Chinese Optics, Volume. 17, Issue 5, 995(2024)

Application of Raman spectroscopy in the detection of brain glioma

Mei-lan GE1,2, Yu-ye WANG1,2、*, Hai-bin LI1,2, De-gang XU1,2, and Jian-quan YAO1,2
Author Affiliations
  • 1School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of Optoelectronic Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China
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    Figures & Tables(24)
    The research state of the application of Raman spectroscopy in the detection of brain glioma
    (a) and (b) are photomicrographs of unstained human glioblastoma cryosections used in Raman mapping experiments; (c) and (d) are pseudo-color maps of Raman spectra of the tissue sections shown in (a) and (b). Red: areas of vital tumor tissue; blue: areas of necrosis; yellow: areas in the scan where no tissue was present (edges, freezing artifacts); (e) and (f) are photomicrographs of the same tissue sections after H&E staining; (g) ×40 magnification of detail marked with a green frame in (f)[34]
    (a) Raman spectra and (b) Raman imaging results of different components in invasive tissue[36]
    Comparison of Raman spectra of IDH1-wt and IDH1-mut glioma[39]
    The average Raman spectra of healthy (blue) and tumorous tissue (red)[39]
    (a) Schematic diagram of the Raman system in Ref. [42] and (b) photo of Raman measurement based on mouse model in vivo[42]
    (a) Mouse brain tissue with exposed cortex; (b) Raman images are segmented by cluster analysis. Normal brain tissue is depicted in blue, cyan, and yellow, the red is blood vessel, and the tumor and tumor margin are shown in gray and black, respectively; (c) superimposition of the photomicrograph and the Raman image of the tissue[42]
    (a) Schematic diagram of experimental setup of the handheld contact Raman spectroscopy probe for and (b) photo of brain tissue detection based on the probe[43]
    (a) Exploded view of the Raman microprobe components along the internal cannula of the commercial brain biopsy needle; (b) images from the neuronavigation system in the process of Raman detection[43]
    The Raman results of handheld contact probes at different tissue locations. (a) Schematic diagram of in vivo Raman spectral measurements taken in the surgical cavity during glioma resection, using a handheld contact probe to target dense cancerous tissue (red), infiltrated brain tissue (yellow) and surrounding normal brain tissue; (b) In vivo high wavenumber Raman spectra of dense cancer, infiltrated brain and normal brain, averaged over all samples; (c) Representative H&E-stained micrographs for each tissue type[44]
    The identification results of normal brain, infiltrated brain and dense cancer tissue based on SVM algorithm. (a) Boxplots of the Raman intensity ratio of the lipid and protein in the bands of 2930 cm−1:2845 cm−1 for normal brain, infiltrated brain and dense cancer tissue in glioma patients; (b) receiver operating characteristic curve computed by the SVM algorithm[44]
    The ratios of (a) I1588 cm−1 ∕I1440 cm−1 and (b) I2934 cm−1 ∕I2885 cm−1 from normal human brain tissues and glioma tissues with increasing malignancy; G0-N: normal human brain tissues; GI: grade I; GII: grade II; GIII: grade III; GIV: grade IV[45]
    The clustering results of normal tissue, low-grade (I and II) , and high-grade (III and IV) glioma[45]
    CARS microscopy image of a healthy mouse brain[46]
    (a) The enlarged image of the white rectangle in the Fig. 14; (b) H&E image of the same region in Fig. 15(a)[46]
    CARS images of astrocytoma in mouse’s brain. (a) Mosaic CARS microscopy image with low-resolution and large field of view; (b) the CARS image of local tissue of the white rectangle in (a) [46]
    (a) Bright field image of glioblastoma in mouse brain, with the tumor boundary outlined (black). The cyan indicates a region of interest (ROI); (b) micrograph of ROIs; (c) pseudocolour CARS image of tumor and normal brain tissues, with nuclei highlighted in blue, lipid content in red and red blood cells in green; (d) CARS image with nuclei highlighted in blue and lipid content in red; (e) CARS image with nuclei highlighted in blue, lipid content in red and CH3 stretch–CH2 in green, NB: normal brain; T: tumor cells, WM: white matter; (f) normalized CARS of different tissues[47]
    SRS imaging of fresh mouse tissue. (a) The myelin sheath neuron bundles of the corpus callosum in mouse brain is marked with abundant CH2 at the characteristic peak of 2845 cm−1; (b) SRS image of CH2 acquired from a brain tissue slice ~1 mm thick; (c) SRS images of CH2 in three separate regions at the same depth in mouse ear skin. From left to right: stratum corneum, sebaceous gland, and subcutaneous fat layer; (d) comparison of SRS and CARS images of stratum corneum on and off the CH2 resonance[47]
    SERS spectra of (a) healthily, (b) II grade, (c) III grade, (d) IV grade tissue[50]
    (a) Measurement results of Raman scattering spectra and (b) the difference in Raman spectra between neuronal glioma cells and normal astrocytes[50]
    Raman spectra of differentiated, undifferentiated C6 and SK-N-SH cells and normal neuronal cells[52]
    Difference in marker peak intensities for differentiated and undifferentiated cells. (a) C6 cells; (b) SK-N-SH cells[52]
    • Table 1. Comparison of advantages and disadvantages of the three nonlinear Raman spectroscopy techniques: CARS, SRS and SERS

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      Table 1. Comparison of advantages and disadvantages of the three nonlinear Raman spectroscopy techniques: CARS, SRS and SERS

      拉曼光谱技术CARSSRSSERS
      优点无标记无标记、标准谱克服荧光背景噪声
      缺点易受非共振背景噪声影响、非标准谱、系统复杂系统复杂、分子选择性强引入新的金属材料、基底制备工艺复杂
    • Table 2. Application of different Raman spectroscopy techniques in the detection of brain glioma biomarkers

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      Table 2. Application of different Raman spectroscopy techniques in the detection of brain glioma biomarkers

      年份拉曼技术标志物光谱范围/cm−1参考文献
      2021表面增强拉曼循环肿瘤DNA100~1800[71]
      2023表面增强拉曼血管内皮生成因子200~1800[54]
      2018共振拉曼光谱乳酸和三磷脂酸腺苷500~4000[72]
      2023共聚焦拉曼光谱糖基化400~1800[73]
      2023自发拉曼光谱γ-氨基丁酸0~150[74]
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    Mei-lan GE, Yu-ye WANG, Hai-bin LI, De-gang XU, Jian-quan YAO. Application of Raman spectroscopy in the detection of brain glioma[J]. Chinese Optics, 2024, 17(5): 995

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    Paper Information

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    Received: Jan. 2, 2024

    Accepted: Feb. 26, 2024

    Published Online: Dec. 31, 2024

    The Author Email:

    DOI:10.37188/CO.2024-0003

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