Pseudotargeted Metabolomic Fingerprinting and Deep Learning for Identification and Visualization of Common Pathogens

Frontiers in Microbiology

Yu Ding¹  Honghui Zhu²  Jumei Zhang²  Moutong Chen²  Qingping Wu²  Xianhu Wei²  Youxiong Zhang²  Liang Xue²  Minling Chen²  Ying Feng⁴  Lanyan Huang⁴  Guoyang Chen⁴ 

[1] Department of Food Science and Technology, Institute of Food Safety and Nutrition, Jinan University, Guangzhou, China;Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China;Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou, China;University of Chinese Academy of Sciences, Beijing, China;

关键词: pseudotargeted metabolomic;    deep learning;    LC–QQQ–MS;    variational autoencoder (VAE);    convolutional neural network (CNN);   

DOI  :  10.3389/fmicb.2022.830832

来源: DOAJ

【 摘 要 】

Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) spectrometry fingerprinting has reduced turnaround times, costs, and labor as conventional procedures in various laboratories. However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures. Metabolomes can identify these strains by amplifying the minor differences because they are directly related to the phenotype. The pseudotargeted metabolomics method has the advantages of both non-targeted and targeted metabolomics. It can provide a new semi-quantitative fingerprinting with high coverage. We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen. A variational autoencoder framework was performed to identify and classify pathogenic bacteria and achieve their visualization, with prediction accuracy exceeding 99%. Therefore, this technology will be a powerful tool for rapidly and accurately identifying pathogens.

【 授权许可】

Unknown