Liver Fibrosis Surface Assessment Based on Non-Linear Optical Microscopy

Ph.DDOCTOR OF PHILOSOPH

Label-free breast histopathology using quantitative phase imaging

According to the latest World Health Organization (WHO) statistics, breast cancer is the most common type of cancer among women worldwide. The WHO has further emphasized that early diagnosis and treatment are key in mitigating the burden of disease. In spite of this assessment, the standard histopathology of breast cancer still relies on manual microscopic examination of stained tissue. Being qualitative and manual in nature, this standard diagnostic procedure can suffer from inter-observer variation and low-throughput. In addition, stain variation between different samples and different laboratories creates problems for supervised image analysis methods for automated diagnosis. A quantitative, label-free and automatable microscopic modality for breast cancer diagnosis is, thus, needed to address these shortcomings in the standard method. Furthermore, prognostic biomarkers are important tools used by clinicians in order assess the disease course in patients. Being correlated with outcomes, these markers allow pathologists to determine aggressiveness of disease and tailor treatment accordingly. However, the current set of biomarkers for breast cancer are ineffective in predicting outcomes in all patients and there is a need for additional markers of prognosis to better account for variation among individuals. Microscopic and imaging tools for extracting new, quantitative biomarkers during breast histopathology are, thus, also desirable. Although a number of new quantitative imaging modalities for diagnostic and prognostic evaluations have been proposed, a key challenge remains compatibility with the existing workflow for easier clinical translation. Quantitative methods that minimally affect the clinical pipeline already in place are expected to have a greater impact than those that require significant new infrastructure. During my graduate work I have approached these problems in modern breast histopathology by using quantitative phase imaging (QPI). QPI is a label-free microscopy technique where image contrast is generated by measuring the optical path-length difference (OPD) across the specimen. OPD refers to the product of the refractive index and thickness at a point in the field of view. Since this measurement relies on a physical property of tissue and is label-free, it provides an objective and potentially automatable basis for tissue assessment. We employ a QPI technique called Spatial Light Interference Microscopy (SLIM) for investigations carried out during this thesis research. The specific aims of my thesis research are: 1. Label-free quantitative evaluation of breast biopsies using SLIM: In this work, we show by imaging a tissue microarray (TMA) that our QPI based method can separate benign and malignant cases by relying on tissue OPD based features. By employing image processing and statistical learning, we demonstrate a label-free quantitative diagnosis scheme that can provide an objective basis for tissue assessment. A quantitative method like this can also, potentially, be automated, reducing case-load for pathologists by automatically flagging problematic cases that require further investigation. 2. Quantifying tumor adjacent collagen structure in breast tissue using SLIM: Recent evidence shows that the structure of tumor adjacent collagen fibers influences tumor progression. In particular, collagen fiber alignment and orientation can facilitate epithelial invasion to surrounding tissue. We demonstrate that SLIM can be used to detect this prognostic marker that in the past had been detected using Second Harmonic Generation Microscopy (SHGM). Our SLIM based method improves on the SHGM based method in terms of throughput and the fact that cellular information can be obtained, in addition to collagen fiber structure, in a single image. 3. Quantitative histopathology on stained tissue biopsies: The instruments and image analysis tools developed in Aims 1 and 2 are designed for unstained tissue biopsies. Since standard tissue histopathology inevitably requires staining, we aim to demonstrate that we can extend these tools to stained tissue biopsies. In this way, the standard diagnostic workflow will be minimally disrupted. In addition, from a single shot, both an OPD map and stained tissue bright field image will be obtainable for evaluation. We demonstrate that QPI images of stained tissue can be used to solve diagnostic and prognostic problems in breast tissue assessment, using quantitative markers

Resolving the fibrotic niche of human liver cirrhosis at single-cell level.

