Investigating pathological extracellular matrix architecture

• Autores: Enrico Almici
• Directores de la Tesis: Josep Samitier Martí (dir. tes.), Jordi Alcaraz (codir. tes.), Juan José Montero Boronat (codir. tes.)
• Lectura: En la Universitat de Barcelona ( España ) en 2022
• Idioma: inglés
• Tribunal Calificador de la Tesis: Sergio Cerutti (presid.), Elena Martínez Fraiz (secret.), Eugenio Martinelli (voc.)
• Programa de doctorado: Programa de Doctorado en Biomedicina por la Universidad de Barcelona
• Materias:
  • Ciencias de la vida
    • Biofísica
      • Física médica
  • Ciencias médicas
    • Patología
      • Oncología
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • The composition and architecture of the extracellular matrix (ECM), and their dynamic alterations, play an important regulatory role on numerous cellular processes. Furthermore, structural and biochemical properties of the ECM are central in regulating cell behavior via mechanical, chemical and topological cues detected by receptors in the cell membrane which induce cytoskeleton rearrangement and/or cell nucleus gene expression. Indeed, distinct ECM architectures are encountered in the native stroma, which depend on tissue type, function and composition. For instance, ECM anisotropy and stiffness are associated with altered ECM degradation and remodeling in cancer. In turn, this architecture favors tumor progression and invasion. Moreover, numerous diseases are associated with mutations in genes encoding ECM components, leading to deficient mechanical properties and altered ECM structure. Thus, there is an increasing interest to exploit and consolidate this knowledge to improve patients’ treatment and care. In this work, we focused on Collagen-VI related muscular dystrophies and Non-small cell lung cancer, to investigate in vitro and in patient tissues ECM-related biomarkers to be implemented in clinical setting. We employed automatic image segmentation to quantify fibrillar characteristics and investigated the association with the clinicopathological information from patients. Ultimately this analysis along with the tools presented is promising for addressing the need of novel descriptors, to stratify patients and evaluate their response to experimental treatments.