Tuberculosis (TB) is the world's deadliest infectious disease, with 1.5
million annual deaths and half a million annual infections. Rapid TB diagnosis
and antibiotic susceptibility testing (AST) are critical to improve patient
treatment and to reduce the rise of new drug resistance. Here, we develop a
rapid, label-free approach to identify Mycobacterium tuberculosis (Mtb) strains
and antibiotic-resistant mutants. We collect over 20,000 single-cell Raman
spectra from isogenic mycobacterial strains each resistant to one of the four
mainstay anti-TB drugs (isoniazid, rifampicin, moxifloxacin and amikacin) and
train a machine-learning model on these spectra. On dried TB samples, we
achieve > 98% classification accuracy of the antibiotic resistance profile,
without the need for antibiotic co-incubation; in dried patient sputum, we
achieve average classification accuracies of ~ 79%. We also develop a low-cost,
portable Raman microscope suitable for field-deployment of this method in
TB-endemic regions