Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aeresol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods
In diagnostics of infectious diseases, matrix-assisted laser desorption/ionizati-
on-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for the identification of pathogenic microorganisms. However, to achieve a trustworthy identification from MALDI-TOF MS data, a significant amount of biomass should be considered. The bacterial load that potentially occurs in a sample is therefore routinely amplified by culturing, which is a time-consuming procedure. In this paper, we show that culturing can be avoided by con-ducting MALDI-TOF MS on individual bacterial cells. This results in a more rapid identification of species with an acceptable accuracy. We propose a deep learning architecture to analyze the data and compare its performance with traditional supervised machine learning algorithms. We illustrate our workflow on a large data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species.
Christina Papagiannopoulou (Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium)
René Parchen (BiosparQ, Leiden, the Netherlands)
Peter Rubbens (Flanders Marine Institute (VLIZ), Ostend, Belgium)