Liver cirrhosis is a major cause of death worldwide and is characterized by extensive fibrosis. There are currently no effective antifibrotic therapies available. To obtain a better understanding of the cellular and molecular mechanisms involved in disease pathogenesis and enable the discovery of therapeutic targets, here we profile the transcriptomes of more than 100,000 single human cells, yielding molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver. We identify a scar-associated TREM2+CD9+ subpopulation of macrophages, which expands in liver fibrosis, differentiates from circulating monocytes and is pro-fibrogenic. We also define ACKR1+ and PLVAP+ endothelial cells that expand in cirrhosis, are topographically restricted to the fibrotic niche and enhance the transmigration of leucocytes. Multi-lineage modelling of ligand and receptor interactions between the scar-associated macrophages, endothelial cells and PDGFRα+ collagen-producing mesenchymal cells reveals intra-scar activity of several pro-fibrogenic pathways including TNFRSF12A, PDGFR and NOTCH signalling. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides a conceptual framework for the discovery of rational therapeutic targets in liver cirrhosis.Includes Wellcome, BHF, MRC, BBSRC and NIHR

Biophysical basis of skin cancer detection using Raman spectroscopy

The goal of this dissertation is to study the potential of Raman spectroscopy in improving the clinical diagnosis of skin cancer, including two main applications: noninvasive screening of melanoma skin cancer and surgical margin detection of nonmelanoma skin cancer. Skin cancer is the most common type of malignancy, accounting for over 5.4 million cases and 10 thousand deaths per year in the United States alone. Like most cancers, the current “gold standard” diagnosis relies on biopsy and histopathology, which is invasive, time-consuming, and costly. Moreover, large numbers of benign lesions are biopsied for melanoma diagnosis, resulting in substantial financial burden and patient discomfort. Therefore, an urgent need exists to develop a noninvasive, fast, and accurate method for skin cancer detection. The first part of the dissertation focuses on exploring the biophysical origin of in vivo melanoma detection. Our group has previously reported on the development of a clinical Raman spectroscopy system towards spectral biopsy of skin; however, the biochemical changes that Raman spectroscopy relies on for accurate melanoma diagnosis remained unclear. As a result, we proposed a biophysical inverse model to address this issue. To build the model, we established a custom confocal Raman microscope to extract in situ human skin constituents spanning normal and various diseased states. Our results indicate collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin, and water are the most important model components. Furthermore, collagen and triolein are the most relevant markers to discriminate malignant melanoma from benign nevi. The second part of the dissertation discusses the biophysical basis of nonmelanoma skin cancer margin delineation. We discovered the diagnostic markers to accurately differentiate tumor from normal skin, which is critical to maximize positive patient outcomes in skin cancer surgery. The biochemical changes derived from our model were highly correlated with histopathological diagnosis. We further demonstrated the feasibility of a superpixel acquisition approach for rapid classification of tumor boundaries in skin biopsies. Our results suggest Raman spectroscopy will be a powerful tool for intraoperative surgical guidanceBiomedical Engineerin

Development of Imaging Mass Spectrometry Analysis of Lipids in Biological and Clinically Relevant Applications

