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摘要:
脑胶质瘤是一种常见的颅内原发性肿瘤，具有治愈率低、复发率高等特点。脑胶质瘤边界的精准识别是减少患者术后复发、改善愈后状况的重要前提。因此，发展快速、高灵敏度、无标记的脑胶质瘤诊断方法具有重要的临床意义。拉曼光谱技术作为一种指纹谱，能够在分子水平上反映物质的化学和结构信息，已经在脑胶质瘤的定性定位识别中表现出巨大的应用前景。本文首先介绍了不同种类的拉曼光谱技术，其次梳理了拉曼光谱技术在脑胶质瘤中的研究现状，最后对拉曼光谱技术在脑胶质瘤检测中的未来发展进行展望。
Abstract:Brain glioma is a common type of brain tumor with a low cure rate and a high recurrence rate. Precise identification of tumor boundaries is an important prerequisite for reducing recurrence and improving prognosis. Developing a rapid, high-sensitivity and label-free diagnostic method is of crucial clinical significance regarding glioma. Raman spectroscopy can reflect substance’s chemical and structural information at the molecular level due to its fingerprint characteristics. It has already shown excellent prospects for the location and identification of glioma. Firstly, we introduce the different types of Raman spectroscopy technologies in this paper. Secondly, the research status of glioma diagnosis based on Raman spectroscopy is reviewed. Finally, the future development of glioma diagnosis through Raman spectroscopy is prospected.
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Key words:
- Raman spectroscopy /
- brain glioma /
- detection /
- identification
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图 1 Raman光谱在脑胶质瘤检测中的研究现状
Figure 1. The research state of the application of Raman spectroscopy in the detection of brain glioma
图 12 正常组织和不同恶性胶质瘤组织中（a）
1588 cm−1∶1440 cm−1和（b）2934 cm−1∶2885 cm−1 Raman强度比值，其中G0-N为正常脑组织；GI：I级胶质瘤；GII：II级胶质瘤；GIII：III级胶质瘤；GIV：IV级胶质瘤[45]Figure 12. The ratios of (a) I
1588 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]
图 17 （a）小鼠脑胶质母细胞瘤的明场图像，其中黑色线为肿瘤边界，青色为感兴趣区域；（b）感光趣区域内的显微图像；（c）肿瘤和正常脑组织的伪彩色图像，蓝色为细胞核、红色为脂质含量、绿色为红细胞；（d）利用细胞核（蓝色）、脂质含量（红色）绘制的CARS图像；（e）细胞核（蓝色）、脂质含量（红色）、CH3-CH2拉伸（绿色）绘制的CARS图像，NB为正常脑组织，T为肿瘤细胞，WM为白质；（f）肿瘤、白质和正常组织的CARS光谱[47]
Figure 17. (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]
图 18 新鲜小鼠组织的SRS图像。（a）特征峰
2845 cm−1处小鼠脑胼胝体神经元束中标记出髓鞘中含丰富CH2；（b）约1 mm厚脑组织切片CH2的SRS图像；（c）3个独立区域在同一深度处，小鼠耳皮肤中CH2的SRS图像。从左到右依次为：角质层、皮脂腺和皮下脂肪层；（d）角质层有无CH2共振的SRS和CARS图像对比[47]Figure 18. 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]
图 7 （a）皮层暴露后小鼠脑组织；（b）通过聚类分割后的Raman图像。其中，蓝色、青色和黄色为正常组织，红色为血管，灰色为肿瘤组织，黑色为肿瘤边缘；（c）组织的显微照片和Raman图像的叠加[42]
Figure 7. (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]
图 10 手持式接触探针在不同组织处的Raman结果。（a）胶质瘤切除术期间，使用手持式接触探针定位致密肿瘤组织（红色）、浸润性组织（黄色）和周围正常组织位置图；（b）在体致密性肿瘤组织、浸润性组织和正常组织的高波数Raman光谱，（c）每种类型组织的H&E染色显微图片[44]
Figure 10. 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]
图 11 使用SVM算法对正常组织、浸润组织和致密性肿瘤组织的识别结果。（a）脑胶质瘤患者中，正常脑组织、浸润脑组织和致密型肿瘤脑组织中脂质和蛋白的Raman强度比值(
2930 cm−1∶2845 cm−1)的箱线图；（b）基于SVM算法的受试者特性曲线[44]Figure 11. 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]
图 2 （a）和（b）为未染色人胶质母细胞瘤冰冻切片在显微镜下的照片；（c）和（d）为Raman光谱绘制的组织切片的伪彩色图（分别对应于（a）和（b））。红色：主要的肿瘤组织区域，蓝色：坏死组织；黄色：未显示区域（边缘冷冻样品）；（e）和（f）为H&E染色后照片；（g）为（f）图中绿框处放大后的图片[34]
Figure 2. (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]
表 1 3种非线性拉曼光谱技术CARS、SRS和SERS的优缺点对比
Table 1. Comparison of advantages and disadvantages of the three nonlinear Raman spectroscopy techniques: CARS, SRS and SERS
拉曼光谱
技术CARS SRS SERS 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 
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表 2 不同拉曼光谱技术在脑胶质瘤生物标志物中的应用
Table 2. Application of different Raman spectroscopy techniques in the detection of brain glioma biomarkers
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