Differentiating the growth phases of single bacteria using Raman spectroscopy

In this paper we present a longitudinal study of bacteria metabolism performed with a novel Raman spectrometer system. Longitudinal study is possible with our Raman setup since the overall procedure to localize a single bacterium and collect a Raman spectrum lasts only 1 minute. Localization and detection of single bacteria are performed by means of lensfree imaging, whereas Raman signal (from 600 to 3200 cm-1) is collected into a prototype spectrometer that allows high light throughput (HTVS technology, Tornado Spectral System). Accomplishing time-lapse Raman spectrometry during growth of bacteria, we observed variation in the net intensities for some band groups, e.g. amides and proteins. The obtained results on two different bacteria species, i.e. Escherichia coli and Bacillus subtilis clearly indicate that growth affects the Raman chemical signature. We performed a first analysis to check spectral differences and similarities. It allows distinguishing between lag, exponential and stationary growth phases. And the assignment of interest bands to vibration modes of covalent bonds enables the monitoring of metabolic changes in bacteria caused by growth and aging. Following the spectra analysis, a SVM (support vector machine) classification of the different growth phases is presented. In sum this longitudinal study by means of a compact and low-cost Raman setup is a proof of principle for routine analysis of bacteria, in a real-time and non-destructive way. Real-time Raman studies on metabolism and viability of bacteria pave the way for future antibiotic susceptibility testing.

[1]  Aydogan Ozcan,et al.  Wide-field optical detection of nanoparticles using on-chip microscopy and self-assembled nanolenses , 2013, Nature Photonics.

[2]  D. Naumann,et al.  Identification of medically relevant microorganisms by vibrational spectroscopy. , 2002, Journal of microbiological methods.

[3]  D. R. Cousens,et al.  SNIP, A STATISTICS-SENSITIVE BACKGROUND TREATMENT FOR THE QUANTITATIVE-ANALYSIS OF PIXE SPECTRA IN GEOSCIENCE APPLICATIONS , 1988 .

[4]  Ian P Thompson,et al.  Raman microscopic analysis of single microbial cells. , 2004, Analytical chemistry.

[5]  Olivier Gal,et al.  Direct identification of clinically relevant bacterial and yeast microcolonies and macrocolonies on solid culture media by Raman spectroscopy , 2014, Journal of biomedical optics.

[6]  Anne-Catherine Simon,et al.  A novel method for single bacteria identification by Raman spectroscopy , 2014, Photonics West - Biomedical Optics.

[7]  Andrew J Berger,et al.  Method for automated background subtraction from Raman spectra containing known contaminants. , 2009, The Analyst.

[8]  Jean-Marc Dinten,et al.  Thin wetting film lensless imaging , 2011, BiOS.

[9]  B. Lendl,et al.  Multidimensional information on the chemical composition of single bacterial cells by confocal Raman microspectroscopy. , 2000, Analytical chemistry.

[10]  Emmanuelle Schultz,et al.  Raman microspectrometer combined with scattering microscopy and lensless imaging for bacteria identification , 2013, Photonics West - Biomedical Optics.

[11]  C. P. Allier,et al.  Bacteria detection with thin wetting 
film lensless imaging , 2010, Biomedical optics express.

[12]  S. Lane,et al.  Effect of cefazolin treatment on the nonresonant Raman signatures of the metabolic state of individual Escherichia coli cells. , 2010, Analytical chemistry.

[13]  P. Gemperline,et al.  Identification of single bacterial cells in aqueous solution using confocal laser tweezers Raman spectroscopy. , 2005, Analytical chemistry.

[14]  K. Maquelin,et al.  Rapid Identification of Mycobacteria by Raman Spectroscopy , 2008, Journal of Clinical Microbiology.

[15]  S. Al-Khaldi,et al.  Gene and bacterial identification using high-throughput technologies: genomics, proteomics, and phonemics. , 2004, Nutrition.