Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Journal: Proceedings of the National Academy of Sciences of the United States of America
PMID: 38861604

Abstract

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Authors

  • Babatunde Ogunlade
    Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305.
  • Loza F Tadesse
    Department of Bioengineering, Stanford University School of Medicine and School of Engineering, Stanford, CA 94305.
  • Hongquan Li
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
  • Nhat Vu
    Pumpkinseed Technologies, Inc., Palo Alto, CA 94306.
  • 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.
  • Amy K Barczak
    The Ragon Institute of Mass General, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139.
  • Amr A E Saleh
    Dept. of Materials Science and Engineering, Stanford University, Stanford, CA, USA. aessawi@stanford.edu.
  • Manu Prakash
    Department of Bioengineering, Stanford University School of Medicine and School of Engineering, Stanford, CA 94305.
  • Jennifer A Dionne
    Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305.

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