Decellularized scaffolds can serve as an excellent three-dimensional environment for cell repopulation. They maintain tissue-specific microarchitecture of extracellular matrix proteins with important spatial cues for cell adhesion, migration, growth, and differentiation. However, criteria for quality assessment of the three-dimensional structure of decellularized scaffolds are rather fragmented, usually study-specific, and mostly semi-quantitative. Thus, we aimed to develop a robust structural assessment system for decellularized porcine liver scaffolds. Five scaffolds of different quality were used to establish the new evaluation system. We combined conventional semi-quantitative scoring criteria with a quantitative scaffold evaluation based on automated image analysis. For the quantitation, we developed a specific open source software tool (ScaffAn) applying algorithms designed for texture analysis, segmentation, and skeletonization. ScaffAn calculates selected parameters characterizing structural features of porcine liver scaffolds such as the sinusoidal network. After evaluating individual scaffolds, the total scores predicted scaffold interaction with cells in terms of cell adhesion. Higher scores corresponded to higher numbers of cells attached to the scaffolds. Moreover, our analysis revealed that the conventional system could not identify fine differences between good quality scaffolds while the additional use of ScaffAn allowed discrimination. This led us to the conclusion that only using the combined score resulted in the best discrimination between different quality scaffolds. Overall, our newly defined evaluation system has the potential to select the liver scaffolds most suitable for recellularization, and can represent a step toward better success in liver tissue engineering.
In summary, the pharmacological activation of HIFs by 3,4-DHB administration, although it showed renoprotective effects in sepsis-related kidney injury, induced more severe problems in other organs such as the liver during sepsis, leading to increased mortality.
The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.
BackgroundThe MAPK-organizer 1 (MORG1) play a scaffold function in the MAPK and/or the PHD3 signalling paths. Recently, we reported that MORG1+/− mice are protected from renal injury induced by systemic hypoxia and acute renal ischemia-reperfusion injury via increased hypoxia-inducible factors (HIFs). Here, we explore whether MORG1 heterozygosity could attenuate renal injury in a murine model of lipopolysaccharide (LPS) induced endotoxemia.MethodsEndotoxemia was induced in mice by an intraperitoneal (i.p) application of 5 mg/kg BW LPS. The renal damage was estimated by periodic acid Schiff’s staining; renal injury was evaluated by detection of urinary and plasma levels of neutrophil gelatinase-associated lipocalin and albumin/creatinine ratio via ELISAs. Renal mRNA expression was assessed by real-time PCR, whereas the protein expression was determined by immunohistochemistry or Western blotting.ResultsLPS administration increased tubular injury, microalbuminuria, IL-6 plasma levels and renal TNF-α expression in MORG1+/+ mice. This was accompanied with enhanced infiltration of the inflammatory T-cells in renal tissue and activation of the NF-κB transcription factors. In contrast, endotoxemic MORG1+/− showed significantly less tubular injury, reduced plasma IL-6 levels, significantly decreased renal TNF-α expression and T-cells infiltration. In support, the renal levels of activated caspase-3 were lower in endotoxemic MORG1+/− mice compared with endotoxemic MORG1+/+ mice. Interestingly, LPS application induced a significantly higher accumulation of renal HIF-2α in the kidneys of MORG1+/− mice than in wild-type mice, accompanied with a diminished phosphorylation of IκB-α and IKK α,β and decreased iNOS mRNA in the renal tissues of the LPS-challenged MORG1+/− mice, indicating an inhibition of the NF-κB transcriptional activation.ConclusionsMORG1 heterozygosity protects against histological renal damage and shows anti-inflammatory effects in a murine endotoxemia model through modulation of HIF-2α stabilisation and/or simultaneous inhibition of the NF-κB signalling. Here, we show for the first time that MORG1 scaffold could represent the missing link between innate immunity and inflammation.Electronic supplementary materialThe online version of this article (10.1186/s12882-018-0826-4) contains supplementary material, which is available to authorized users.
Background: Biological organ engineering is a novel experimental approach to generate functional liver grafts by decellularization and repopulation. Currently, healthy organs of small or large animals and human organs with preexisting liver diseases are used to optimize decellularization and repopulation. However, the effects of morphological changes on allo-and xenogeneic cell-scaffold interactions during repopulation procedure, e.g., using scaffold-sections, are unknown. We present a sequential morphological workflow to identify murine liver scaffold-sections with well-preserved microarchitecture. Methods: Native livers (CONT, n ¼ 9) and livers with experimentally induced pathologies (hepatics steatosis: STEA, n ¼ 7; hepatic fibrosis induced by bile duct ligation: BDL, n ¼ 9; nodular regenerative hyperplasia induced by 90% partial hepatectomy: PH, n ¼ 8) were decellularized using SDS and Triton X-100 to generate cell-free scaffolds. Scaffold-sections were assessed using a sequential morphological workflow consisting of macroscopic, microscopic and morphological evaluation: (1) The scaffold was evaluated by a macroscopic decellularization score. (2) Regions without visible tissue remnants were localized for sampling and histological processing. Subsequent microscopical examination served to identify tissue samples without cell remnants. (3) Only cell-free tissue sections were subjected to detailed liver-specific morphological assessment using a histological and immunohistochemical decellularization score. Results: Decellularization was feasible in 33 livers, which were subjected to the sequential morphological workflow. In 11 of 33 scaffolds we achieved a good macroscopic decellularization result (CONT:
Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy.
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