La spectrométrie de masse mesure la masse des ions selon leur rapport masse sur charge. Cette technique est employée dans plusieurs domaines et peut analyser des mélanges complexes. L’imagerie par spectrométrie de masse (Imaging Mass Spectrometry en anglais, IMS), une branche de la spectrométrie de masse, permet l’analyse des ions sur une surface, tout en conservant l’organisation spatiale des ions détectés. Jusqu’à présent, les échantillons les plus étudiés en IMS sont des sections tissulaires végétales ou animales. Parmi les molécules couramment analysées par l’IMS, les lipides ont suscité beaucoup d'intérêt. Les lipides sont impliqués dans les maladies et le fonctionnement normal des cellules; ils forment la membrane cellulaire et ont plusieurs rôles, comme celui de réguler des événements cellulaires. Considérant l’implication des lipides dans la biologie et la capacité du MALDI IMS à les analyser, nous avons développé des stratégies analytiques pour la manipulation des échantillons et l’analyse de larges ensembles de données lipidiques. La dégradation des lipides est très importante dans l’industrie alimentaire. De la même façon, les lipides des sections tissulaires risquent de se dégrader. Leurs produits de dégradation peuvent donc introduire des artefacts dans l’analyse IMS ainsi que la perte d’espèces lipidiques pouvant nuire à la précision des mesures d’abondance. Puisque les lipides oxydés sont aussi des médiateurs importants dans le développement de plusieurs maladies, leur réelle préservation devient donc critique. Dans les études multi-institutionnelles où les échantillons sont souvent transportés d’un emplacement à l’autre, des protocoles adaptés et validés, et des mesures de dégradation sont nécessaires. Nos principaux résultats sont les suivants : un accroissement en fonction du temps des phospholipides oxydés et des lysophospholipides dans des conditions ambiantes, une diminution de la présence des lipides ayant des acides gras insaturés et un effet inhibitoire sur ses phénomènes de la conservation des sections au froid sous N2. A température et atmosphère ambiantes, les phospholipides sont oxydés sur une échelle de temps typique d’une préparation IMS normale (~30 minutes). Les phospholipides sont aussi décomposés en lysophospholipides sur une échelle de temps de plusieurs jours. La validation d’une méthode de manipulation d’échantillon est d’autant plus importante lorsqu’il s’agit d’analyser un plus grand nombre d’échantillons. L’athérosclérose est une maladie cardiovasculaire induite par l’accumulation de matériel cellulaire sur la paroi artérielle. Puisque l’athérosclérose est un phénomène en trois dimension (3D), l'IMS 3D en série devient donc utile, d'une part, car elle a la capacité à localiser les molécules sur la longueur totale d’une plaque athéromateuse et, d'autre part, car elle peut identifier des mécanismes moléculaires du développement ou de la rupture des plaques. l'IMS 3D en série fait face à certains défis spécifiques, dont beaucoup se rapportent simplement à la reconstruction en 3D et à l’interprétation de la reconstruction moléculaire en temps réel. En tenant compte de ces objectifs et en utilisant l’IMS des lipides pour l’étude des plaques d’athérosclérose d’une carotide humaine et d’un modèle murin d’athérosclérose, nous avons élaboré des méthodes «open-source» pour la reconstruction des données de l’IMS en 3D. Notre méthodologie fournit un moyen d’obtenir des visualisations de haute qualité et démontre une stratégie pour l’interprétation rapide des données de l’IMS 3D par la segmentation multivariée. L’analyse d’aortes d’un modèle murin a été le point de départ pour le développement des méthodes car ce sont des échantillons mieux contrôlés. En corrélant les données acquises en mode d’ionisation positive et négative, l’IMS en 3D a permis de démontrer une accumulation des phospholipides dans les sinus aortiques. De plus, l’IMS par AgLDI a mis en évidence une localisation différentielle des acides gras libres, du cholestérol, des esters du cholestérol et des triglycérides. La segmentation multivariée des signaux lipidiques suite à l’analyse par IMS d’une carotide humaine démontre une histologie moléculaire corrélée avec le degré de sténose de l’artère. Ces recherches aident à mieux comprendre la complexité biologique de l’athérosclérose et peuvent possiblement prédire le développement de certains cas cliniques. La métastase au foie du cancer colorectal (Colorectal cancer liver metastasis en anglais, CRCLM) est la maladie métastatique du cancer colorectal primaire, un des cancers le plus fréquent au monde. L’évaluation et le pronostic des tumeurs CRCLM sont effectués avec l’histopathologie avec une marge d’erreur. Nous avons utilisé l’IMS des lipides pour identifier les compartiments histologiques du CRCLM et extraire leurs signatures lipidiques. En exploitant ces signatures moléculaires, nous avons pu déterminer un score histopathologique quantitatif et objectif et qui corrèle avec le pronostic. De plus, par la dissection des signatures lipidiques, nous avons identifié des espèces lipidiques individuelles qui sont discriminants des différentes histologies du CRCLM et qui peuvent potentiellement être utilisées comme des biomarqueurs pour la détermination de la réponse à la thérapie. Plus spécifiquement, nous avons trouvé une série de plasmalogènes et sphingolipides qui permettent de distinguer deux différents types de nécrose (infarct-like necrosis et usual necrosis en anglais, ILN et UN, respectivement). L’ILN est associé avec la réponse aux traitements chimiothérapiques, alors que l’UN est associé au