Identification of circulating tumour cells in nasopharyngeal carcinoma based on microconfocal Raman spectroscopy with multi-target aptamer microfluidics
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摘要:
目的 建立一种基于显微共聚焦拉曼光谱技术的鼻咽癌循环肿瘤细胞鉴定方法，实现对鼻咽癌循环肿瘤细胞的无接触、无标记检测。 方法 体外培养人T淋巴细胞白血病细胞株Jurkat和鼻咽癌细胞株Sune1，利用EpCAM、CD44、EGFR和波形蛋白适配体组装的纳米微流控芯片技术识别和亲和捕获单细胞，并使用显微共聚焦拉曼光谱技术对单细胞进行鉴定，得到单细胞拉曼光谱。以两种机器学习算法：支持向量机和线性判别分析构建分类器，对单细胞拉曼光谱进行建模分析。 结果 共采集得到474个Jurkat细胞与Sune1细胞的单细胞拉曼光谱，其峰位分析结果显示：与Jurkat细胞相比，Sune1细胞中的腺嘌呤、胸腺嘧啶和鸟嘌呤含量显著下降（P＜0.0001）；羟脯氨酸、蛋白质和脂类的含量显著上升（P＜0.0001），差异有统计学意义。线性判别分析的预测准确率较高，能够有效区分两种细胞，预测准确率高达98.31%。 结论 本研究基于显微共聚焦拉曼光谱技术和机器学习算法，建立了一种可能适用于鉴定鼻咽癌循环肿瘤细胞的方法，可对鼻咽癌的临床微创诊断产生积极作用。 Abstract:Objective To establish a method for the identification of nasopharyngeal carcinoma circulating tumor cells based on microconfocal Raman spectroscopy, in order to realize the non-contact and marker-free detection of nasopharyngeal carcinoma circulating tumor cells. Methods Human T lymphocytic leukemia cell line Jurkat and nasopharyngeal carcinoma cell line Sune1 were cultured in vitro, and single cells were identified and affinity captured by nanomicronic chip technology assembled by EpCAM, CD44, EGFR and vimentin aptamer. The single cells were identified by microconfocal Raman spectroscopy to obtain single cell Raman spectroscopy. Two machine learning algorithms, support vector machine and linear discriminant analysis, were used to construct a classifier to model and analyze single-cell Raman spectra. Results Single-cell Raman spectra of 474 Jurkat cells and Sune1 cells were collected. Peak location analysis results showed that the contents of adenine, thymine and guanine in Sune1 cells decreased significantly compared with Jurkat cells. The contents of hydroxyproline, protein and lipids increased significantly, and the differences were statistically significant. Linear discriminant analysis had a high prediction accuracy, which could effectively distinguish the two types of cells, and the prediction accuracy was as high as 98.31%. Conclusion Based on microconfocal Raman spectroscopy and machine learning algorithms, this study established a method that may be suitable for the identification of nasopharyngeal carcinoma circulating tumor cells, which has a positive effect on the clinical minimally invasive diagnosis of nasopharyngeal carcinoma. -
图 2 外周血模拟样本的鼻咽癌CTCs分离鉴定（×40）
Figure 2. Isolation and identification of nasopharyngeal carcinoma CTCs in simulated peripheral blood samples(×40).
图 3 装置分选结果的免疫荧光鉴别（×20）
Figure 3. Immunofluorescence identification of device sorting results(×20).
图 4 Jurkat和Sune1两种细胞系PCA分析图
Figure 4. Plot of PCA analysis of two cell lines, Jurkat and Sune1.
图 5 Jurkat细胞和Sune1细胞拉曼差异峰谱及生物标志物拉曼差异峰谱定量比较
Figure 5. Quantitative comparison of Raman difference peak spectra(A) and biomarker Raman difference peak spectra of Jurkat and Sune1 cells(B-G). ****P<0.0001.
表 1 拉曼波数的生物学归属
Table 1. Biological assignment of Raman wavenumbers
Raman wavenumber (cm-1) Biomolecule assignment 730 Adenosine ring respiration 752 v15 (Porphyrin respiration) 784 Phosphodiester; Cytosine 830 Polyhydroxybutyrate 853 Tyrosine cyclic respiratory mode and Proline cyclic C-C stretching 876 v (Carbon-Carbon), Hydroxyproline (Protein partitioning) 957 Hydroxyapatite/Carotenoids/Cholesterol 1003 Phenylalanine, Carbon-Carbon skeleton 1030 Collagen with phenylalanine 1094 Deoxyribonucleic acid 1126 Lipid acyl backbone (Trans conformation) 1340 Nucleotide 1375 Thymine, Adenine, Guanine 1446 CH2 bending of proteins and Lipids 1487 Guanine 1576 Nucleotide 1655 Amide Ⅰ (Protein C=O stretching mode, α-helical conformation)/C= C lipid stretching 
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表 2 LDA模型区分Jurkat细胞和Sune1细胞
Table 2. LDA model distinguishes between Jurkat and Sune1 cells
LDA Reference Jurkat Sune1 Jurkat(n) 70 0 Sune1(n) 1 47 Sensitivity (%) 98.59 - Specificity (%) - 100.00
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表 3 Jurkat细胞和Sune1细胞的特征拉曼峰
Table 3. Characteristic Raman peaks of Jurkat cells and Sune1 cells
Wave number(cm-1) Molecular vibration Jurkat cell Sune1 cell Biological significance 730 Adenine respiration High Low It is related to the nucleic acid content. 1375 Lipid CH2 bending High Low It is related to the lipid composition of the cell membrane. 784 Respiration by guanine High Low It is related to the nucleic acid content. 1446 Lipid CH2 bending Low High It may be related to changes in lipid composition. 876 Carbohydrates or amino acids Low High It may be related to glycosylation or protein structure changes. 1655 C=O stretching vibration (alpha helix) Low High It may be related to protein secondary structure changes.
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