口腔医学研究

口腔医学研究 ›› 2021, Vol. 37 ›› Issue (9): 794-799.DOI: 10.13701/j.cnki.kqyxyj.2021.09.006

• 牙体牙髓病学研究 • 上一篇    下一篇

基于单细胞拉曼技术的口腔病原微生物快速鉴别研究

李姗姗1, 孙雁斐1, 郭艺2, 杨芳1,3*   

  1. 1.青岛大学口腔医学院 山东 青岛 266003;
    2.同济大学嵌入式系统与服务计算教育部重点实验室 上海 200092;
    3.青岛市市立医院口腔医学中心 山东 青岛 266071
  • 收稿日期:2021-02-18 出版日期:2021-09-28 发布日期:2021-09-16
  • 通讯作者: *杨芳,E-mail:yangf82@sina.com
  • 作者简介:李姗姗(1990~ ),山东青岛人,硕士,医师,研究方向:口腔医学,龋病,口腔微生物。

Rapid Identification of Oral Pathogens Microorganism Based on Single-cell Raman

LI Shanshan1, SUN Yanfei1, GUO Yi2, YANG Fang1,3*   

  1. 1. School of Stomatology, Qingdao University, Qingdao 266003, China;
    2. Department of Computer Science and Technology, the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 200092, China;
    3. Stomatology Center, Qingdao Municipal Hospital, Qingdao 266071, China
  • Received:2021-02-18 Online:2021-09-28 Published:2021-09-16

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摘要： 目的: 评价单细胞拉曼技术对3种口腔病原微生物的快速分类效能。方法: (1)培养变异链球菌UA159、白色念珠菌ATCC10231和粪肠球菌ATCC29212至稳定期,绘制生长曲线评估生长状态;(2)测量对数和稳定期菌株单细胞拉曼图谱,使用主成分和随机森林分析方法分析光谱。结果: (1)3个菌株的对数期和稳定期均可被拉曼技术快速区分。准确率分别为:99.6%、99.86%、99.60%;(2)单细胞拉曼技术能够快速精确区分稳定期的3种实验菌株,准确率高达99.68%;(3)通过随机森林算法分析得到3种稳定期实验菌株之间的拉曼差异峰分别为:1126~1128 cm-1、736~744 cm-1、1330~1440 cm-1、778~785 cm-1、1001~1003 cm-1和1431~1481 cm-1。其生物学标志分别为:蛋白质、胸腺嘧啶、脂质、胞嘧啶、尿嘧啶、苯丙氨酸、蛋白质。结论: 单细胞拉曼光谱可高效区分物种的生长时期和菌株类别。

关键词: 单细胞拉曼, 口腔病原微生物, 鉴定, 随机森林, 主成分分析

Abstract: Objective: To evaluate the efficiency of single-cell Raman technique for rapid classification of three kinds of oral pathogens microorganism. Methods: Streptococcus mutans UA159, Candida albicans ATCC10231, and Enterococcus faecalis ATCC29212 were cultured in each medium respectively until the stationary phase. Cells were sampled and treated as below: (1) inoculated culture was monitored by the optical density at 600 nm to measure bacterial growth. (2) inoculated culture was sampled at logarithmic and stationary phase respectively to measure the Raman spectra. Results: Firstly, the logarithmic phase and stationary phase of three strains could be rapidly distinguished by Raman technique. And the classification accuracy rates of random forest were 99.6%, 99.86%, and 99.60%. Secondly, Raman based method could discriminate different oral microbes in stationary phase, and the specificity in classification was 99.68%. Lastly, Raman biomarkers for the classification were 1126-1128 cm-1 (protein), 736-744 cm-1 (thymine), 1330-1440 cm-1 (lipid), 778-785 cm-1 (cytosine, uracil), 1001-1003 cm-1 (phenylalanine), and 1431-1481 cm-1 (Marker protein 1451), respectively. Conclusion: Single-cell Raman could be used to differentiate between growth phases of a single species and strain types of difference species.

Key words: single-cell raman spectroscopy, oral pathogens microorganism, identification, random forest, PCA