Comparison of Raman spectroscopy and two molecular diagnostic methods for Burkholderia cepacia complex species identification

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.

Antimicrobial susceptibility testing of Gram-positive and -negative bacterial isolates directly from spiked blood culture media with Raman spectroscopy

Patients suffering from bacterial bloodstream infections have an increased risk of developing systematic inflammatory response syndrome (SIRS), which can result in rapid deterioration of the patients' health. Diagnostic methods for bacterial identification and antimicrobial susceptibility tests are time-consuming. The aim of this study was to investigate whether Raman spectroscopy would be able to rapidly provide an antimicrobial susceptibility profile from bacteria isolated directly from positive blood cultures. First, bacterial strains (n = 133) were inoculated in tryptic soy broth and incubated in the presence or absence of antibiotics for 5 h. Antimicrobial susceptibility profiles were analyzed by Raman spectroscopy. Subsequently, a selection of strains was isolated from blood cultures and analyzed similarly. VITEK®2 technology and broth dilution were used as the reference methods. Raman spectra from 67 antibiotic-susceptible strains showed discriminatory spectra in the absence or at low concentrations of antibiotics as compared to high antibiotic concentrations. For 66 antibiotic-resistant strains, no antimicrobial effect was observed on the bacterial Raman spectra. Full concordance with VITEK®2 data and broth dilution was obtained for the antibiotic-susceptible strains, 68 % and 98 %, respectively, for the resistant strains. Discriminative antimicrobial susceptibility testing (AST) profiles were obtained for all bacterial strains isolated from blood cultures, resulting in full concordance with the VITEK®2 data. It can be concluded that Raman spectroscopy is able to detect the antimicrobial susceptibility of bacterial species isolated from a positive blood culture bottle within 5 h. Although Raman spectroscopy is cheap and rapid, further optimization is required, to fulfill a great promise for future AST profiling technology development.