表面增强拉曼光谱法对侵袭性真菌病相关病原体的鉴别  

Identification of Invasive Fungal Disease Related Pathogens Using Surface-Enhanced Raman Spectroscopy

作　　者：王梦凡[1,2] 黄嘉维 刘春龙 洪杰[1] 齐崴 Wang Mengfan;Huang Jiawei;Liu Chunlong;Hong Jie;Qi Wei(School of Chemical Engineering and Technology,Tianjin University,Tianjin 300350,China;School of Life Sciences,Tianjin University,Tianjin 300072,China;Dynamiker Biotechnology(Tianjin)Co.,Ltd.,Tianjin 300457,China;Collaborative Innovation Center of Chemical Science and Engineering(Tianjin),Tianjin 300072,China)

机构地区：[1]天津大学化工学院,天津300350 [2]天津大学生命科学学院,天津300072 [3]丹娜(天津)生物科技股份有限公司,天津300457 [4]天津化学化工协同创新中心,天津300072

出　　处：《天津大学学报（自然科学与工程技术版）》2023年第9期927-934,共8页Journal of Tianjin University：Science and Technology

基　　金：国家自然科学基金资助项目(21621004,22178260);天津市科学技术局重大专项资助项目(20ZXGBSY00040)。

摘　　要：侵袭性真菌病每年在全球范围内造成了大量的病例和死亡,白假丝酵母菌、新生隐球菌和烟曲霉菌是该病常见的3种致病菌.传统真菌的检测方法包括微生物培养、显微镜检与分子生物学法等,均存在操作复杂且耗时耗力等缺点.表面增强拉曼光谱(SERS)具有非标记、灵敏度高、操作简便、可以提供检测物分子指纹信息的特点,在微生物的检测与鉴别中具有独特优势.本研究旨在利用SERS光谱结合数学统计分析建立一种侵袭性真菌病相关病原体的检测及鉴别方法.研究中采用原位还原法在3种表面包覆银纳米粒子以提升拉曼光谱信号的强度和区分度.对于每种真菌,各收集20组拉曼光谱数据.然后分别利用主成分分析(PCA)和正交偏最小二乘判别分析(OPLSDA)对SERS光谱中的生物化学信息进行提取及统计区分.结果发现,在PCA中,第一、第二主成分的累计方差贡献率达到了91.7%;在OPLS-DA中,模型质量参数R^(2)_(X)、R^(2)_(Y)和Q^(2)均大于0.85,由此可见,所得的白假丝酵母菌、新生隐球菌和烟曲霉的SERS光谱辨析度较高,借助PCA和OPLS-DA均能准确地识别这3种真菌病原体并将其区分开.这些结果表明,SERS光谱法结合数学统计分析是一种识别、鉴定真菌病原体的有效工具,本研究为侵袭性真菌病相关病原体的检测及临床疾病诊断提供了一种新思路。Invasive fungal disease(IFD)causes a large number of morbidities and mortality every year.Candida albicans,Cryptococcus neoformans,and Aspergillus fumigatus are the primary pathogens of IFD.Traditional methods for fungal detection include cell culture,microscopy,or molecular biology,all of which are complicated,timeconsuming,and labor-intensive.Surface-enhanced Raman spectroscopy(SERS)has the characteristics of nonlabeling,high sensitivity,simple operation,and fingerprint,which have advantages in the detection and identification of microorganisms.In this study,SERS was applied to the detection and identification of IFD-related pathogens through combining with mathematical statistical analysis.The in-situ growth of AgNPs on the fungal surface was carried out obtain enhanced and well-differentiated SERS spectra of these three species.For each fungus,20 Raman spectra were collected.Then principal components analysis(PCA)and orthogonal partial least-squares discriminant analysis(OPLS-DA)were separately used to extract the biochemical information from the fungal spectra and statisti-cally classify them.The results show that the first two principal components account for 91.7%of the variance in PCA,and the parameters of the model,R^(2)_(X),R^(2)_(Y) and Q^(2),are all greater than 0.85 in OPLS-DA.This indicate the SERS spectral resolution of Candida albicans,Gyptococcus neoformans and Aspergillus fumigatus is high,and the PCA and OPLS-DA can identify and distinguish them effectively.These results show that the SERS detection method is a powerful tool in the identification and discrimination of these fungi.This work provided a promising technique for the detection of IFD-related pathogens as well as an early clinical diagnosis of IFD.

关 键 词：表面增强拉曼光谱 侵袭性真菌病 主成分分析 正交偏最小二乘判别分析 

分 类 号：Q5-33[生物学—生物化学]