Targeting Complement C5a Receptor 1 for the Treatment of Immunosuppression in Sepsis.

Complement factor C5a was originally identified as a powerful promoter of inflammation through activation of the C5a receptor 1 (C5ar1). Recent evidence suggests involvement of C5a not only in pro- but also in anti-inflammatory signaling. The present study aims to unveil the role of C5ar1 as potential therapeutic target in a murine sepsis model. Our study discloses a significantly increased survival in models of mild to moderate but not severe sepsis of C5ar1-deficient mice. The decreased mortality of C5ar1-deficient mice is accompanied by improved pathogen clearance and largely preserved liver function. C5ar1-deficient mice exhibited a significantly increased production of the pro-inflammatory mediator interferon-γ (IFN-γ) and a decreased production of the anti-inflammatory cytokine interleukin-10 (IL-10). Together, these data uncover C5a signaling as a mediator of immunosuppressive processes during sepsis and describe the C5ar1 and related changes of the IFN-γ to IL-10 ratio as markers for the immunological (dys)function accompanying sepsis.

Enhanced segmentation of label-free cells for automated migration and interaction tracking.

In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time-lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well-known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label-free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell-cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.

Untangling cell tracks: Quantifying cell migration by time lapse image data analysis.

Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. © 2017 International Society for Advancement of Cytometry.

FHR3 Blocks C3d-Mediated Coactivation of Human B Cells.

The autoimmune renal disease deficient for complement factor H-related (CFHR) genes and autoantibody-positive form of hemolytic uremic syndrome is characterized by the presence of autoantibodies specific for the central complement regulator, factor H, combined with a homozygous deficiency, mostly in CFHR3 and CFHR1 Because FHR3 and FHR1 bind to C3d and inactivated C3b, which are ligands for complement receptor type 2 (CR2/CD21), the aim of the current study was to examine whether FHR3-C3d or FHR1-C3d complexes modulate B cell activation. Laser-scanning microscopy and automated image-based analysis showed that FHR3, but not FHR1 or factor H, blocked B cell activation by the BCR coreceptor complex (CD19/CD21/CD81). FHR3 bound to C3d, thereby inhibiting the interaction between C3d and CD21 and preventing colocalization of the coreceptor complex with the BCR. FHR3 neutralized the adjuvant effect of C3d on B cells, as shown by inhibited intracellular CD19 and Akt phosphorylation in Raji cells, as well as Ca(2+) release in peripheral B cells. In cases of CFHR3/CFHR1 deficiency, the FHR3 binding sites on C3d are occupied by factor H, which lacks B cell-inhibitory functions. These data provide evidence that FHR3, which is absent in patients with the autoimmune form of hemolytic uremic syndrome, is involved in B cell regulation.

Fully Automated Evaluation of Total Glomerular Number and Capillary Tuft Size in Nephritic Kidneys Using Lightsheet Microscopy.

The total number of glomeruli is a fundamental parameter of kidney function but very difficult to determine using standard methodology. Here, we counted all individual glomeruli in murine kidneys and sized the capillary tufts by combining in vivo fluorescence labeling of endothelial cells, a novel tissue-clearing technique, lightsheet microscopy, and automated registration by image analysis. Total hands-on time per organ was <1 hour, and automated counting/sizing was finished in <3 hours. We also investigated the novel use of ethyl-3-phenylprop-2-enoate (ethyl cinnamate) as a nontoxic solvent-based clearing reagent that can be handled without specific safety measures. Ethyl cinnamate rapidly cleared all tested organs, including calcified bone, but the fluorescence of proteins and immunohistochemical labels was maintained over weeks. Using ethyl cinnamate-cleared kidneys, we also quantified the average creatinine clearance rate per glomerulus. This parameter decreased in the first week of experimental nephrotoxic nephritis, whereas reduction in glomerular numbers occurred much later. Our approach delivers fundamental parameters of renal function, and because of its ease of use and speed, it is suitable for high-throughput analysis and could greatly facilitate studies of the effect of kidney diseases on whole-organ physiology.

Image-based systems biology of infection.

The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image-based systems biology approach. This review demonstrates how image-based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach--imaging, quantitative characterization, and modeling--and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image-derived data to simulate host-pathogen interactions in silico.

Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens.

