MALDI-TOF mass spectrometry and Artificial Intelligence applications in food safety and public health CALL 2024

MALDI-TOF mass spectrometry is currently the gold-standard for rapid microbial identification. The promising use of MALDI-TOF for bacterial typing, direct diagnostics, and rapid identification of antibiotic resistance is well reported, as well as the use of Artificial Intelligence (AI) techniques in the field of microbiology for the construction of informative and predictive models from complex biological data. The protein mass spectra profiles obtained from MALDI-TOF in combination with the training of AI algorithms could therefore potentially assess intra and inter species genetic diversity among strains. Nonetheless, its real application in food safety is scarce and, hitherto, it suffers from several drawbacks. This research topic aims to: i) develop rapid diagnostic methods for the detection of foodborne pathogens directly from food; ii) investigate the strains’ virulence and antimicrobial resistance; iii) create a mass spectra profiles’ database of pathogenic strains isolated from human, animal, food and environment; iv) develop training of Machine Learning algorithms for typing screening studies. The research will better clarify the role that MALDI-TOF could play in surveillance systems and will create a multidisciplinary and low-cost network, that already operates at the National Health System in the interfaces between environment and health (both human and animal) by combining existing activities into a synergistic approach able to optimize the resources.

Five publications related to the Research Topic for the candidate interview: 

  1. Sauget M, Valot B, Bertrand X, Hocquet D. Can MALDI-TOF Mass Spectrometry reasonably type bacteria? Trends Microbiol. 2017: 25(6):447-455. https://doi.org/10.1016/j.tim.2016.12.006
  2. Huber CA, Reed SJ, Paterson DL. Bacterial sub-species typing using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry: what is promising? Curr. Issues Mol. Biol. 2021: 43,749-757. https://doi.org/10.3390/cimb43020054
  3. Feucherolles M, Nennig M, Becker S.L., Martiny D, Losch S, Penny C, Cauchie HM, Ragimbeau C. Investigation of MALDI-TOF Mass Spectrometry for assessing the molecular diversity of Campylobacter jejuni and comparison with MLST and cgMLST: a Luxembourg One-Health Study. Diagnostics. 2021: 11,1949. https://doi.org/10.3390/diagnostics11111949
  4. Feucherolles M, Nennig M, Becker SL, Martiny D, Losch S, Penny C, Cauchie H-M and Ragimbeau C (2022) Combination of MALDI-TOF Mass Spectrometry and Machine Learning for rapid antimicrobial resistance screening: the case of Campylobacter spp. Front. Microbiol. 12:804484. https://doi:10.3389/fmicb.2021.804484
  5. Weis CV, Jutzeler CR, Borgwardt K. "Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review." Clinical Microbiology and Infection. 2020, 26:1310-1317. https://doi.org/10.1016/j.cmi.2020.03.014

Contact person

Prof. Federica Giacometti

Department of Animal Medicine, Production and Health (https://www.maps.unipd.it/)
University of Padova
e-mail: federica.giacometti@unipd.it