fonctionnement normal de la tumeur.Mass spectrometry is the measurement of the mass over charge ratio of ions. It is broadly applicable and capable of analyzing complex mixtures. Imaging mass spectrometry (IMS) is a branch of mass spectrometry that analyses ions across a surface while conserving their spatial organization on said surface. At this juncture, the most studied IMS samples are thin tissue sections from plants and animals. Among the molecules routinely imaged by IMS, lipids have generated significant interest. Lipids are important in disease and normal cell function as they form cell membranes and act as signaling molecules for cellular events among many other roles. Considering the potential of lipids in biological and clinical applications and the capability of MALDI to ionize lipids, we developed analytical strategies for the handling of samples and analysis of large lipid MALDI IMS datasets. Lipid degradation is massively important in the food industry with oxidized products producing a bad smell and taste. Similarly, lipids in thin tissue sections cut from whole tissues are subject to degradation, and their degradation products can introduce IMS artifacts and the loss of normally occurring species to degradation can skew accuracy in IMS measures of abundance. Oxidized lipids are also known to be important mediators in the progression of several diseases and their accurate preservation is critical. As IMS studies become multi-institutional and collaborations lead to sample exchange, the need for validated protocols and measures of degradation are necessary. We observed the products of lipid degradation in tissue sections from multiple mouse organs and reported on the conditions promoting and inhibiting their presence as well as the timeline of degradation. Our key findings were the increase in oxidized phospholipids and lysophospholipids from degradation at ambient conditions, the decrease in the presence of lipids containing unsaturations on their fatty acyl chains, and the inhibition of degradation by matrix coating and cold storage of sections under N2 atmosphere. At ambient atmospheric and temperature, lipids degraded into oxidized phospholipids on the time-scale of a normal IMS experiment sample preparation (within 30 min). Lipids then degraded into lysophospholipids’ on a time scale on the order of several days. Validation of sample handling is especially important when a greater number of samples are to be analyzed either through a cohort of samples, or analysis of multiple sections from a single tissue as in serial 3D IMS. Atherosclerosis is disease caused by accumulation of cellular material at the arterial wall. The accumulation implanted in the cell wall grows and eventually occludes the blood vessel, or causes a stroke. Atherosclerosis is a 3D phenomenon and serial 3D IMS is useful for its ability to localize molecules throughout the length of a plaque and help to define the molecular mechanisms of plaque development and rupture. Serial 3D IMS has many challenges, many of which are simply a matter of producing 3D reconstructions and interpreting them in a timely fashion. In this aim and using analysis of lipids from atherosclerotic plaques from a human carotid and mouse aortic sinuses, we described 3D reconstruction methods using open-source software. Our methodology provides means to obtain high quality visualizations and demonstrates strategies for rapid interpretation of 3D IMS datasets through multivariate segmentation. Mouse aorta from model animals provided a springboard for developing the methods on lower risk samples with less variation with interesting molecular results. 3D MALDI IMS showed localized phospholipid accumulation in the mouse aortic sinuses with correlation between separate positive and negative ionization datasets. Silver-assisted LDI imaging presented differential localization of free fatty acids, cholesterol / cholesterol esters, and triglycerides. The human carotid’s 3D segmentation shows molecular histologies (spatial groupings of imaging pixels with similar spectral fingerprints) correlating to the degree of arterial stenosis. Our results outline the potential for 3D IMS in atherosclerotic research. Molecular histologies and their 3D spatial organization, obtained from the IMS techniques used herein, may predict high-risk features, and particularly identify areas of plaque that have higher-risk of rupture. These investigations would help further unravel the biological complexities of atherosclerosis, and predict clinical outcomes. Colorectal cancer liver metastasis (CRCLM) is the metastatic disease of primary colorectal cancer, one of the most common cancers worldwide. CRC is a cancer of the endothelial lining of the colon or rectum. CRC itself is often cured with surgery, while CRCLM is more deadly and treated with chemotherapy with more limited efficacy. Prognosticating and assessment of tumors is performed using classical histopathology with a margin of error. We have used lipid IMS to identify the histological compartments and extract their signatures. Using these IMS signatures we obtained a quantitative and objective histopathological score that correlates with prognosis. Additionally, by dissecting out the lipid signatures we have identified single lipid moieties that are unique to different histologies that could potentially be used as new biomarkers for assessing response to therapy. Particularly, we found a series of plasmalogen and sphingolipid species that differentiate infarct-like and usual necrosis, typical of chemotherapeutic response and normal tumor function, respectively

