|
|
|
|
|
|
| Study on Identification of Non-Tuberculosis Mycobacteria Based on Single-Cell Raman Spectroscopy |
| RUAN Zhen1, ZHU Peng-fei3, ZHANG Lei3, CHEN Rong-ze3, LI Xun-rong3, FU Xiao-ting3, HUANG Zheng-gu4, ZHOU Gang4, JI Yue-tong5, LIAO Pu1, 2* |
1. Key Laboratory of Diagnostic Medicine, Ministry of Education; School of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
2. Department of Laboratory Medicine, Chongqing General Hospital, Chongqing 400013, China
3. Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
4. Center Lab, Chongqing Public Health Medical Center, Chongqing 400036, China
5. Qingdao Single-Cell Biotechnology Co., Ltd., Qingdao 266101, China |
|
|
|
|
Abstract Non-tuberculosis mycobacteria (NTM) are the collection of mycobacteria other than Mycobacterium tuberculosis complex (MTC) and Mycobacterium leprosy. The clinical symptoms of NTM are very similar to MTC infection, yet their treatments are different, thus rapid and accurate identification methods of NTM are urgently needed. Single-cell Raman Spectroscopy (SCRS) is label-free, and independent of cultivation, thus it is deemed a rapid and efficient technology with low cost. Here we propose an SCRS based method to identify NTM based on confocal SCRS. We selected six common NTM species in the clinic, Mycobacterium abscessus, Mycobacterium gordonae, Mycobacterium fortuitum, Mycobacterium fortuitum, Mycobacterium avium and Mycobacterium kansasii. The unsupervised low-dimensional visualization t-distribution random neighborhood embedding method for the data structures proved the separability of data in the low-dimensional space. Performance of six commonly classifiers, including Support Vector Machine (SVM), K-Nearest Neighbor method (KNN), Partial Least Square-Discriminate Analysis (PLS-DA), Random Forests (RF), Linear Discriminant Analysis (LDA) and XG Boost was compared, with SVM and LDA achieving an accuracy of 99.4% and 98.8% respectively in NTMs classification. SVM offers 100% classification accuracy for every species, except Mycobacterium kansasii which is slightly lower (97.96%, 48/49), while LDA offers 100% accuracy for each species except Mycobacterium abscessus (95.65%; 22/23) and Mycobacterium gordonae(96.30%, 26/27). Therefore, SCRS combined with SVM can accurately classify NTMs and thus provide a new tool for the rapid diagnosis of NTM.
|
|
Received: 2021-03-25
Accepted: 2021-07-04
|
|
|
|
Corresponding Authors:
LIAO Pu
E-mail: liaopu2015@163.com
|
|
[1] Tortoli E, Fedrizzi T, Meehan C J, et al. Infection, Genetics and Evolution, 2017, 56: 19.
[2] Baldwin S L, Larsen S E, Ordway D, et al. PLOS Neglected Tropical Diseases, 2019, 13(2): e0007083.
[3] Huang W C, Yu M C, Huang Y W. Journal of the Formosan Medical Association, 2020, 119: 532.
[4] WANG Li-xia, CHENG Shi-ming, CHEN Ming-ting, et al(王黎霞, 成诗明, 陈明亭, 等). Chinese Journal of Antituberculosis（中国防痨杂志）, 2012，(8): 485.
[5] Mcgrath E E, Anderson P B. The Lancet, 2007, 370(9581): 28.
[6] Griffith D E, Aksamit T, Brown-Elliott B A, et al. American Journal of Respiratory and Critical Care Medicine, 2007, 175(4): 367.
[7] Brown-Elliott B A, Nash K A, Wallace R J Jr. Clinical Microbiology Reviews, 2012, 25(3): 545.
[8] Seagar A L, Prendergast C, Emmanuel F X, et al. Journal of Medical Microbiology, 2008, 57: 605.
[9] Kim S, Park E M, Kwon O J, et al. The International Journal of Tuberculosis and Lung Disease, 2008, 12(11): 1344.
[10] LIANG Hao-yue, CHENG Xue-lian, YANG Wan-zhu, et al(梁昊岳, 程雪莲, 杨晚竹, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(12): 3670.
[11] RUAN Zhen, ZHU Peng-fei, FU Xiao-ting, et al(阮 真, 朱鹏飞, 付晓婷，等). Microbiology China（微生物学通报）. doi：10.13344/j.microbiol.china.200703.
[12] Petry R, Schmitt M, Popp J. Chemphyschem, 2003, 4(1): 14.
[13] Montanari L B, Martins C H G, De Mello C A, et al. Journal of Water and Health, 2018, 16(2): 311.
