Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

Journal: Nature communications
PMID: 31666527

Abstract

Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.

Authors

  • Chi-Sing Ho
    Dept. of Applied Physics, Stanford University, Stanford, CA, USA. csho@alumni.stanford.edu.
  • Neal Jean
    Department of Computer Science, Stanford University, Stanford, CA, USA. Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Catherine A Hogan
    Dept. of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Lena Blackmon
    Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Stefanie S Jeffrey
    3Department of Surgery, Stanford University School of Medicine, MSLS Bldg, 1201 Welch Road, Stanford, CA 94305 USA.
  • Mark Holodniy
    Dept. of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Niaz Banaei
    Department of Pathology, Stanford University School of Medicine, CA, USA; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, CA, USA; Clinical Microbiology Laboratory, Stanford Health Care, CA, USA. Electronic address: nbanaei@stanford.edu.
  • Amr A E Saleh
    Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA. aessawi@stanford.edu.
  • Stefano Ermon
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Jennifer Dionne
    Pumpkinseed Technologies, Palo Alto, CA, United States.

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