Automated Computational Techniques for High-throughput Image Analysis of Skin Structure

Biological image processing and analysis are concerned with enhancing and quantifying features that reflect different pathological states, based on the use of combinations of image processing algorithms. The integration of image processing and analysis techniques to evaluate and assess skin integrity in both human and mouse models is a major theme in this thesis. More specifically, this thesis describes computational systems for high-throughput analysis of skin tissue section images and non-invasive imaging techniques. As the skin is a largest organ in the mammalian body, and is complex in structure, manual quantification and analysis a hard task for the observer to determine an objective result, and furthermore, the analysis is complex in terms of accuracy and time taken. To look at the gross morphology of the skin, I developed high throughput analysis based on an adaptive active contour model to isolate the skin layers and provide quantification methods. This was utilised in a study to evaluate cutaneous morphology in 475 knockout mouse lines provided by the Mouse Genetics Project (MGP) pipeline, that was generated by the Wellcome Trust Sanger Institute (WTSI). This is a major international initiative to provide both functional annotation of the mammalian genome and insight into the genetic basis of disease. I found 53 interesting adipocyte phenotypes, 18 interesting dermal phenotypes and 3 interesting epidermal phenotypes. I also focussed on the analysis of collagen in the dermis of skin images in several ways. For collagen structure analysis, I developed a combined system of Gabor filtering and Fast Fourier Transform FFT. This analysis allowed the detection of subtle changes in collagen organisation. Using similar images, I also measured collagen bundle thickness by computing the maximum frequency of the FFT power spectrum. To assess collagen dynamics, I developed k-means clustering for segmentation based on colour distribution. The use of this approach allowed the measurement of dermal degradation with age and disease, which was not possible by existing means. Obtaining human skin material to facilitate the drug discovery and development process is not an easy task. The manipulation, monitoring and cost of human subjects makes the use of mouse models more suitable for high-throughput screening. Therefore, I have evaluated skin integrity from mouse tissue rather than human skin, however, mouse skin is thinner than human skin and many morphological features are easier to visualise in human skin, which has implications for analysis. Skin moulds can be used to create an impression of the skin surface. Changes in texture of skin can reflect skin conditions. I developed a skin surface structure analysis system to measure the degree of change in texture of the human skin surface. The alterations detected in texture parameters in skin mould impressions reflected changes caused by sun exposure, ageing and many other clinical parameters. I compared my analysis with the existing Beagley-Gibson scoring system to find correlations between automated and manual analysis to inform a decision on the use of optimal methods. By removing subjectivity of manual methods, I was able to develop a robust system to evaluate, for example, damage resulting from UV exposure. My experimental analysis indicated that techniques developed in this thesis were able to analyse both histological samples and skin surface images in high-throughput experiments. They could, therefore, make a contribution to biological image analysis by providing accurate results to help clinical decision making, and facilitate biological laboratory experiments to improve the quality of research in this field, and save time. Overall, my thesis demonstrated that accurate analysis of the skin to gain meaningful biological information requires an automated system that can achieve feature extraction, quantification, analysis and decision making to find interesting phenotypes and abnormalities. This will help the evaluation of the effects of a specific treatment, and answer many biological questions in fields of cosmetic dermatology and drug discovery, and improve our understanding of the genetic basis of disease

