Published:
Abstract: The spread of Acinetobacter baumannii in clinical settings is a great concern at the time being, given the epidemic potential and the capacity to elude the effects of drugs of this bacterial species. How A. baumannii achieves its resistance determinants is not yet exactly understood, hence the need for new studies aimed at resolving its structure and functions. In this work, we employ Re-scan Confocal Microscopy (RCM), an emerging super-resolution technique, for investigating the morphology of this pathogen. Interestingly, about 1.5% of the imaged cells were found to be filamentous without internal division septa, in both logarithmic and stationary phases, which, to our knowledge, has not been previously reported. Since filamenting represents one of the first mechanisms adopted by bacteria to resist antibiotic effects, we also focus on evaluating the sensitivity of A. baumannii to colistin. For this, the LIVE/DEAD ratio is assessed by means of a fluorometric approach based on SYTO9 and KK114 dyes, two stains that we also used for RCM imaging. Their dual role demonstrated here can potentially be exploited in-tandem in multimodal systems for imaging and fluorometry.
Abstract: Second harmonic generation (SHG) microscopy has emerged over the past two decades as a powerful tool for tissue characterization and diagnostics. Its main applications in medicine are related to mapping the collagen architecture of in-vivo, ex-vivo and fixed tissues based on endogenous contrast. In this work we present how H&E staining of excised and fixed tissues influences the extraction and use of image parameters specific to polarization-resolved SHG (PSHG) microscopy, which are known to provide quantitative information on the collagen structure and organization. We employ a theoretical collagen model for fitting the experimental PSHG datasets to obtain the second order susceptibility tensor elements ratios and the fitting efficiency. Furthermore, the second harmonic intensity acquired under circular polarization is investigated. The evolution of these parameters in both forward- and backward-collected SHG are computed for both H&E-stained and unstained tissue sections. Consistent modifications are observed between the two cases in terms of the fitting efficiency and the second harmonic intensity. This suggests that similar quantitative analysis workflows applied to PSHG images collected on stained and unstained tissues could yield different results, and hence affect the diagnostic accuracy.
Super-Resolution Re-scan Second Harmonic Generation Microscopy, Stanciu SG, Hristu R, Stanciu GA, Tranca DE, Eftimie L, Dumitru A, Costache M, Stenmark HA, Manders H, Cherian A, Tark-Dame M., PNAS (in press), (pre-print available)
Second Harmonic Generation Microscopy (SHG) is generally acknowledged as a powerful tool for the label-free 3D visualization of tissues and advanced materials, with one of its most popular applications being collagen imaging. Although the great need, progress in super-resolved SHG imaging lags behind the developments reported over the past years in fluorescence-based optical nanoscopy. In this work, we quantitatively show on collagenous tissues that by combining SHG imaging with re-scan microscopy resolutions that surpass the diffraction limit with ~1.4x become available. Besides Re-scan Second Harmonic Generation Microscopy (rSHG), we demonstrate as well super-resolved Re-scan Two-Photon Excited Fluorescence Microscopy (rTPEF). These two techniques are implemented by modifying a Re-scan Confocal Microscope (RCM), retaining its initial function, resulting thus in a multimodal rSHG/rTPEF/RCM system. Given the simplicity and flexibility of re-scan microscopy, we consider that the reported results are likely to augment the number and nature of applications relying on super-resolved non-linear optical imaging.
Submitted:
Towards Next-Generation Endoscopes Integrating Biomimetic Video-Systems, Nonlinear Optical Microscopy and Deep Learning
On the Automated Detection of Corneal Edema with Second Harmonic Generation Microscopy and Deep Learning (preprint available)
In preparation:
On the classification of non-linear optical microscopy images of tissues with super-pixel descriptors
Automated detection of gastric cancers with deep learning assisted non-linear optical microscopies