Fibrosis is a general term to describe diseases that lead to an increase of connective tissue. This may lead to tissue remodelling, formation of permanent scar tissue and changes in the mechanical properties of the organ involved. Fibrosis is a result of deregulated wound healing process, which can be caused by injuries or various diseases, as well as chronic or inflammatory processes. Fibrosis can occur in various organs such as kidneys, heart, liver and lungs. The here presented work is focused on lung fibrosis. The lungs contain complex connective tissue structures whose function is to keep the airways open and elastic, thus enabling oxygen uptake and gas exchange. Several di↵erent fibre types such as elastin, fibrin and collagen are present within said connective tissue, and aid the aforementioned processes. However, they exhibit di↵erent responses, for instance while elastin degrades during inflammation, collagen deposition increases. This complex interplay of pathomechanisms acting on the composition and structure of the connective tissue within the lungs and the correlation to a specific disease has not yet been studied in detail. Therefore, the aim of the thesis is to establish a multi- modal analysis pipeline to provide a comprehensive analysis of lung fibrosis. To this end I utilized phase contrast micro-computed tomography to obtain structural information of the lung, histology to map the regions of collagen deposition, Fourier Transformed Infrared Spectroscopy to obtain the chemical fingerprint of a tissue, and atomic force microscopy to perform mechanical characterization of a tissue. The novelty of this approach does not lie in the application of specific single techniques, but in their spatial correlation to study the same tissue area. To validate the consistency and benefits of the pipeline, I performed studies on two di↵erent mouse models for lung fibrosis - one chemically induced, one using genetically modified mice. Based on the di↵erences in the involved pathomechanism in those two models, di↵erences in the composition of the lung fibrosis can also be assumed. Thus, these models were used to show-case the performance of the developed pipeline. I found that both models showed strong features of lung fibrosis. However, the typically applied histological scoring cannot distinguish between them. By applying the here presented analysis pipeline, I found significant differences between the fibrotic regions of the two models. Thus, I argue that this novel method presents a vital tool for the analysis of tissue specimens of various origin.
Fibrosi è un termine generico per descrivere malattie che portano ad un incremento del tessuto connettivo. Questa patologia può portare al rimodellamento dei tessuti, alla formazione di tessuti cicatriziali permanenti e ai cambiamenti nelle proprietà meccaniche dell'organo coinvolto. La fibrosi è il risultato di un processo di guarigione delle ferite deregolamentato, che può essere causato da lesioni o diverse malattie come processi cronici o infiammatori. Essa si può verificarsi in vari organi come reni, cuore, fegato e polmone. Il lavoro qui presentato è focalizzato, in particolare, sulla fibrosi polmonare. Il polmone contiene complesse strutture di tessuto connettivo per mantenere le vie aeree aperte ed elastiche consentendo l'assorbimento di ossigeno e lo scambio di gas. In questo organo sono presenti diversi tipi di fibre come elastina, fibrina e collagene. Mentre ad esempio l'elastina viene degradata in presenza di infiammazione, la deposizione di collagene aumenta. Questa complessa interazione del meccanismo patogenetico che agisce sulla composizione e sulla struttura del tessuto connettivo all'interno del polmone e la correlazione con una specifica malattia non è stata ancora studiata in dettaglio. Pertanto, lo scopo di questa tesi è quello di stabilire una pipeline multimodale per fornire un'analisi completa della fibrosi polmonare. A tal fine ho utilizzato la micro-CT a contrasto di fase per ottenere informazioni strutturali del polmone, l'istologia per mappare le regioni di deposizione di collagene, la spettroscopia infrarossa trasformata di Fourier per ottenere un'impronta chimica del tessuto e la microscopia a forza atomica per eseguire la caratterizzazione meccanica del tessuto polmonare. La novità di questo approccio non risiede nell'applicazione di singole tecniche specifiche, ma nella loro correlazione spaziale per studiare la stessa area di tessuto. Per convalidare la coerenza e i vantaggi di questa pipeline, sono stati condotti studi su due diversi modelli murini di fibrosi polmonare: uno indotto chimicamente, l'altro utilizzando topi geneticamente modificati. Sulla base delle differenze nel meccanismo patogenetico coinvolto in questi due modelli si possono ipotizzare delle differenze nella composizione della fibrosi polmonare. Pertanto, questi modelli sono stati utilizzati per testare il funzionamento della pipeline. I risultati hanno portato a concludere che entrambi i modelli mostrano forti caratteristiche di fibrosi polmonare. Lo scoring istologico tipicamente applicato tuttavia consente di distinguerli. Applicando la pipeline di analisi qui presentata, sono state riscontrate differenze significative tra le regioni fibrotiche nei due modelli. Pertanto, credo che questo nuovo metodo presenti uno strumento vitale per l'analisi di campioni di tessuto di varia origine.
Imaging e caraterizzazione di tessuti fibrotici / D'Amico, Lorenzo. - (2024 Mar 13).
Imaging e caraterizzazione di tessuti fibrotici
D'AMICO, LORENZO
2024-03-13
Abstract
Fibrosis is a general term to describe diseases that lead to an increase of connective tissue. This may lead to tissue remodelling, formation of permanent scar tissue and changes in the mechanical properties of the organ involved. Fibrosis is a result of deregulated wound healing process, which can be caused by injuries or various diseases, as well as chronic or inflammatory processes. Fibrosis can occur in various organs such as kidneys, heart, liver and lungs. The here presented work is focused on lung fibrosis. The lungs contain complex connective tissue structures whose function is to keep the airways open and elastic, thus enabling oxygen uptake and gas exchange. Several di↵erent fibre types such as elastin, fibrin and collagen are present within said connective tissue, and aid the aforementioned processes. However, they exhibit di↵erent responses, for instance while elastin degrades during inflammation, collagen deposition increases. This complex interplay of pathomechanisms acting on the composition and structure of the connective tissue within the lungs and the correlation to a specific disease has not yet been studied in detail. Therefore, the aim of the thesis is to establish a multi- modal analysis pipeline to provide a comprehensive analysis of lung fibrosis. To this end I utilized phase contrast micro-computed tomography to obtain structural information of the lung, histology to map the regions of collagen deposition, Fourier Transformed Infrared Spectroscopy to obtain the chemical fingerprint of a tissue, and atomic force microscopy to perform mechanical characterization of a tissue. The novelty of this approach does not lie in the application of specific single techniques, but in their spatial correlation to study the same tissue area. To validate the consistency and benefits of the pipeline, I performed studies on two di↵erent mouse models for lung fibrosis - one chemically induced, one using genetically modified mice. Based on the di↵erences in the involved pathomechanism in those two models, di↵erences in the composition of the lung fibrosis can also be assumed. Thus, these models were used to show-case the performance of the developed pipeline. I found that both models showed strong features of lung fibrosis. However, the typically applied histological scoring cannot distinguish between them. By applying the here presented analysis pipeline, I found significant differences between the fibrotic regions of the two models. Thus, I argue that this novel method presents a vital tool for the analysis of tissue specimens of various origin.File | Dimensione | Formato | |
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