[14] Harz M, Kiehntopf M, Stockel S, et al. Journal of Biophotonics, 2009, 2(1-2): 70.
[15] Jorgensen J H, Pfaller M A. Manual of Clinical Microbiology. American Society for Microbiology, 2015.
[16] He Y, Wang X, Ma B, et al. Biotechnology Advances, 2019, 37(6): 107388.
[17] Xu T, Gong Y, Su X, et al. Small, 2020, 16(30): e2001172. |
| [1] |
WANG Gan-lin1, LIU Qian1, LI Ding-ming1, YANG Su-liang1*, TIAN Guo-xin1, 2*. Quantitative Analysis of NO-3，SO2-4，ClO-4 With Water as Internal Standard by Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1855-1861. |
| [2] |
FU Qiu-yue1, FANG Xiang-lin1, ZHAO Yi2, QIU Xun1, WANG Peng1, LI Shao-xin1*. Research Progress of Pathogenic Bacteria and Their Drug Resistance
Detection Based on Surface Enhanced Raman Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1339-1345. |
| [3] |
FAN Xian-guang1, 2, HUANG Yan-rui1, LIU Long1, XU Ying-jie1, WANG Xin1, 2*. An Interpolation Method for Raman Imaging Using Voigt Function[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1478-1483. |
| [4] |
WANG Zi-xiong, XU Da-peng*, ZHANG Yi-fan, LI Jia-jia. Research Progress of Surface-Enhanced Raman Scattering Detection Analyte Molecules[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 341-349. |
| [5] |
FU Juan, MO Jia-mei, YU Yi-song, ZHANG Qing-zong, CHEN Xiao-li, CHEN Pei-li, ZHANG Shao-hong, SU Qiu-cheng*. Experimental Study on the Structure Characteristics of CO2 in Gas Hydrate by Solid-State Nuclear Magnetic Resonance and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 464-469. |
| [6] |
CHEN Fu-shan1, WANG Gao-min1, WU Yue1, LU Peng2, JI Zhe1, 2*. Advances in the Application of Confocal Raman Spectroscopy in Lignocellulosic Cell Walls Pretreatment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 15-19. |
| [7] |
ZHAO Ying1，2, LI Xiao-peng2, CUI Fei-peng2, LIU Jia1，2, LI Xiao-jia1，2*. Development and Application of Fluorescence Suppression Based on Multi Wavelength Raman Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 86-92. |
| [8] |
ZHANG Lin1, WEN Bao-ying2, LIU Wei-wei1, FU Wen-xiang1, KONG Jing-lin1*, LI Jian-feng2*. Rapidly Detection of Chemical Warfare Agent Simulants by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 110-114. |
| [9] |
WANG Yuan1, ZHANG Zhen2*, GUO Yuan2, 3. A Simplified Method of Microscopic Polarizability Tensor Differential of Hyper-Raman Spectroscopy Based on Experimental Correction Bond Additivity Model-C2v Symmetry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 124-129. |
| [10] |
SHI Si-qian, YANG Fang-wei, YAO Wei-rong, YU Hang, XIE Yun-fei*. Rapid Detection of Levamisole Residue in Pork by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3759-3764. |
| [11] |
ZHAO Sen, LIANG Xiao-tian, YU Meng-ke, CAI Jing*. Study on the Inspection of Shooting Residues by Micro-Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3142-3146. |
| [12] |
MA Jin-ge1, YANG Qiao-ling2, DENG Xiao-jun1*, SHI Yi-yin1, GU Shu-qing1, ZHAO Chao-min1, YU Yong-ai3, ZHANG Feng4. On-Site Rapid and Non-Destructive Identification Method for Imported Bulk Olive Oil Quality Based on Portable Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2789-2794. |
| [13] |
WANG Yi1, 2, LI Chang-rong1, 2*, ZHUANG Chang-ling1, 2. Study on Alumina/Lanthanum Oxide X-Ray Diffraction and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2480-2483. |
| [14] |
TAN Hui-jiao, DANG Rui*. Comparison of Raman Spectroscopy and Color Difference in the Light-Induced Color Damage Evaluation of Cultural Heritages With Silk[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2474-2479. |
| [15] |
GUI Bo1, 2, YANG Yu-dong1, ZHAO Qian1, 2, SHI Meng1, MAO Hai-yang1, 3*, WANG Wei-bing1, CHEN Da-peng1, 3. A SERS Substrate for On-Site Detection of Trace Pesticide Molecules Based on Parahydrophobic Nanostructures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2499-2504. |
|
|
|
|