Hyperspectral unmixing is an unsupervised algorithm to calculate a bilinear model of spectral endmembers and abundances of components from Raman images. Thirty-nine Raman images were collected from six glioma brain tumor specimens. The tumor grades ranged from astrocytoma WHO II to glioblastoma multiforme WHO IV. The abundance plots of the cell nuclei were processed by an image segmentation procedure to determine the average nuclei size, the number of nuclei, and the fraction of nuclei area. The latter two morphological parameters correlated with the malignancy. A combination of spectral unmixing and non-negativity constrained linear least squares fitting is introduced to assess chemical parameters. First, endmembers of the most abundant and most dissimilar components were defined that represent all data sets. Second, the content of the obtained components' proteins, nucleic acids, lipids, and lipid to protein ratios were determined in all Raman images. Except for the protein content, all chemical parameters correlated with the malignancy. We conclude that the morphological and chemical information offer new ways to develop Raman-based classification approaches that can complement diagnosis of brain tumors. The role of non-linear Raman modalities to speed-up image acquisition is discussed.

Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

Contribution of radixin and ezrin to the maintenance of hepatocytes' excretory function in health and disease.

Background & aims: Excretory liver failure is frequently associated with poor prognosis in critically ill patients. It is characterized by the loss of canalicular membrane export pumps at the hepatocyte membrane. The membrane export pump Multidrug resistant-associated protein (MRP) 2 is pivotal in hepatocytes for brushed membrane morphology and transport of various metabolites. In addition, MRP2 anchoring proteins of the Ezrin/Radixin/Moesin (ERM) family are crucial for the correct MRP2 location, integration, and function in different tissues. In hepatocytes, altered ERM signaling is elementary for developing excretory liver failure. Methods: Polarized human HepaRG cells, primary human hepatocytes, and hepatocyte-specific Ezrin knockout mice are employed to investigate ERM expression and function in health and the bile duct ligation model of obstructive cholestasis. Results: ERM-scaffolding protein Ezrin has no relevant function in maintaining the canalicular structure in hepatocytes during health and disease. Conclusions: Homeostasis of the canalicular pole in hepatocytes is maintained exclusively by Radixin but not Ezrin, and Radixin dysfunction promotes cholestasis.

Sodium thiosulfate refuels the hepatic antioxidant pool reducing ischemia-reperfusion-induced liver injury.

Ischemia-reperfusion injury is a critical liver condition during hepatic transplantation, trauma, or shock. An ischemic deprivation of antioxidants and energy characterizes liver injury in such cases. In the face of increased reactive oxygen production, hepatocytes are vulnerable to the reperfusion driving ROS generation and multiple cell-death mechanisms. In this study, we investigate the importance of hydrogen sulfide as part of the liver's antioxidant pool and the therapeutic potency of the hydrogen sulfide donors sodium sulfide (Na2S, fast releasing) and sodium thiosulfate (STS, Na2S2O3, slow releasing). The mitoprotection and toxicity of STS and Na2S were investigated on isolated mitochondria and a liver perfusion oxidative stress model by adding text-butyl hydroperoxide and hydrogen sulfide donors. The respiratory capacity of mitochondria, hepatocellular released LDH, glutathione, and lipid-peroxide levels were quantified. In addition, wild-type and cystathionine-γ-lyase knockout mice were subjected to warm selective ischemia-reperfusion injury by clamping the main inflow for 1 h followed by reperfusion of 1 or 24 h. A subset of animals was treated with STS shortly before reperfusion. Glutathione, plasma ALT, and lipid-peroxide levels were investigated alongside mitochondrial changes in structure (electron microscopy) and function (intravital microscopy). Liver tissue necrosis quantified 24 h after reperfusion indicates the net effects of the treatment on the organ. STS refuels and protects the endogenous antioxidant pool during liver ischemia-reperfusion injury. In addition, STS-mediated ROS scavenging significantly reduced lipid peroxidation and mitochondrial damage, resulting in better molecular and histopathological preservation of the liver tissue architecture. STS prevents tissue damage in liver ischemia-reperfusion injury by increasing the liver's antioxidant pool, thereby protecting mitochondrial integrity.

Automated characterisation of neutrophil activation phenotypes in ex vivo human Candida blood infections.

Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour - for instance in terms of their speed, dynamic cell morphology - that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.

An Image Analysis Pipeline for Quantifying the Features of Fluorescently-Labeled Biomolecular Condensates in Cells.

Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated with pediatric leukemia inspired us to develop an objective and reliable computational approach for such analyses. The NUP98-HOXA9 FO forms hundreds of punctate transcriptional condensates in cells, leading to hematopoietic cell transformation and leukemogenesis. To quantify the features of these puncta and derive the associated thermodynamic parameters, we developed a live-cell fluorescence microscopy image processing pipeline based on existing methodologies and open-source tools. The pipeline quantifies the numbers and volumes of puncta and fluorescence intensities of the fluorescently-labeled biomolecule(s) within them and generates reports of their features for hundreds of cells. Using a standard curve of fluorescence intensity versus protein concentration, the pipeline determines the apparent molar concentration of fluorescently-labeled biomolecules within and outside of puncta and calculates the partition coefficient (Kp) and Gibbs free energy of transfer (ΔGTr), which quantify the favorability of a labeled biomolecule partitioning into puncta. In addition, we provide a library of R functions for statistical analysis of the extracted measurements for certain experimental designs. The source code, analysis notebooks, and test data for the Punctatools pipeline are available on GitHub: https://github.com/stjude/punctatools. Here, we provide a protocol for applying our Punctatools pipeline to extract puncta features from fluorescence microscopy images of cells.

Phase Separation Mediates NUP98 Fusion Oncoprotein Leukemic Transformation.

NUP98 fusion oncoproteins (FO) are drivers in pediatric leukemias and many transform hematopoietic cells. Most NUP98 FOs harbor an intrinsically disordered region from NUP98 that is prone to liquid-liquid phase separation (LLPS) in vitro. A predominant class of NUP98 FOs, including NUP98-HOXA9 (NHA9), retains a DNA-binding homeodomain, whereas others harbor other types of DNA- or chromatin-binding domains. NUP98 FOs have long been known to form puncta, but long-standing questions are how nuclear puncta form and how they drive leukemogenesis. Here we studied NHA9 condensates and show that homotypic interactions and different types of heterotypic interactions are required to form nuclear puncta, which are associated with aberrant transcriptional activity and transformation of hematopoietic stem and progenitor cells. We also show that three additional leukemia-associated NUP98 FOs (NUP98-PRRX1, NUP98-KDM5A, and NUP98-LNP1) form nuclear puncta and transform hematopoietic cells. These findings indicate that LLPS is critical for leukemogenesis by NUP98 FOs. SIGNIFICANCE: We show that homotypic and heterotypic mechanisms of LLPS control NUP98-HOXA9 puncta formation, modulating transcriptional activity and transforming hematopoietic cells. Importantly, these mechanisms are generalizable to other NUP98 FOs that share similar domain structures. These findings address long-standing questions on how nuclear puncta form and their link to leukemogenesis. This article is highlighted in the In This Issue feature, p. 873.

DeconvTest: Simulation framework for quantifying errors and selecting optimal parameters of image deconvolution.

Deconvolution is an essential step of image processing that aims to compensate for the image blur caused by the microscope's point spread function. With many existing deconvolution methods, it is challenging to choose the method and its parameters most appropriate for particular image data at hand. To facilitate this task, we developed DeconvTest: an open-source Python-based framework for generating synthetic microscopy images, deconvolving them with different algorithms, and quantifying reconstruction errors. In contrast to existing software, DeconvTest combines all components required to analyze deconvolution performance in a systematic, high-throughput and quantitative manner. We demonstrate the power of the framework by using it to identify the optimal deconvolution settings for synthetic and real image data. Based on this, we provide a guideline for (a) choosing optimal values of deconvolution parameters for image data at hand and (b) optimizing imaging conditions for best results in combination with subsequent image deconvolution.

Dynamic spherical harmonics approach for shape classification of migrating cells.

Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should - in the future - help to pinpoint factors that play an essential role in cell migration.

Human Neutrophils Produce Antifungal Extracellular Vesicles against Aspergillus fumigatus.

