Fast pathogen identification using single-cell MALDI-ATOF mass spectrometry data and deep learning methods.

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Abstract

In diagnostics of infectious diseases, MALDI-TOF 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 conducting 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 dataset that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85\% in discriminating five different species.