Identification of five Listeria species based on infrared spectra (FTIR) using macrosamples is superior to a microsample approach.

Microorganisms can be identified using both macrosamples and microsamples based on infrared spectra (FTIR). This work compares the identification of the five closely related Listeria species L. innocua, L. ivanovii, L. monocytogenes, L. seeligeri, and L. welshimeri using both methods. The overall identification success for 25 strains was 92.8% for the former and 79.2% for the latter methods, respectively. The worst performances of the microsample method were obtained for L. innocua, L. ivanovii, and L. monocytogenes, while L. seeligeri and L. welshimeri did not show significant differences between the techniques. Identification success was mainly influenced by the age of the cells and the spatial heterogeneity of the microcolonies, as analyzed by the microsample method. Spectra of Listeria cells near the stationary phase exhibited more species-specific markers and thus allowed for better discrimination than spectra of growing cells. Furthermore, the heterogeneity of cell composition at different locations in microcolonies of L. innocua, L. ivanovii and L. monocytogenes resulted in limited discrimination success of the microsample method. We conclude that, at least in the case of Listeria, the macrosample method is superior to the microsample method, although the latter is the faster technique.

Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra.

A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.

Rapid species and strain differentiation of non-tubercoulous mycobacteria by Fourier-Transform Infrared microspectroscopy.

Rapid identification of non-tuberculous mycobacteria (NTM) species is important in clinical laboratories to stipulate the appropriate therapy and to offer a comprehensive infection control. We applied Fourier-Transform Infrared microspectroscopy to evaluate, whether the most frequent species of NTM can be rapidly and uniformly identified by this method using microcolonies of NTM growing on solid nutrient agar plates. To establish a standardized protocol, the heterogeneity of cell growth within the microcolonies and the reproducibility of measuring the IR spectra from whole mycobacterial microcolonies were first studied. Hierarchical cluster analysis applied to spectra obtained by linear mapping across microcolony imprints from fast- and slow-growing NTM revealed only little spectral variance between the various microcolony zones. In parallel, when repetitive measurements were performed on independently grown whole single microcolonies with diameters of 80 and 140 mum, excellent reproducibility could be achieved, verifying that mycobacterial microcolonies are well suited for FT-IR-based identification. Twenty-eight different and well-defined strains, comprising the most frequent species of NTM isolated in clinical laboratories, were used to create a classification system based on FT-IR spectra from single microcolonies. Hierarchical cluster analysis allowed the assignment of all isolates measured in replicates to their correct species-specific clusters. Additionally, a clear separation of all strains into strain-specific sub-clusters was observed. These results demonstrate the potential of FT-IR microspectroscopy to rapidly differentiate NTM at the species and strain level. The data so far obtained suggest that an extended spectral database, containing more NTM strains and covering a broader biological variance, may provide a practical solution to rapidly identify unknown NTM isolates in routine clinical-microbiological laboratories with the additional possibility to type these microorganisms at the sub-species level.