Genomics and metabonomics in severe alcoholic hepatitis

Severe alcoholic hepatitis is a florid presentation of alcohol-related liver disease and is associated with very high short-term mortality, in excess of 20% within 28 days. Severe alcoholic hepatitis occurs in a minority of patients who develop alcohol-related liver disease. A combination of genetic and environmental factors is likely to predispose to severe alcoholic hepatitis. To date the clinical phenotype has not been extensively examined in candidate gene studies and has been the subject of a single, small genome-wide association study. A genome-wide association study of severe alcoholic hepatitis identified two loci potentially associated with the risk of developing severe alcoholic hepatitis: i) A strong association with PNPLA3, a well-recognised risk locus for alcohol-related liver disease, and ii) a novel but weaker association with SLC38A4, an amino acid transporter. The primary genetic variant at each locus was evaluated to determine whether there was an influence on disease phenotype or outcome. The primary variant in PNPLA3, rs738409, is a missense variant. Analyses indicated a deleterious effect of homozygosity on medium-term survival in addition to more severe disease on baseline histology and a slower recovery in liver function over the short-term period; consistent with established literature in alcohol-related cirrhosis. In contrast the primary variant in SLC38A4, rs11183620, is intronic with no clear evidence for an effect on gene expression or function. Analyses did not indicate an influence on histology, clinical phenotypes or outcomes. In light of the locus’ novelty further work was undertaken to determine any potential contribution to disease pathogenesis. SLC38A4 was down-regulated in whole liver tissue in severe alcoholic hepatitis. Experiments with cell lines in culture suggested the pro-inflammatory cytokine IL-1 as a potential driver. SLC38A4 knockdown resulted in upregulation of some cellular responses associated with nutrient deprivation. There was no influence of the variant on serum amino acid profiles. The functional significance of SLC38A4 down-regulation remains the subject of ongoing work.Open Acces

SteatoSITE: an Integrated Gene-to-Outcome Data Commons for Precision Medicine Research in NAFLD

Nonalcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease worldwide and a growing healthcare burden. The pathobiology of NAFLD is complex, disease progression is variable and unpredictable, and there are no qualified prognostic biomarkers or licensed pharmacotherapies that can improve clinical outcomes; it represents an unmet precision medicine challenge. We established a retrospective multicentre national cohort of 940 patients, across the complete NAFLD spectrum, integrating quantitative digital pathology, hepatic RNA-sequencing and 5.67 million days of longitudinal electronic health record follow-up into a secure, searchable, open resource (SteatoSITE) to inform rational biomarker and drug development and facilitate personalised medicine approaches for NAFLD. A complementary web-based gene browser was also developed. Here, our initial analysis uncovers disease stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with disease progression in NAFLD. Additionally, we construct novel transcriptional risk prediction tools for the development of future hepatic decompensation events

Constrained K-Means Classification

Classification-via-clustering (CvC) is a widely used method, using a clustering procedure to perform classification tasks. In this paper, a novel K-Means-based CvC algorithm is presented, analysed and evaluated. Two additional techniques are employed to reduce the effects of the limitations of K-Means. A hypercube of constraints is defined for each centroid and weights are acquired for each attribute of each class, for the use of a weighted Euclidean distance as a similarity criterion in the clustering procedure. Experiments are made with 42 well–known classification datasets. The experimental results demonstrate that the proposed algorithm outperforms CvC with simple K-Means