Biological imaging of challenging targets: Peripheral nerve, mouse bones and cultured osteoclasts

During this study, advances in bioimaging have affected the biomedical research field. In this thesis, traditional microscopy and selected new techniques, namely microcomputed tomography (µCT), STED microscopy and laser-capture microdissection have been combined to study complex tissues in human adults such as developing peripheral nerves, bones and osteoclasts to reveal previously unseen features of these tissues and cells. Confocal microscopy was used to analyze the adult and developing human peripheral nerves. Tight junction proteins were localized to subcellular structures of myelinating Schwann cells. The combination of tight junction proteins differed from that of rodents. Furthermore, claudin expression was weak in fetal endoneurium during the second trimester, and the junctions were not fully maturated by the end of the third trimester. The results suggest that the maturation of Schwann cell autotypic junctions continues after birth. Various imaging modalities were combined in order to analyze the phenotype of the Nf1Ocl mouse model. µCT revealed narrowed growth plates and slight differences in trabecular and cortical bone in NfOcl-/- mice, but not in the osteoporotic bone phenotype. However, in vitro studies showed accelerated bone resorption capacity and a hyperactivated Ras signaling pathway in Nf1-/- osteoclasts. STED microscopy uncovered new features of actin, as bending and branching filaments were demonstrated in human osteoclasts. New features of actin filaments were detected also in macrophages and keratinocytes. The results emphasize the importance of bioimaging techniques in studying challenging tissues.Biokuvantamisen haastavat kohteet: erifeerinen hermo, luu ja osteoklastiviljelmät Väitöskirjassa tutkittiin vaikeasti kuvannettavia kudoksia, aikuisen ja kehittyvän sikiön perifeerista hermoa, luuta sekä osteklasteja yhdistämällä tavanomaisia kuvantamismenetelmiä valikoitujen mikroskopian erityismenetelmien kanssa. Aikuisen ja kehittyvän sikiön perifeerinen hermo kuvannettiin konfokaali-mikroskopialla. Tiiviit liitokset sijaitsivat solunsisäisissä rakenteissa myeliiniä tuottavissa soluissa. Tulokset eroavat aiemmin jyrsijöillä tehtyihin havaintoihin nähden. Tiivisliitosproteiini klaudiinin ilmentyminen oli vähäistä toisen raskauskolmanneksen aikana, eivätkä tiiviit liitokset ehtineet kehittyä loppuun asti viimeisen kolmanneksen loppuun mennessä. Todennäköisesti tiiviit liitokset kehittyvät vielä syntymän jälkeen. Ehdollisesti poistogeenisen Nf1Ocl -hiirimallin ilmiasua tutkittiin yhdistämällä useita kuvausmodaliteetteja. Mikrotietokonetomografian avulla havaittiin sääriluussa kasvulevyn madaltuneen sekä kuoriluussa että hohkaluussa havaittiin useita pieniä muutoksia, mutta vastoin olettamusta kudoksessa ei todettu luukatoon viittaavaa. Soluviljelyolosuhteissa Nf-/- osteoklastit hajoittivat aggressiivisesti luuta sekä niiden Ras-signaalireitti oli aktivoitunut. STED mikroskoopin avulla paljastui uusia aktiinin ominaisuuksia. Aktiinin havaittiin taipuvan ja haarautuvan ihmisen osteoklasteissa sekä muissa solutyypeissä, makrofageissa ja keratinosyyteissä. Nämä tulokset korostavat biokuvantamisen tekniikan tärkeyttä varsinkin, kun kohteena on haastava kudos

Liver Biopsy

Liver biopsy is recommended as the gold standard method to determine diagnosis, fibrosis staging, prognosis and therapeutic indications in patients with chronic liver disease. However, liver biopsy is an invasive procedure with a risk of complications which can be serious. This book provides the management of the complications in liver biopsy. Additionally, this book provides also the references for the new technology of liver biopsy including the non-invasive elastography, imaging methods and blood panels which could be the alternatives to liver biopsy. The non-invasive methods, especially the elastography, which is the new procedure in hot topics, which were frequently reported in these years. In this book, the professionals of elastography show the mechanism, availability and how to use this technology in a clinical field of elastography. The comprehension of elastography could be a great help for better dealing and for understanding of liver biopsy

