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Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods

(2020) ANALYTICAL CHEMISTRY. 92(11). p.7523-7531
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Abstract
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.
Keywords
Analytical Chemistry, DESORPTION IONIZATION-TIME, URINARY-TRACT-INFECTIONS, BACTERIA, CLASSIFICATION, DIAGNOSIS, MS

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MLA
Papagiannopoulou, Christina, et al. “Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods.” ANALYTICAL CHEMISTRY, vol. 92, no. 11, 2020, pp. 7523–31, doi:10.1021/acs.analchem.9b05806.
APA
Papagiannopoulou, C., Parchen, R., Rubbens, P., & Waegeman, W. (2020). Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. ANALYTICAL CHEMISTRY, 92(11), 7523–7531. https://doi.org/10.1021/acs.analchem.9b05806
Chicago author-date
Papagiannopoulou, Christina, René Parchen, Peter Rubbens, and Willem Waegeman. 2020. “Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods.” ANALYTICAL CHEMISTRY 92 (11): 7523–31. https://doi.org/10.1021/acs.analchem.9b05806.
Chicago author-date (all authors)
Papagiannopoulou, Christina, René Parchen, Peter Rubbens, and Willem Waegeman. 2020. “Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods.” ANALYTICAL CHEMISTRY 92 (11): 7523–7531. doi:10.1021/acs.analchem.9b05806.
Vancouver
1.
Papagiannopoulou C, Parchen R, Rubbens P, Waegeman W. Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. ANALYTICAL CHEMISTRY. 2020;92(11):7523–31.
IEEE
[1]
C. Papagiannopoulou, R. Parchen, P. Rubbens, and W. Waegeman, “Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods,” ANALYTICAL CHEMISTRY, vol. 92, no. 11, pp. 7523–7531, 2020.
@article{8665242,
  abstract     = {{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.}},
  author       = {{Papagiannopoulou, Christina and Parchen, René and Rubbens, Peter and Waegeman, Willem}},
  issn         = {{0003-2700}},
  journal      = {{ANALYTICAL CHEMISTRY}},
  keywords     = {{Analytical Chemistry,DESORPTION IONIZATION-TIME,URINARY-TRACT-INFECTIONS,BACTERIA,CLASSIFICATION,DIAGNOSIS,MS}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{7523--7531}},
  title        = {{Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods}},
  url          = {{http://dx.doi.org/10.1021/acs.analchem.9b05806}},
  volume       = {{92}},
  year         = {{2020}},
}

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