|
|
|
|
|
|
| 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 Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
| [2] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1，ZHAO Yan-jie1*，NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
| [3] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
| [4] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
| [5] |
SU Xin-yue1, MA Yan-li2, ZHAI Chen3, LI Yan-lei4, MA Qian-yun1, SUN Jian-feng1, WANG Wen-xiu1*. Research Progress of Surface Enhanced Raman Spectroscopy in Quality and Safety Detection of Liquid Food[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2657-2666. |
| [6] |
LI Xin-li1, CONG Li-li2, XU Shu-ping2, LI Su-yi1*. Cell Growth Analysis Method Based on Spectral Clustering and Single-Cell Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2832-2836. |
| [7] |
LIU Guo-peng1, YOU Jing-lin1*, WANG Jian1, GONG Xiao-ye1, ZHAO Yu-fan1, ZHANG Qing-li2, WAN Song-ming2. Application of Aerodynamic Levitator Laser Heating Technique: Microstructures of MgTi2O5 Crystal and Melt by in-situ Superhigh Temperature Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2507-2513. |
| [8] |
LAI Chun-hong*, ZHANG Zhi-jun, WEN Jing, ZENG Cheng, ZHANG Qi. Research Progress in Long-Range Detection of Surface-Enhanced Raman Scattering Signals[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2325-2332. |
| [9] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
| [10] |
MA Zhong-kai1, LI Mao-gang2, YAN Chun-hua1, LIU Hao-sen1, TAO Shu-hao1, TANG Hong-sheng2, ZHANG Tian-long2*, LI Hua1, 2*. Application of Raman Spectroscopy Combined With Partial Least Squares Method in Rapid Quantitative Analysis of Diesel n-Butanol[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2153-2157. |
| [11] |
CHENG Chang-hong1, XUE Chang-guo1*, XIA De-bin2, TENG Yan-hua1, XIE A-tian1. Preparation of Organic Semiconductor-Silver Nanoparticles Composite Substrate and Its Application in Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2158-2165. |
| [12] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
| [13] |
LI Jia-jia, XU Da-peng *, WANG Zi-xiong, ZHANG Tong. Research Progress on Enhancement Mechanism of Surface-Enhanced Raman Scattering of Nanomaterials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1340-1350. |
| [14] |
HUANG Xiao-wei1, ZHANG Ning1, LI Zhi-hua1, SHI Ji-yong1, SUN Yue1, ZHANG Xin-ai1, ZOU Xiao-bo1, 2*. Detection of Carbendazim Residue in Apple Using Surface-Enhanced Raman Scattering Labeling Immunoassay[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1478-1484. |
| [15] |
LU Yan-hua, XU Min-min, YAO Jian-lin*. Preparation and Photoelectrocatalytic Properties Study of TiO2-Ag
Nanocomposites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1112-1116. |
|
|
|
|
