Estimating Bacterial and Cellular Load in FCFM Imaging

We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician’s assessment

Optical profiling of macrophages

Macrophages are required to show plasticity in how they react to their microenvironment and orchestrate an inflammatory response. With such an integral role in human immunity, aberrant macrophage function can directly contribute to a variety of pathologies: from driving chronic inflammation to a compromised clearance of invading pathogens. Although there are pharmaceutical opportunities to restore alveolar macrophage function in disease, there still remains a challenge to truly profile their activity in situ. The use of optical endomicroscopy - a non-invasive, fibre-optic imaging platform capable of accessing the alveolar space, may be used in combination with optical probes to profile alveolar macrophage activity in their native environment. Work outlined in this thesis covers the characterisation of a human monocytederived macrophage model phenotype, performed using gold-standard in vitro systems including flow cytometric analysis of receptor expression and phagocytic activity. The work then moves on to explore alternative ways of optically profiling macrophages that may have clinical applications. An optical probe was synthesised to target the mannose receptor, a cell-surface receptor expressed on macrophages, using a camelid nanobody fragment as a targeting ligand. Initial characterisation showed cell-type specificity of the probe towards macrophages. While labelling appeared to be via active internalisation by cells, more evidence is required to determine if this probe interacts specifically with the mannose receptor target. A novel form of optical endomicroscopy was used to explore imaging macrophages, label-free, via their auto-fluorescent emission spectra. This was to distinguish macrophages following internalisation of a fluorescent target, without further labelling required to image negative cells. Initial imaging showed that in vitro monocyte-derived macrophages did not fluoresce brightly enough to be imaged label-free, though it is expected that primary lung macrophages – particularly from COPD patients who smoke – would be sufficiently bright enough to profile with this technique. Ultimately this work will be the foundation to profiling primary alveolar macrophages in health and disease. Using optical endomicroscopic imaging systems with optical probes for markers of cell phenotype, as well as other label-free methods in development, there is potential to profile the activity of alveolar macrophages directly in the alveolar space of the human lung and monitor pharmaceutical effects on their activity

Estimating Bacterial and Cellular Load in FCFM Imaging

We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician’s assessment

Development and clinical translation of optical and software methods for endomicroscopic imaging

Endomicroscopy is an emerging technology that aims to improve clinical diagnostics by allowing for in vivo microscopy in difficult to reach areas of the body. This is most commonly achieved by using coherent fibre bundles to relay light for illumination and imaging to and from the area under investigation. Endomicroscopy’s attraction for researchers and clinicians is two-fold: on the one hand, its use can reduce the invasiveness of a diagnostic procedure by removing the need for biopsies; On the other hand, it allows for structural and functional in vivo imaging. Endomicroscopic images acquired through optical fibre bundles exhibit artefacts that deteriorate image quality and contrast. This thesis aims to improve an existing endomicroscopy imaging system by exploring two methods that mitigate these artefacts. The first, software-based method takes several processing steps from literature and implements them in an existing endomicroscopy device with a focus on real-time application to enable clinical use, after image quality was found to be inadequate without further processing. A contribution to the field is that two different approaches are implemented and compared in quantitative and qualitative means that have not been compared directly in this manner before. This first attempt at improving endomicroscopy image quality relies solely on digital image processing methods and is developed with a strong focus on real-time applicability in clinical use. Both approaches are compared on pre-clinical and clinical human imaging data. The second method targets the effect of inter-core coupling, which reduces contrast in fibre images. A parallelised confocal imaging method is developed in which a sequence of images is acquired while selectively illuminating groups of fibre cores through the use of a spatial light modulator. A bespoke algorithm creates a composite image in a final processing step. In doing so, unwanted light is detected and removed from the final image. This method is shown to reduce the negative impact of inter-core coupling on image contrast on small imaging targets, while no benefit was found in large, scattering samples