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Ogunlade B, Tadesse LF, Li H, Vu N, Banaei N, Barczak AK, Saleh AAE, Prakash M, Dionne JA. Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy. ArXiv 2024:arXiv:2306.05653v2. [PMID: 37332564 PMCID: PMC10274949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year1. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture2,3, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) 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 in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 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 <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.
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Affiliation(s)
- Babatunde Ogunlade
- Department of Materials Science and Engineering, Stanford University; Stanford, 94305, CA, USA
| | - Loza F. Tadesse
- Department of Bioengineering, Stanford University School of Medicine and School of Engineering; Stanford, 94305, CA, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology; Cambridge, 02142, MA, USA
- The Ragon Institute, Massachusetts General Hospital; Cambridge, 02139, MA, USA
| | - Hongquan Li
- Department of Applied Physics, Stanford University; Stanford, 94305, CA, USA
| | - Nhat Vu
- Pumpkinseed Technologies, Inc; Palo Alto, 94306, CA, USA
| | - Niaz Banaei
- Department of Pathology, Stanford University School of Medicine; Stanford, 94305, CA, USA
| | - Amy K. Barczak
- The Ragon Institute, Massachusetts General Hospital; Cambridge, 02139, MA, USA
- Division of Infectious Diseases, Massachusetts General Hospital; Boston, 02114, MA, USA
- Department of Medicine, Harvard Medical School; Boston, 02115, MA, USA
| | - Amr. A. E. Saleh
- Department of Materials Science and Engineering, Stanford University; Stanford, 94305, CA, USA
- Department of Engineering Mathematics and Physics, Cairo University; Giza, 12613, Egypt
| | - Manu Prakash
- Department of Bioengineering, Stanford University School of Medicine and School of Engineering; Stanford, 94305, CA, USA
| | - Jennifer A. Dionne
- Department of Materials Science and Engineering, Stanford University; Stanford, 94305, CA, USA
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine; Stanford, 94035, CA, USA
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