Detecting Phenotypically Resistant Mycobacterium tuberculosis Using Wavelength Modulated Raman Spectroscopy

  • Vincent O. Baron
  • Mingzhou Chen
  • Simon O. Clark
  • Ann Williams
  • Kishan Dholakia
  • Stephen H. Gillespie
Part of the Methods in Molecular Biology book series (MIMB, volume 1736)


Raman spectroscopy is a non-destructive and label-free technique. Wavelength modulated Raman (WMR) spectroscopy was applied to investigate Mycobacterium tuberculosis cell state, lipid rich (LR) and lipid poor (LP). Compared to LP cells, LR cells can be up to 40 times more resistant to key antibiotic regimens. Using this methodology single lipid rich (LR) from lipid poor (LP) bacteria can be differentiated with both high sensitivity and specificity. It can also be used to investigate experimentally infected frozen tissue sections where both cell types can be differentiated. This methodology could be utilized to study the phenotype of mycobacterial cells in other tissues.

Key words

Raman spectroscopy Mycobacteria Phenotypic resistance Lipids 



This work was supported by the PreDiCT-TB consortium [IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution ( This work was supported by the Department of Health, UK. The views expressed in this chapter are those of the authors and not necessarily those of the Department of Health. This work was supported by the UK Engineering and Physical Sciences Research Council (Grant code EP/J01771X/1) and a European Union FAMOS project (FP7 ICT, 317744). Authors acknowledge the loan of a laser from M Squared Lasers.


  1. 1.
    Gillespie SH, Crook AM, McHugh TD et al (2014) Four-month moxifloxacin-based regimens for drug-sensitive tuberculosis. N Engl J Med 371(17):1577–1587CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Merle CS, Fielding K, Sow OB et al (2014) A four-month gatifloxacin-containing regimen for treating tuberculosis. N Engl J Med 371(17):1588–1598CrossRefPubMedGoogle Scholar
  3. 3.
    Jindani A, Harrison TS, Nunn AJ et al (2014) High-dose rifapentine with moxifloxacin for pulmonary tuberculosis. N Engl J Med 371(17):1599–1608CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Phillips PP, Mendel CM, Burger DA et al (2016) Limited role of culture conversion for decision-making in individual patient care and for advancing novel regimens to confirmatory clinical trials. BMC Med 14:19CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Daniel J, Deb C, Dubey VS et al (2004) Induction of a novel class of diacylglycerol acyltransferases and triacylglycerol accumulation in Mycobacterium tuberculosis as it goes into a dormancy-like state in culture. J Bacteriol 186(15):5017–5030CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Garton NJ, Waddell SJ, Sherratt AL et al (2008) Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum. PLoS Med 5(4):e75CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Deb C, Lee CM, Dubey VS et al (2009) A novel in vitro multiple-stress dormancy model for Mycobacterium tuberculosis generates a lipid-loaded, drug-tolerant, dormant pathogen. PLoS One 4(6):e6077CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Baek SH, Li AH, Sassetti CM (2011) Metabolic regulation of mycobacterial growth and antibiotic sensitivity. PLoS Biol 9(5):e1001065CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Hammond RJ, Baron VO, Oravcova K et al (2015) Phenotypic resistance in mycobacteria: is it because I am old or fat that I resist you? J Antimicrob Chemother 70(10):2823–2827CrossRefPubMedGoogle Scholar
  10. 10.
    Maquelin K, Kirschner C, Choo-Smith LP et al (2002) Identification of medically relevant microorganisms by vibrational spectroscopy. J Microbiol Methods 51(3):255–271CrossRefPubMedGoogle Scholar
  11. 11.
    Buijtels PCAM, Willemse-Erix HFM, Petit PLC et al (2008) Rapid identification of mycobacteria by Raman spectroscopy. J Clin Microbiol 46(3):961–965CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Pahlow S, Meisel S, Cialla-May D et al (2015) Isolation and identification of bacteria by means of Raman spectroscopy. Adv Drug Deliv Rev 89:105–120CrossRefPubMedGoogle Scholar
  13. 13.
    Canetta E, Mazilu M, De Luca AC et al (2011) Modulated Raman spectroscopy for enhanced identification of bladder tumor cells in urine samples. J Biomed Opt 16(3):037002CrossRefPubMedGoogle Scholar
  14. 14.
    De Luca AC, Mazilu M, Riches A et al (2010) Online fluorescence suppression in modulated Raman spectroscopy. Anal Chem 82(2):738–745CrossRefPubMedGoogle Scholar
  15. 15.
    Mazilu M, De Luca AC, Riches A et al (2010) Optimal algorithm for fluorescence suppression of modulated Raman spectroscopy. Opt Express 18(11):11382–11395CrossRefPubMedGoogle Scholar
  16. 16.
    Baron VO, Chen M, Clark SO et al (2017) Label-free optical vibrational spectroscopy to detect the metabolic state of M. tuberculosis cells at the site of disease. Sci Rep 7(1):9844Google Scholar
  17. 17.
    Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26 (3):303–304Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Vincent O. Baron
    • 1
  • Mingzhou Chen
    • 2
  • Simon O. Clark
    • 3
  • Ann Williams
    • 3
  • Kishan Dholakia
    • 2
  • Stephen H. Gillespie
    • 1
  1. 1.School of MedicineUniversity of St AndrewsSt AndrewsUK
  2. 2.SUPA, School of Physics and AstronomyUniversity of St AndrewsSt AndrewsUK
  3. 3.National Infectious Service, Public Health EnglandSalisburyUK

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