Exploring combined influences of material topography, stiffness and chemistry on cell behavior at biointerfaces

Complexe materiaalgrensvlakken zijn ontworpen voor het exploreren van cel-materiaal interactions om de relatie tussen biomateriaal eigenschappen en biologische prestatie blood te leggen welke in de toekomst gebruikt kan worden voor geadvanceerde weefselbouw en regeneratieve geneeskunde. Cellen nemen altijd verscheidene signalen op uit hun microomgeving en hierdoor moeten we vele verschillende parameters van de biomaterialen incorporeren zodat we deze gedragingen zo accuraat mogelijk kunnen bestuderen en de toestand van de cel koppelen aan de interactie die wordt aangegaan met de materiaal eigenschappen. Om controle te verkrijgen over cel gedrag en deze te kunnen sturen, is het cruciaal om optimaal celgedrag te bepalen waarbij in detail naar een groot aantal parameterwaardes wordt gekeken binnen een groot spreidingsgebied. Naar verwachting illusteren de bevindingen beschreven in dit proefschrift andere onderzoekers dat celgedrag in een complexere mix van stimuli moet worden bestudeerd. Ons werk zoals hier beschreven gaat niet alleen om het verkrijgen van meer kennis op het gebied van celgedrag omder invloed van materiaaleigenschappen maar ook om het toe te passen als manier om hoogwaardige biomaterialen te ontwikkelen voor commerciele doeleinden.Complex material interfaces were designed and developed to explore cell-material interactions and elicit the relationship between biomaterial properties and biological performance to be used in the future as possible advanced tissue engineering and regenerative medicine approaches. Cells always integrate multiple cues from their microenvironment and we should include as many different parameters in our biomaterials as possible and study these for an accurate description of the cell state as a consequence of interacting with a material. In order to highly control cellular behavior, it is crucial to identify the optimal cell response by studying a detailed interaction between cells and materials over a broad range. The findings in this thesis are expected to act as a catalyst for other researchers to efficiently explore cell behavior from a more complex point of view. Our work is not just to obtain more knowledge on cell and material interactions, but to apply this knowledge for accelerating the development of high-performance biomaterials, which can become commercially available

MOGAN: Morphologic-structure-aware Generative Learning from a Single Image

In most interactive image generation tasks, given regions of interest (ROI) by users, the generated results are expected to have adequate diversities in appearance while maintaining correct and reasonable structures in original images. Such tasks become more challenging if only limited data is available. Recently proposed generative models complete training based on only one image. They pay much attention to the monolithic feature of the sample while ignoring the actual semantic information of different objects inside the sample. As a result, for ROI-based generation tasks, they may produce inappropriate samples with excessive randomicity and without maintaining the related objects' correct structures. To address this issue, this work introduces a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances and reliable structures based on only one image. For training for ROI, we propose to utilize the data coming from the original image being augmented and bring in a novel module to transform such augmented data into knowledge containing both structures and appearances, thus enhancing the model's comprehension of the sample. To learn the rest areas other than ROI, we employ binary masks to ensure the generation isolated from ROI. Finally, we set parallel and hierarchical branches of the mentioned learning process. Compared with other single image GAN schemes, our approach focuses on internal features including the maintenance of rational structures and variation on appearance. Experiments confirm a better capacity of our model on ROI-based image generation tasks than its competitive peers

Decoupling the Amplitude and Wavelength of Anisotropic Topography and the Influence on Osteogenic Differentiation of Mesenchymal Stem Cells Using a High-Throughput Screening Approach

High-throughput screening (HTS) methods based on anisotropically topography gradients have been broadly used to investigate the interactions between cells and biomaterials. However, few studies focus on the optimum parameters of topography for osteogenic differentiation because the structures of topography are complex with multiple combinations of parameters. In this study, we developed polydimethylsiloxane (PDMS)-based wrinkled topography gradients (amplitudes between 144 and 2854 nm and wavelengths between 0.91 and 13.62 mu m) and decoupled the wavelength and amplitude via imprinting lithography and shielded plasma oxidation. The PDMS wrinkle gradient was then integrated with the bottomless 96-well plate to constitute the wrinkled HTS platform, which consists of 70 different wrinkle parameters. From the in vitro culture of bone marrow stem cells, it was observed that aligned topography has an important influence on the macroscopic cell behavior (i.e., cell area, elongation, and nucleus area). Furthermore, the optimum wrinkle parameter (wavelength: 1.91 mu m; amplitude: 360 nm) for osteogenic differentiation of stem cells was determined via this screening plate approach. This screening platform is not only beneficial for a better understanding of the interactions between topography and biomaterials but also advances the development of bone tissue engineering developments

Development of an Aptamer-Conjugated Polyrotaxane-Based Biodegradable Magnetic Resonance Contrast Agent for Tumor-Targeted Imaging

Gadolinium-based magnetic resonance imaging (MRI) contrast agents with biodegradability, biosafety, and high efficiency are highly desirable for tumor diagnosis. Herein, a biodegradable, AS1411-conjugated, α-cyclodextrin polyrotaxane-based MRI contrast agent (AS1411-G2­(DTPA-Gd)-SS-PR) was developed for targeted imaging of cancer. The polyrotaxane-based contrast agent was achieved by the complexation of α-cyclodextrin (α-CD) and a linear poly­(ethylene glycol) (PEG) chain containing disulfide linkages at two terminals. The disulfides enable the dethreading of the polyrotaxane into excretable small units due to cleavage of the disulfide linkages by reducing agents such as intracellular glutathione (GSH). Furthermore, the second-generation lysine dendron conjugated with gadolinium chelates and AS1411, a G-quadruplex oligonucleotide that has high binding affinity to nucleolin generally presenting a high level on the surface of tumor cells, coupled to the α-CD via click chemistry. The longitudinal relaxivity of AS1411-G2­(DTPA-Gd)-SS-PR (11.7 mM–1 s–1) was two times higher than the clinically used Gd-DTPA (4.16 mM–1 s–1) at 0.5 T. The in vitro degradability was confirmed by incubating with 10 mM 1,4-dithiothreitol (DTT). Additionally, the cytotoxicity, histological assessment, and gadolinium retention studies showed that the prepared polyrotaxane-based contrast agent had a superior biocompatibility and was predominantly cleared renally without long-term accumulation toxicity. Importantly, AS1411-G2­(DTPA-Gd)-SS-PR displayed the enhanced performance in MRI of breast cancer cells in vitro as well as a subcutaneous breast tumor in vivo due to the targeting ability of the AS1411 aptamer. The enhanced performance was due to efficient multivalent interactions with tumor cells, producing faster accumulation and longer contrast imaging time at the tumor site. This work clearly confirms that the specially designed and fabricated α-CD-based polyrotaxane is a promising contrast agent with an excellent contrast imaging performance and biosafety for tumor MR imaging