3D bioprinting is an innovative manufacturing technology that has gained significant attention over the last few decades for the fabrication of functional biological tissues and biology-related applications. Its success derives from the numerous advantages it offers compared to traditional regenerative medicine techniques, including process automation, reduction of inter-operator variability and manual errors, increased throughput, higher achievable cell concentrations, and enhanced spatial precision in cell deposition. 3D bioprinting encompasses different technologies, such as ejection-based techniques (e.g., extrusion bioprinting, inkjet bioprinting, and drop-on-demand) and photopolymerization-based techniques (e.g., stereolithography and two-photon polymerization). The latter provides remarkable advantages in terms of printing precision and spatial resolution, enabling the fabrication of micro- and nano-scale structures that can interact with cells and their components. Such structures can deliver geometrical, mechanical, and topological cues, thereby influencing cellular behavior in a controlled manner. This doctoral thesis explores the opportunities offered by two-photon polymerization (2PP) 3D printing, focusing on two main application domains: i) the fabrication of microstructures and microdevices for cell entrapment, contributing to the development of a biosensing chip where cells act as sensing elements in line with the biointelligence paradigm; and ii) the fabrication of biological tissues within the broader field of regenerative medicine. The thesis presents the use of 2PP for the fabrication of microstructures and investigates the effects of microgeometrical patterns on cell proliferation and spatial distribution. In this context, novel methods are proposed for quantifying cell concentration in noisy microscopy images and for analyzing spatial cellular behavior. Additionally, the work addresses the limitations associated with a specific photoresist, which, while suitable for microscopy imaging and light-based measurements, proved challenging to print and prone to instabilities. Parameter optimization strategies are developed to fabricate stable structures, alongside a deep-learning-based in situ monitoring framework for the early detection of deviations from the nominal shape. Moreover, the fabricated microstructures are successfully integrated with a microdevice for dynamic cell seeding, providing a proof-of-concept for cell-encapsulating microstructures. Finally, the thesis introduces a preliminary attempt to develop a digital twin of a bioprinted construct. Specifically, a computational model of nutrient diffusion and consumption is proposed to estimate the impact of the 3D-printed geometry on cell proliferation and necrosis. By predicting cell death within the construct, the model represents a promising tool for the rational design of vascularization networks in 3D bioprinted tissues.
Il bioprinting è una tecnologia di manifattura innovativa, che ha ricevuto particolare attenzione negli ultimi decenni per la fabbricazione di tessuti biologici funzionali e altre applicazioni nel campo della biologia. Questo successo deriva dai numerosi vantaggi che offre rispetto alle tradizionali tecniche di medicina rigenerativa: automatizzazione del processo, riduzione di variabilità tra operatori ed errori manuali, maggiore produttività, possibilità di ottenere concentrazioni cellulari più elevate e maggiore precisione spaziale nella deposizione delle cellule. Il bioprinting è costituito da diverse tecnologie, quali tecniche basate su deposizione di materiale (estrusione, inkjet, drop-on-demand) e tecniche basate su foto-polimerizzazione (stereo-litografia e foto-polimerizzazione a due fotoni). Quest'ultima offre un insieme di vantaggi in termini di precisione di stampa e risoluzione spaziale, consentendo così la fabbricazione di strutture alla micro e nano scala, che possano interagire con le cellule e i loro componenti. Queste strutture forniscono stimoli geometrici, meccanici e topologici, influenzando così il comportamento cellulare in maniera controllata e modulabile. Questa tesi di dottorato esplora le opportunità offerte dalla stampa 3D mediante foto-polimerizzazione a due fotoni (2PP), focalizzandosi su due tematiche principali: i) La fabbricazione di microstrutture e micro-dispositivi per intrappolamento cellulare, contribuendo allo sviluppo di un bio-sensore dove le cellule fungono da elementi sensibili, secondo il paradigma della bio-intelligenza. ii) La fabbricazione di tessuti biologici nell'ambito della medicina rigenerativa. La tesi presenta l'uso della 2PP per la fabbricazione di microstrutture ed investiga gli effetti delle micro-geometrie sulla proliferazione e distribuzione cellulare. In questo contesto vengono proposti metodi per quantificare le cellule in immagini da microscopia e per analizzare il comportamento spaziale delle cellule. Questo lavoro di dottorato considera le limitazioni associate alla stampa di specifiche resine foto-sensibili, adatte alle ispezioni mediante microscopia e misure ottiche, ma difficili da stampare e inclini ad instabilità. Vengono fornite strategie di ottimizzazione dei parametri per la fabbricazione di strutture stabili, ed un approccio di monitoraggio in-situ basato su deep-learning per identificare prontamente le deviazioni dalla geometria nominale. Inoltre, le microstrutture fabbricate sono inserite in un micro-dispositivo per semina cellulare dinamica, fornendo un proof-of-concept di microstrutture in grado di intrappolare le cellule. Infine, la tesi introduce un approccio allo sviluppo di gemelli digitali di costrutti bio-stampati. Viene proposto un modello computazionale di diffusione e consumo di nutrienti per stimare l'effetto della geometria 3D del costrutto su proliferazione cellulare e necrosi. La predizione della morte cellulare nei costrutti rappresenta uno strumento valido per la progettazione di reti vascolari in tessuti bio-stampati.
Opportunities and challenges of high resolution bioprinting via two-photon polymerization
GIRONI, PATRIZIA
2025/2026
Abstract
3D bioprinting is an innovative manufacturing technology that has gained significant attention over the last few decades for the fabrication of functional biological tissues and biology-related applications. Its success derives from the numerous advantages it offers compared to traditional regenerative medicine techniques, including process automation, reduction of inter-operator variability and manual errors, increased throughput, higher achievable cell concentrations, and enhanced spatial precision in cell deposition. 3D bioprinting encompasses different technologies, such as ejection-based techniques (e.g., extrusion bioprinting, inkjet bioprinting, and drop-on-demand) and photopolymerization-based techniques (e.g., stereolithography and two-photon polymerization). The latter provides remarkable advantages in terms of printing precision and spatial resolution, enabling the fabrication of micro- and nano-scale structures that can interact with cells and their components. Such structures can deliver geometrical, mechanical, and topological cues, thereby influencing cellular behavior in a controlled manner. This doctoral thesis explores the opportunities offered by two-photon polymerization (2PP) 3D printing, focusing on two main application domains: i) the fabrication of microstructures and microdevices for cell entrapment, contributing to the development of a biosensing chip where cells act as sensing elements in line with the biointelligence paradigm; and ii) the fabrication of biological tissues within the broader field of regenerative medicine. The thesis presents the use of 2PP for the fabrication of microstructures and investigates the effects of microgeometrical patterns on cell proliferation and spatial distribution. In this context, novel methods are proposed for quantifying cell concentration in noisy microscopy images and for analyzing spatial cellular behavior. Additionally, the work addresses the limitations associated with a specific photoresist, which, while suitable for microscopy imaging and light-based measurements, proved challenging to print and prone to instabilities. Parameter optimization strategies are developed to fabricate stable structures, alongside a deep-learning-based in situ monitoring framework for the early detection of deviations from the nominal shape. Moreover, the fabricated microstructures are successfully integrated with a microdevice for dynamic cell seeding, providing a proof-of-concept for cell-encapsulating microstructures. Finally, the thesis introduces a preliminary attempt to develop a digital twin of a bioprinted construct. Specifically, a computational model of nutrient diffusion and consumption is proposed to estimate the impact of the 3D-printed geometry on cell proliferation and necrosis. By predicting cell death within the construct, the model represents a promising tool for the rational design of vascularization networks in 3D bioprinted tissues.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/246390