Rapid identification and antimicrobial susceptibility test for Mycobacterium tuberculosis via metabolic inhibition using CAST-R
The combat against multidrug resistant Mycobacterium tuberculosis (MTB) infections demands fast, reliable and sensitive methods for pathogen identification (ID), and phenotype-based antimicrobial susceptibility test (AST) that are broadly applicable over all first- and second-line anti-TB drugs. Here we established Clinical Antimicrobial Susceptibility Test Ramanometry for TB (CAST-R-MTB), and validated it with clinical samples. Based on machine learning, Single-cell Raman spectra (SCRS) can distinguish mycobacteria species from other major pathogens with > 99% accuracy, and distinguish MTB from non-tuberculosis mycobacteria (NTM) with > 99% accuracy. Moreover, by adding a 48h D2O-feeding and drug-exposure step prior to Raman spectra acquisition, CAST-R-MTB reports metabolism-inhibition based AST to four first-line anti-TB drugs (rifampicin, isoniazid, ethambutol and streptomycin) and four second-line anti-TB drugs (moxifloxacin, kanamycin, amikacin, ofloxacin) with 90-100% accuracy for pure cultures. In addition, the multi-omics analyses reveal that the RIF exerts more extensive metabolic disruption than INH and metabolic states exhibit delayed responses. Furthermore, four distinct metabolic clusters reveal the changes of cell subpopulations in single-cell precision with time and drugs, and further explain the molecular basis from the transcriptome aspect suggesting metabolic heterogeneity in different cellular subpopulations. Consequently, the 5-fold acceleration, high resolution, mechanism-revealing output and general applicability to anti-tuberculosis drugs suggest CAST-R-MTB as a powerful tool for diagnosis and treatment of tuberculosis.