioma samples.
  • Employed a specialized 3D U-Net deep learning model for automated vessel extraction.
  • Performed immunofluorescent labeling for vasculature, cells, and nuclei.

Main Results:

  • Achieved holistic 3D representation of glioma vasculature and surrounding cells using OPTIClear.
  • Demonstrated superior accuracy and efficiency of the 3D U-Net compared to Imaris for vessel extraction.
  • Identified significant variations in vascular morphology and vessel wall integrity between low- and high-grade gliomas.

Conclusions:

  • The integrated approach provides enhanced 3D visualization of human glioma vasculature.
  • Deep learning significantly improves vessel extraction accuracy and efficiency.
  • Observed morphological disparities in vasculature correlate with glioma grade, impacting pathology and treatment strategies.