Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods.
Christina PapagiannopoulouRené ParchenPeter RubbensWillem WaegemanPublished in: Analytical chemistry (2020)
In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-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 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 data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species.
Keyphrases
- machine learning
- deep learning
- electronic health record
- big data
- mass spectrometry
- artificial intelligence
- infectious diseases
- single cell
- urinary tract infection
- bioinformatics analysis
- convolutional neural network
- minimally invasive
- oxidative stress
- cell cycle arrest
- genetic diversity
- pi k akt
- cell proliferation
- signaling pathway
- quantum dots
- sensitive detection
- endoplasmic reticulum stress