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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

321
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
321

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Related Experiment Video

Updated: Jun 18, 2025

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion
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Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy.

Lijun Zhu1,2, Jianrui Li3, Jing Pan3

  • 1Department of Radiology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, 305 Zhongshan Road East, Xuanwu, Nanjing, 210002, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

Raman spectroscopy precisely identifies glioblastoma (GBM) microinfiltration at the cellular level in surgical samples. This technique detects even minimal cancer cell presence, aiding surgeons in achieving more complete tumor removal.

Keywords:
Raman spectroscopyglioblastomaidentificationmicro‐infiltration

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Area of Science:

  • Biomedical Optics
  • Neuro-oncology
  • Chemical Imaging

Background:

  • Accurate identification of glioblastoma (GBM) microinfiltration is critical for complete surgical resection but remains clinically challenging.
  • Current methods often lack the cellular resolution needed to detect diffuse tumor spread.

Purpose of the Study:

  • To evaluate the efficacy of Raman spectroscopy for detecting glioblastoma microinfiltration in clinical specimens.
  • To assess the potential of Raman spectroscopy in guiding surgical resection of GBM.

Main Methods:

  • Raman spectroscopy was employed to analyze clinical brain tissue specimens.
  • Spectral data were correlated with biochemical compositions (phospholipids, nucleic acids, amino acids, unsaturated fatty acids).
  • Spatial metabolomics and machine learning algorithms were utilized for data analysis and detection.

Main Results:

  • Raman spectroscopy achieved cellular resolution in identifying GBM microinfiltration.
  • Spectral analysis revealed significant biochemical differences between infiltrative GBM and normal brain tissue.
  • Raman imaging provided label-free morphological information of GBM lesions.
  • The combination of Raman spectroscopy and machine learning demonstrated high accuracy (AUC > 95%) in detecting infiltrative lesions.
  • A detection threshold as low as 3 GBM cells per 0.01 mm² was achieved.

Conclusions:

  • Raman spectroscopy effectively identifies glioblastoma microinfiltration with high sensitivity and specificity.
  • This technique can detect previously undetectable diffuse cancer cells, offering potential for improved surgical guidance in GBM patients.
  • Raman spectroscopy holds promise for enhancing the completeness of tumor resection in glioblastoma surgery.