质谱法
化学
细菌细胞结构
微生物培养
基质辅助激光解吸/电离
样品制备
临床微生物学
细菌生长
细菌分类学
鉴定(生物学)
色谱法
细菌
计算生物学
微生物学
解吸
生物
生物化学
植物
基因
吸附
有机化学
遗传学
16S核糖体RNA
标识
DOI:10.1016/j.ijms.2022.116935
摘要
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) has been widely used for bacterial identification in clinical laboratories. However, the current technique needs bacterial culture to obtain purified single colonies for MS analysis. It is also limited in direct bacterial antimicrobial resistance (AMR) analysis. The two limitations restrict fast clinical diagnosis of bacterial infectious diseases and the choice of suitable antibiotic drugs in a timely manner. In the past years, we focus on bacterial identification and bacterial AMR analysis by MALDI-TOF MS, and developed a number of new methods with respect to sample pretreatment, mass spectrometry data acquisition and mass spectrometry data mining, with the aim of more accurate and more rapid bacterial identification as well as direct bacterial AMR analysis. Microfluidic chips and functional nanomaterials can be used to extract bacterial cells directly from the raw materials to shorten or even avoid the bacterial culture step. In some sample, the total bacterial cell amount is limited and hence it is necessary to further enhance the MS analysis sensitivity, and the single cell mass spectrometry techniques may play an important role in bacterial identification in the future. In view of bacterial AMR analysis, it is necessary to collect more molecular information of bacterial cells by mass spectrometry and to develop advanced data mining strategies based on machine learning and deep learning techniques. It is expected that the MALDI-TOF MS technique can further advance clinical treatment of bacterial infection diseases in combination with the development of sample pretreatment methods, MS techniques and data analysis algorithms.
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