Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Chi-Sing Ho; Neal Jean; Catherine A Hogan; Lena Blackmon; Stefanie S Jeffrey; Mark Holodniy; Niaz Banaei; Amr A E Saleh; Stefano Ermon; Jennifer Dionne

doi:10.1038/s41467-019-12898-9

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Nat Commun. 2019 Oct 30;10(1):4927. doi: 10.1038/s41467-019-12898-9.

Authors

Chi-Sing Ho^{1

2}, Neal Jean^{3

4}, Catherine A Hogan^{5

6}, Lena Blackmon⁷, Stefanie S Jeffrey⁸, Mark Holodniy^{9

10

11}, Niaz Banaei^{5

6

11}, Amr A E Saleh^{12

13}, Stefano Ermon¹⁴, Jennifer Dionne¹⁵

Affiliations

¹ Dept. of Applied Physics, Stanford University, Stanford, CA, USA. [email protected].
² Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA. [email protected].
³ Dept. of Computer Science, Stanford University, Stanford, CA, USA.
⁴ Dept. of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁵ Dept. of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Clinical Microbiology Laboratory, Stanford Health Care, Stanford, CA, USA.
⁷ Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
⁸ Dept. of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Dept. of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ VA Palo Alto Health Care System, Palo Alto, CA, USA.
¹¹ Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹² Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA. [email protected].
¹³ Dept. of Engineering Mathematics and Physics, Faculty of Engineering, Cairo University, Giza, Egypt. [email protected].
¹⁴ Dept. of Computer Science, Stanford University, Stanford, CA, USA. [email protected].
¹⁵ Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA. [email protected].

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Anti-Bacterial Agents / therapeutic use*
Bacteria / chemistry
Bacteria / classification*
Bacterial Infections / diagnosis*
Bacterial Infections / drug therapy
Bacterial Infections / microbiology
Bacterial Typing Techniques
Candida / chemistry
Candida / classification
Deep Learning*
Enterococcus / chemistry
Enterococcus / classification
Escherichia coli / chemistry
Escherichia coli / classification
Humans
Klebsiella / chemistry
Klebsiella / classification
Logistic Models
Methicillin-Resistant Staphylococcus aureus / chemistry
Methicillin-Resistant Staphylococcus aureus / classification
Microbial Sensitivity Tests
Neural Networks, Computer
Principal Component Analysis
Proteus mirabilis / chemistry
Proteus mirabilis / classification
Pseudomonas aeruginosa / chemistry
Pseudomonas aeruginosa / classification
Salmonella enterica / chemistry
Salmonella enterica / classification
Single-Cell Analysis
Spectrum Analysis, Raman / methods*
Staphylococcus aureus / chemistry
Staphylococcus aureus / classification
Streptococcus / chemistry
Streptococcus / classification
Support Vector Machine

Substances

Anti-Bacterial Agents