Hepatocellular Carcinoma

This open access book offers a comprehensive review of hepatocellular carcinoma (HCC) with a particular focus on the pathobiology and clinical aspects of the disease, including diagnosis and treatment. HCC is becoming one of the most common causes of cancer-related death worldwide. It is the fifth most common malignancy in men and the ninth in women, with an estimated 500,000 to 1 million new cases annually around the world. Independent of its cause, cirrhosis is considered a major clinical and histopathological risk factor for HCC development. Five percent of all cirrhotic patients develop HCC every year. Diagnostic tools for HCC include blood tests, high-quality imaging studies and liver biopsy. The treatment of HCC depends on the size and location of the HCC and includes surgical resection, liver transplantation, endovascular approaches, percutaneous ablation, and medical treatments. The book is organized into four parts – overview, diagnosis, management strategies, and recommendations – and aims to provide surgeons and clinicians with a valuable resource for complete and up-to-date research on the clinical aspects and management of HCC

Mechanochemical Regulation of Epithelial Tissue Remodeling: A Multiscale Computational Model of the Epithelial-Mesenchymal Transition Program

Epithelial-mesenchymal transition (EMT) regulates the cellular processes of migration, growth, and proliferation - as well as the collective cellular process of tissue remodeling - in response to mechanical and chemical stimuli in the cellular microenvironment. Cells of the epithelium form cell-cell junctions with adjacent cells to function as a barrier between the body and its environment. By distributing localized stress throughout the tissue, this mechanical coupling between cells maintains tensional homeostasis in epithelial tissue structures and provides positional information for regulating cellular processes. Whereas in vitro and in vivo models fail to capture the complex interconnectedness of EMT-associated signaling networks, previous computational models have succinctly reproduced components of the EMT program. In this work, we have developed a computational framework to evaluate the mechanochemical signaling dynamics of EMT at the molecular, cellular, and tissue scale. First, we established a model of cell-matrix and cell-cell feedback for predicting mechanical force distributions within an epithelial monolayer. These findings suggest that tensional homeostasis is the result of cytoskeletal stress distribution across cell-cell junctions, which organizes otherwise migratory cells into a stable epithelial monolayer. However, differences in phenotype-specific cell characteristics led to discrepancies in the experimental and computational observations. To better understand the role of mechanical cell-cell feedback in regulating EMT-dependent cellular processes, we introduce an EMT gene regulatory network of key epithelial and mesenchymal markers, E-cadherin and N-cadherin, coupled to a mechanically-sensitive intracellular signaling cascade. Together these signaling networks integrate mechanical cell-cell feedback with EMT-associated gene regulation. Using this approach, we demonstrate that the phenotype-specific properties collectively account for discrepancies in the computational and experimental observations. Additionally, mechanical cell-cell feedback suppresses the EMT program, which is reflected in the gene expression of the heterogeneous cell population. Together, these findings advance our understanding of the complex interplay in cell-cell and cell-matrix feedback during EMT of both normal physiological processes as well as disease progression

Hepatocellular Carcinoma

This open access book offers a comprehensive review of hepatocellular carcinoma (HCC) with a particular focus on the pathobiology and clinical aspects of the disease, including diagnosis and treatment. HCC is becoming one of the most common causes of cancer-related death worldwide. It is the fifth most common malignancy in men and the ninth in women, with an estimated 500,000 to 1 million new cases annually around the world. Independent of its cause, cirrhosis is considered a major clinical and histopathological risk factor for HCC development. Five percent of all cirrhotic patients develop HCC every year. Diagnostic tools for HCC include blood tests, high-quality imaging studies and liver biopsy. The treatment of HCC depends on the size and location of the HCC and includes surgical resection, liver transplantation, endovascular approaches, percutaneous ablation, and medical treatments. The book is organized into four parts – overview, diagnosis, management strategies, and recommendations – and aims to provide surgeons and clinicians with a valuable resource for complete and up-to-date research on the clinical aspects and management of HCC

