Publications

To date, strategies aiming to modulate cell to extracellular matrix (ECM) interactions during organoid derivation remain largely unexplored. Here renal decellularized ECM (dECM) hydrogels are fabricated from porcine and human renal cortex as biomaterials to enrich cell-to-ECM crosstalk during the onset of kidney organoid differentiation from human pluripotent stem cells (hPSCs). Renal dECM-derived hydrogels are used in combination with hPSC-derived renal progenitor cells to define new approaches for 2D and 3D kidney organoid differentiation, demonstrating that in the presence of these biomaterials the resulting kidney organoids exhibit renal differentiation features and the formation of an endogenous vascular component. Based on these observations, a new method to produce kidney organoids with vascular-like structures is achieved through the assembly of hPSC-derived endothelial-like organoids with kidney organoids in 3D. Major readouts of kidney differentiation and renal cell morphology are assessed exploiting these culture platforms as new models of nephrogenesis. Overall, this work shows that exploiting cell-to-ECM interactions during the onset of kidney differentiation from hPSCs facilitates and optimizes current approaches for kidney organoid derivation thereby increasing the utility of these unique cell culture platforms for personalized medicine. Natural hydrogels derived from decellularized porcine or human kidney tissues are used to generate kidney organoids from human pluripotent stem cells, resulting in the enrichment of organoids' endogenous vascular component and improved renal differentiation. Exploiting the autonomous capacity of kidney organoids to exhibit endogenous vascularization in combination with these biomaterials, a novel approach is established to generate endothelial-kidney assembloids showing vascular-like structures. image

JTD Keywords: Angiogenesis, Animals, Assembloids, Biocompatible materials, Cell differentiation, Decellularized extracellular matrix, Extracellular matrix, Extracellular matrix-derived hydrogels, Extracellular matrix‐derived hydrogels, Extracellular-matrix, Human pluripotent stem cells, Humans, Hydrogels, Kidney, Kidney organoids, Neovascularization, physiologic, Organoids, Pluripotent stem cells, Pluripotent stem-cells, Swine, Tissu, Tissue engineering, Vascularizatio, Vascularization

Objectives This study aimed to compare the performance of a xenograft (XG) and a biomimetic synthetic graft (SG) in three-wall alveolar defects in minipigs by means of 3D computerised tomography and histology. Materials and methods Eight minipigs were used. A total of eight defects were created in the jaw of each animal, three of which were grafted with XGs, three with SGs, and two were left empty as a negative control. The allocation of the different grafts was randomised. Four animals were euthanised at 6 weeks and four at 12 weeks. The grafted volume was then measured by spiral computed tomography to assess volume preservation. Additionally, a histological analysis was performed in undecalcified samples by backscattered scanning electron microscopy and optical microscopy after Masson's trichrome staining. Results A linear mixed-effects model was applied considering four fixed factors (bone graft type, regeneration time, anatomic position, and maxilla/mandible) and one random factor (animal). The SG exhibited significantly larger grafted volume (19%) than the XG. The anterior sites preserved better the grafted volume than the posterior ones. Finally, regeneration time had a positive effect on the grafted volume. Histological observations revealed excellent osseointegration and osteoconductive properties for both biomaterials. Some concavities found in the spheroidal morphologies of SGs were associated with osteoclastic resorption. Conclusions Both biomaterials met the requirements for bone grafting, i.e. biocompatibility, osseointegration, and osteoconduction. Granule morphology was identified as an important factor to ensure a good volume preservation.

JTD Keywords: bone graft, bone regeneration, in vivo, miniature swine, synthetic graft, 3-dimensional changes, Anorganic bovine bone, Autogenous bone, Bio-oss, Biomaterials, Bone graft, Bone regeneration, Calcium-phosphate, Hydroxyapatite, In vivo, Miniature swine, Sinus floor augmentation, Substitute, Synthetic graft, Volume, Xenograft

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

JTD Keywords: authenticity, classification, electronic-nose, feature extraction, food analysis, gc-ims, headspace, least-squares, models, pld-da, pre-processing, quality, sensory analysis, wine, Discriminant analysis, Feature extraction, Food analysis, Gas chromatography-mass spectrometry, Gc-ims, Hs-gc-ims, Ion mobility spectrometry, Odorants, Pld-da, Pre-processing, Workflow