The current methods for diagnosis of acute and chronic infections are complex and skill-intensive. For complex clinical biofilm infections, it can take days from collecting and processing a patient’s sample to achieving a result. These aspects place a significant burden on healthcare providers, delay treatment, and can lead to adverse patient outcomes. We report the development and application of a novel multi-excitation Raman spectroscopy-based methodology for the label-free and non-invasive detection of microbial pathogens that can be used with unprocessed clinical samples directly and provide rapid data to inform diagnosis by a medical professional. The method relies on the differential excitation of non-resonant and resonant molecular components in bacterial cells to enhance the molecular finger-printing capability to obtain strain-level distinction in bacterial species. Here, we use this strategy to detect and characterize the respiratory pathogens Pseudomonas aeruginosa and Staphylococcus aureus as typical infectious agents associated with cystic fibrosis. Planktonic specimens were analyzed both in isolation and in artificial sputum media. The resonance Raman components, excited at different wavelengths, were characterized as carotenoids and porphyrins. By combining the more informative multi-excitation Raman spectra with multivariate analysis (support vector machine) the accuracy was found to be 99.75% for both species (across all strains), including 100% accuracy for drug-sensitive and drug-resistant S. aureus. The results demonstrate that our methodology based on multi-excitation Raman spectroscopy can underpin the development of a powerful platform for the rapid and reagentless detection of clinical pathogens to support diagnosis by a medical expert, in this case relevant to cystic fibrosis. Such a platform could provide translatable diagnostic solutions in a variety of disease areas and also be utilized for the rapid detection of anti-microbial resistance.

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We report a technologically novel microscopy system for bioimaging based on a 100 fs titanium:sapphire (Ti:Sa) laser pumped coherent continuum from a tailored, 9-cm long, all normal dispersion (ANDi) fiber, enabling concurrent image contrast with (a) spectral focusing coherent anti-Stokes Raman scattering (SF-CARS) (spanning 900-3200 cm −1) and (b) sum frequency generation (SFG). Both modalities were efficiently excited with power levels at the microscope focus compatible with biological samples. Moreover, using the continuum, images were recorded in the back-scattering (epi-detection) geometry, without the necessity for an expensive, computer-controlled, spatial light modulator (SLM), clearly demonstrating the strong signal levels achieved. Image contrast from the multiple modalities provided greater chemical and structural insights than imaging with any single technique in isolation. Numerical simulations supported these developments in regard to both the optimum fiber length for SC generation and the achievement of high spectral resolution in SF-CARS via careful group delay dispersion matching across the pump and Stokes pulses using just an inexpensive sequence of short glass blocks inserted into the Stokes beam. We show bio-images of mouse tissue recorded concurrently via label/stain-free contrast from multiple modalities: CARS, two-photon auto-fluorescence (TPaF) and second harmonic/sum frequency generation (SHG/ SFG). Overall, our approach delivers optimum performance in back-scattered (epi-) detection configuration, suited for thick samples, at reduced complexity and cost. The addition of this simple fiber add-on to lasers already widely used for TPF microscopy can thus extend the capabilities of a significant number of existing microscopy laboratories.

Current methods for diagnosing acute and complex infections mostly rely on culture-based methods and, for biofilms, fluorescence in-situ hybridization. These techniques are labor-intensive and can take 2-4 days to return a test result, especially considering an extra culturing step required for the antibiotic susceptibility testing (AST). This places a significant burden on healthcare providers, delaying treatment and leading to adverse patient outcomes. Here, we report the complementary use of our newly developed multi-excitation Raman spectroscopy (ME-RS) method with whole-genome sequencing (WGS). Four WHO priority pathogens are AST phenotyped and their antimicrobial resistance (AMR) profile determined by WGS. On application of ME-RS method we find high correlation with the WGS characterization. Highly accurate classification based on the species (98.93%), wild-type/non-wild type (99.45%), and presence or absence of thick peptidoglycan layers in cell walls (100%), as well as at the individual strain level (99.29%). These results clearly demonstrate the potential of ME-RS as a rapid and first-stage tool for species, resistance and strain-level classification which can be followed up by WGS for confirmation. Such a workflow can facilitate efficient antimicrobial stewardship to handle and prevent the spread of AMR.

In this paper we describe our recent work in multi-excitation surface enhanced Raman spectroscopy (MX-SERS), and its application for robust strain-level bacteria identification. The development of MX-SERS follows directly from our previous work in rapid bacterial identification using multi-excitation Raman spectroscopy (MX-Raman), which enabled highly accurate (up to 99.75%) strain-level distinction of bacteria, including antibiotic resistant strains of bacteria and from within complex media. In this work we use the strong wavelength dependence of both the Raman scattering cross-section and the surface plasmon to demonstrate a novel capability in bacteria identification. Compared to MX-Raman, MX-SERS has up to 8x faster data acquisition speed as well as up to 4000x lower laser power incident on the sample. Furthermore, we fabricate SERS-active substrates with a simple and low-cost fabrication method that can be adapted to fit a chosen wavelength regime. This combination of strain-level sensitivity and high-speed detection, combined with a low-cost SERS substrate, has strong potential applications in clinical diagnostics, and could be integrated within a real-world pathogen detection workflow.