State-of-the-art MR imaging in the work-up of primary hepatocellular tumors

Magnetic resonance (MR) imaging is an imaging modality that has evolved rapidly in the past two decades. The development of advanced hardware and new sophisticated pulse sequences have allowed faster imaging, with increased temporal and spatial resolution. This has resulted in the development and implementation of new acquisition techniques that facilitate improved visualisation of neoplastic processes. In addition, faster sequences enable multiphasic dynamic imaging after intravenous administration of contrast material, which results in better tumor characterisation and improved diagnostic confidence by the reading radiologist. The radiol

Comparison of proteins of the endoplasmic reticulum from control rat liver with proteins of the endoplasmic reticulum from dissected liver tumor nodules

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Network-based methods for biological data integration in precision medicine

[eng] The vast and continuously increasing volume of available biomedical data produced during the last decades opens new opportunities for large-scale modeling of disease biology, facilitating a more comprehensive and integrative understanding of its processes. Nevertheless, this type of modelling requires highly efficient computational systems capable of dealing with such levels of data volumes. Computational approximations commonly used in machine learning and data analysis, namely dimensionality reduction and network-based approaches, have been developed with the goal of effectively integrating biomedical data. Among these methods, network-based machine learning stands out due to its major advantage in terms of biomedical interpretability. These methodologies provide a highly intuitive framework for the integration and modelling of biological processes. This PhD thesis aims to explore the potential of integration of complementary available biomedical knowledge with patient-specific data to provide novel computational approaches to solve biomedical scenarios characterized by data scarcity. The primary focus is on studying how high-order graph analysis (i.e., community detection in multiplex and multilayer networks) may help elucidate the interplay of different types of data in contexts where statistical power is heavily impacted by small sample sizes, such as rare diseases and precision oncology. The central focus of this thesis is to illustrate how network biology, among the several data integration approaches with the potential to achieve this task, can play a pivotal role in addressing this challenge provided its advantages in molecular interpretability. Through its insights and methodologies, it introduces how network biology, and in particular, models based on multilayer networks, facilitates bringing the vision of precision medicine to these complex scenarios, providing a natural approach for the discovery of new biomedical relationships that overcomes the difficulties for the study of cohorts presenting limited sample sizes (data-scarce scenarios). Delving into the potential of current artificial intelligence (AI) and network biology applications to address data granularity issues in the precision medicine field, this PhD thesis presents pivotal research works, based on multilayer networks, for the analysis of two rare disease scenarios with specific data granularities, effectively overcoming the classical constraints hindering rare disease and precision oncology research. The first research article presents a personalized medicine study of the molecular determinants of severity in congenital myasthenic syndromes (CMS), a group of rare disorders of the neuromuscular junction (NMJ). The analysis of severity in rare diseases, despite its importance, is typically neglected due to data availability. In this study, modelling of biomedical knowledge via multilayer networks allowed understanding the functional implications of individual mutations in the cohort under study, as well as their relationships with the causal mutations of the disease and the different levels of severity observed. Moreover, the study presents experimental evidence of the role of a previously unsuspected gene in NMJ activity, validating the hypothetical role predicted using the newly introduced methodologies. The second research article focuses on the applicability of multilayer networks for gene priorization. Enhancing concepts for the analysis of different data granularities firstly introduced in the previous article, the presented research provides a methodology based on the persistency of network community structures in a range of modularity resolution, effectively providing a new framework for gene priorization for patient stratification. In summary, this PhD thesis presents major advances on the use of multilayer network-based approaches for the application of precision medicine to data-scarce scenarios, exploring the potential of integrating extensive available biomedical knowledge with patient-specific data