MALDI-TOF MS for pathogenic bacteria analysis

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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) is widely used for bacterial identification in clinical laboratories. However, the current technique requires bacterial culture for MS analysis, limiting its application in direct bacterial antimicrobial resistance (AMR) analysis. Researchers have focused on improving bacterial identification and AMR analysis using MALDI-TOF MS, developing new methods for sample pretreatment, data acquisition, and data mining to achieve more accurate and rapid bacterial identification and direct AMR analysis. Microfluidic chips and functional nanomaterials can extract bacterial cells directly from raw materials, shortening or eliminating the need for bacterial culture. Single cell mass spectrometry techniques and advanced data mining strategies based on machine learning and deep learning techniques are expected to play important roles in bacterial identification and AMR analysis. With the development of sample pretreatment methods, MS techniques, and data analysis algorithms, the MALDI-TOF MS technique has the potential to significantly advance the clinical treatment of bacterial infectious diseases.