Viability of 3h grown bacterial micro-colonies after direct Raman identification.

Clinical diagnostics in routine microbiology still mostly relies on bacterial growth, a time-consuming process that prevents test results to be used directly as key decision-making elements for therapeutic decisions. There is some evidence that Raman micro-spectroscopy provides clinically relevant information from a limited amount of bacterial cells, thus holding the promise of reduced growth times and accelerated result delivery. Indeed, bacterial identification at the species level directly from micro-colonies at an early time of growth (6h) directly on their growth medium has been demonstrated.

Direct identification of clinically relevant bacterial and yeast microcolonies and macrocolonies on solid culture media by Raman spectroscopy.

Decreasing turnaround time is a paramount objective in clinical diagnosis. We evaluated the discrimination power of Raman spectroscopy when analyzing colonies from 80 strains belonging to nine bacterial and one yeast species directly on solid culture medium after 24-h (macrocolonies) and 6-h (microcolonies) incubation. This approach, that minimizes sample preparation and culture time, would allow resuming culture after identification to perform downstream antibiotic susceptibility testing.

Optical forward-scattering for identification of bacteria within microcolonies.

The development of methods for the rapid identification of pathogenic bacteria is a major step towards accelerated clinical diagnosis of infectious diseases and efficient food and water safety control. Methods for identification of bacterial colonies on gelified nutrient broth have the potential to bring an attractive solution, combining simple optical instrumentation, no need for sample preparation or labelling, in a non-destructive process. Here, we studied the possibility of discriminating different bacterial species at a very early stage of growth (6 h of incubation at 37 °C), on thin layers of agar media (1 mm of Tryptic Soy Agar), using light forward-scattering and learning algorithms (Bayes Network, Continuous Naive Bayes, Sequential Minimal Optimisation).