Correlative Multimodal Approach Based on Optical Near-Field and Topographic Imaging to Characterize the Morphology of ESKAPE Pathogen Bacteria at Nanoscale

Microscopy techniques such as confocal laser scanning microscopy (CLSM), multiphoton excitation microscopy (MPM) or coherent anti-Stokes Raman scattering microscopy (CARS), are nowadays extensively utilized to study biological species and represent the "backbone" of many important imaging applications in biology and medicine. However, the resolution of these far-field techniques is limited by diffraction to half the wavelength of the excitation light which impedes an accurate understanding of important structures and processes taking place at nanoscale. Super-resolution techniques overcome this limitation via different ingenious concepts and strategies, and techniques such as fluorescence-Photoactivation Localization Microscopy (f-PALM), Stochastic Optical Reconstruction Microscopy (STORM), or Stimulated Emission Depletion (STED) microscopy are now capable to routinely achieve optical resolutions of ~50 nm, but they rely on fluorescence labelling which results in various limitations. Apertureless scattering near-field optical microscopy (ASNOM) can resolve in a label-free manner optical details <10 nm, and the underlying architecture of techniques in this family allows the simultaneous acquisition of atomic force microscopy (AFM) datasets, which is useful for placing nanoscale optical data into a registered topographic context. However, most of the studies that have been carried out until now with ASNOM were targeted on the characterization of basic and advanced materials, and due to the limited body of work targeting biological samples, ASNOM images acquired in such experiments are in most cases difficult to interpret. Moreover, although ASNOM and AFM images are usually displayed side by side in studies that involve these two techniques, an exact model to explain how the information extracted with these two distinct modalities are correlated is still not available at the time being. In this paper, we contribute to this field, by proposing a model for correlated multimodal imaging (CMI) that combines ASNOM data collected on multiple harmonics (amplitudes and phases) with AFM topography data, to create a composite image that allows a to better characterize the morphology of bacterial cells. A complete and detailed characterization of bacterial species plays a fundamental role in many biomedical studies, related to bacterial infection diagnosis and treatment. In particular, we consider pathogens of the ESKAPE group, that are involved in hospital- and community-associated infections, with a high capability to resist to different antibiotics.