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Many physiological processes and pathological phenomena in the liver tissue are spatially heterogeneous. At a local scale, biomarkers can be quantified along the axis of the blood flow, from portal fields (PFs) to central veins (CVs), i.e., in zonated form. This requires detecting PFs and CVs. However, manually annotating these structures in multiple whole-slide images is a tedious task. We describe and evaluate a fully automated method, based on a convolutional neural network, for simultaneously detecting PFs and CVs in a single stained section. Trained on scans of hematoxylin and eosin-stained liver tissue, the detector performed well with an F1 score of 0.81 compared to annotation by a human expert. It does, however, not generalize well to previously unseen scans of steatotic liver tissue with an F1 score of 0.59. Automated PF and CV detection eliminates the bottleneck of manual annotation for subsequent automated analyses, as illustrated by two proof-of-concept applications: We computed lobulus sizes based on the detected PF and CV positions, where results agreed with published lobulus sizes. Moreover, we demonstrate the feasibility of zonated quantification of biomarkers detected in different stainings based on lobuli and zones obtained from the detected PF and CV positions. A negative control (hematoxylin and eosin) showed the expected homogeneity, a positive control (glutamine synthetase) was quantified to be strictly pericentral, and a plausible zonation for a heterogeneous F4/80 staining was obtained. Automated detection of PFs and CVs is one building block for automatically quantifying physiologically relevant heterogeneity of liver tissue biomarkers. Perspectively, a more robust and automated assessment of zonation from whole-slide images will be valuable for parameterizing spatially resolved models of liver metabolism and to provide diagnostic information.

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 = 9) and livers with experimentally induced pathologies (hepatics steatosis: STEA, n = 7; hepatic fibrosis induced by bile duct ligation: BDL, n = 9; nodular regenerative hyperplasia induced by 90% partial hepatectomy: PH, n = 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: 3 scaffolds; STEA: 3 scaffolds; BDL: 3 scaffolds; PH: 2 scaffolds). The microscopic assessment resulted in the selection of 88 cell-free tissue sections (CONT: 15 sections; STEA: 38 sections; BDL: 30 sections; PH: 5 sections). In 27 of those sections we obtained a good histological decellularization result (CONT: 3 sections; STEA: 6 sections; BDL: 17 sections; PH: 1 section). All experimental groups contained sections with a good immunohistochemical decellularization result (CONT: 6 sections; STEA: 5 sections; BDL: 4 sections; PH: 1 section). Discussion: Decellularization was possible in all experimental groups, irrespectively of the underlying morphological alteration. Furthermore, our proposed sequential morphological workflow was suitable to detect tissue sections with well-preserved hepatic microarchitecture, as needed for further repopulation experiments.

BACKGROUND: Prostate cancer (PCa) is the most common type of cancer among men in Western countries. Despite numerous therapeutic options, few treatments are available for patients with end-stage disease. In the present study, different somatostatin receptors (SSTs) and the chemokine receptor CXCR4 were evaluated for their suitability as novel therapeutic targets in PCa. MATERIALS AND METHODS: The expression of SST subtypes 1, 2A, 3, and 5 and of CXCR4 was evaluated in 276 PCa tumor samples on a tissue microarray (TMA) in 23 whole-block tumor samples and in 3 PCa cell lines by immunohistochemistry using well-characterized monoclonal antibodies. RESULTS: Overall, the frequency and intensity of expression of SSTs and CXCR4 were very low among the PCa samples investigated. Specifically, SST5, SST2A, and SST3 were expressed, albeit at low intensity, in 10.5%, 9.1%, and 0.7% of the TMA samples, respectively. None of the TMA samples showed SST1 or CXCR4 expression. Only a single small-cell-type neuroendocrine carcinoma that was coincidentally included among the whole-block samples exhibited strong SST2A, SST5, and CXCR4 and moderate SST3 expression. Independent of the tumor cells, the tumor capillaries in many of the PCa samples were strongly positive for SST2A, SST3, SST5, or CXCR4 expression. SST expression in the tumor cells was associated with advanced tumor grade and stage. CONCLUSION: Overall, SST and CXCR4 expression levels are clearly of no therapeutic relevance in PCa. SST- or CXCR4-based therapy might be feasible, however, in rare cases of small-cell-type neuroendocrine carcinoma of the prostate.

Question: Donor liver organs with moderate to high fat content (i.e. steatosis) suffer from an enhanced susceptibility to ischemia/reperfusion injury (IRI) during liver transplantation. Responsible for the cellular injury is an increased level of oxidative stress, however the underlying mechanistic network is still not fully understood. Method: We developed a phenomenological mathematical model of key processes of hepatic lipid metabolism linked to pathways of oxidative stress. The model allows the simulation of hypoxia (i.e. ischemia-like conditions) and reoxygenation (i.e. reperfusion-like conditions) for various degrees of steatosis and predicts the level of hepatic lipid peroxidation (LPO) as a marker of cell damage caused by oxidative stress. Results & Conclusions: Our modeling results show that the underlying feedback loop between the formation of reactive oxygen species (ROS) and LPO leads to bistable systems behavior. Here, the first stable state corresponds to a low basal level of ROS production. The system is directed to this state for healthy, non-steatotic livers. The second stable state corresponds to a high level of oxidative stress with an enhanced formation of ROS and LPO. This state is reached, if steatotic livers with a high fat content undergo a hypoxic phase. Theoretically, our proposed mechanistic network would support the prediction of the maximal tolerable ischemia time for steatotic livers: Exceeding this limit during the transplantation process would lead to severe IRI and a considerable increased risk for liver failure. Keywords: Steatosis, Hepatic fatty acid metabolism, Oxidative stress, Reactive oxygen species, Lipid peroxidation

Introduction: The registration of histological whole slide images is an important prerequisite for modern histological image analysis. A partial reconstruction of the original volume allows e.g. colocalization analysis of tissue parameters or high-detail reconstructions of anatomical structures in 3D. Methods: In this paper, we present an automatic staining-invariant registration method, and as part of that, introduce a novel vessel-based rigid registration algorithm using a custom similarity measure. The method is based on an iterative best-fit matching of prominent vessel structures. Results: We evaluated our method on a sophisticated synthetic dataset as well as on real histological whole slide images. Based on labeled vessel structures we compared the relative differences for corresponding structures. The average positional error was close to 0, the median for the size change factor was 1, and the median overlap was 0.77. Conclusion: The results show that our approach is very robust and creates high quality reconstructions. The key element for the resulting quality is our novel rigid registration algorithm.

Histological image analysis methods often employ machine-learning classifiers in order to adapt to the huge variability of histological images. To train these classifiers, the user must select samples of the relevant image objects. In the field of active learning, there has been much research on sampling strategies that exploit the uncertainty of the current classification in order to guide the user to maximally informative samples. Although these approaches have the potential to reduce the training effort and increase the classification accuracy, they are very rarely employed in practice. In this paper, we investigate the practical value of uncertainty sampling in the context of histological image analysis. To obtain practically meaningful results, we have devised an evaluation algorithm that simulates the way a human interacts with a user interface. The results show that uncertainty sampling outperforms common random or error sampling strategies by achieving more accurate classification results with a lower number of training images.