Polymorphonuclear granulocytes (PMNs) are indispensable for controlling life-threatening fungal infections. In addition to various effector mechanisms, PMNs also produce extracellular vesicles (EVs). Their contribution to antifungal defense has remained unexplored. We reveal that the clinically important human-pathogenic fungus Aspergillus fumigatus triggers PMNs to release a distinct set of antifungal EVs (afEVs). Proteome analyses indicated that afEVs are enriched in antimicrobial proteins. The cargo and the release kinetics of EVs are modulated by the fungal strain confronted. Tracking of afEVs indicated that they associated with fungal cells and even entered fungal hyphae, resulting in alterations in the morphology of the fungal cell wall and dose-dependent antifungal effects. To assess as a proof of concept whether the antimicrobial proteins found in afEVs might contribute to growth inhibition of hyphae when present in the fungal cytoplasm, two human proteins enriched in afEVs, cathepsin G and azurocidin, were heterologously expressed in fungal hyphae. This led to reduced fungal growth relative to that of a control strain producing the human retinol binding protein 7. In conclusion, extracellular vesicles produced by neutrophils in response to A. fumigatus infection are able to associate with the fungus, limit growth, and elicit cell damage by delivering antifungal cargo. This finding offers an intriguing, previously overlooked mechanism of antifungal defense against A. fumigatusIMPORTANCE Invasive fungal infections caused by the mold Aspergillus fumigatus are a growing concern in the clinic due to the increasing use of immunosuppressive therapies and increasing antifungal drug resistance. These infections result in high rates of mortality, as treatment and diagnostic options remain limited. In healthy individuals, neutrophilic granulocytes are critical for elimination of A. fumigatus from the host; however, the exact extracellular mechanism of neutrophil-mediated antifungal activity remains unresolved. Here, we present a mode of antifungal defense employed by human neutrophils against A. fumigatus not previously described. We found that extracellular vesicles produced by neutrophils in response to A. fumigatus infection are able to associate with the fungus, limit growth, and elicit cell damage by delivering antifungal cargo. In the end, antifungal extracellular vesicle biology provides a significant step forward in our understanding of A. fumigatus host pathogenesis and opens up novel diagnostic and therapeutic possibilities.

Automated tracking of label-free cells with enhanced recognition of whole tracks.

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

A three-dimensional immunocompetent intestine-on-chip model as in vitro platform for functional and microbial interaction studies.

Alterations of the microbial composition in the gut and the concomitant dysregulation of the mucosal immune response are associated with the pathogenesis of opportunistic infections, chronic inflammation, and inflammatory bowel disease. To create a platform for the investigation of the underlying mechanisms, we established a three-dimensional microphysiological model of the human intestine. This model resembles organotypic microanatomical structures and includes tissue resident innate immune cells exhibiting features of mucosal macrophages and dendritic cells. The model displays the physiological immune tolerance of the intestinal lumen to microbial-associated molecular patterns and can, therefore, be colonised with living microorganisms. Functional studies on microbial interaction between probiotic Lactobacillus rhamnosus and the opportunistic pathogen Candida albicans show that pre-colonization of the intestinal lumen of the model by L. rhamnosus reduces C. albicans-induced tissue damage, lowers its translocation, and limits fungal burden. We demonstrate that microbial interactions can be efficiently investigated using the in vitro model creating a more physiological and immunocompetent microenvironment. The intestinal model allows a detailed characterisation of the immune response, microbial pathogenicity mechanisms, and quantification of cellular dysfunction attributed to alterations in the microbial composition.

Molecular signatures of liver dysfunction are distinct in fungal and bacterial infections in mice.

Rationale: The liver is a central organ not only for metabolism but also immune function. Life-threatening infections of both bacterial and fungal origin can affect liver function but it is yet unknown whether molecular changes differ depending on the pathogen. We aimed to determine whether the hepatic host response to bacterial and fungal infections differs in terms of hepatic metabolism and liver function. Methods: We compared murine models of infection, including bacterial peritoneal contamination and infection (PCI), intraperitoneal and systemic C. albicans infection, at 6 and 24 h post-infection, to sham controls. The molecular hepatic host response was investigated by the detection of regulatory modules based on large-scale protein-protein interaction networks and expression data. Topological analysis of these regulatory modules was used to reveal infection-specific biological processes and molecular mechanisms. Intravital microscopy and immunofluorescence microscopy were used to further analyze specific aspects of pathophysiology such as cholestasis. Results: Down-regulation of lipid catabolism and bile acid synthesis was observed after 6 h in all infection groups. Alterations in lipid catabolism were characterized by accumulation of long chain acylcarnitines and defective beta-oxidation, which affected metabolism by 6 h. While PCI led to an accumulation of unconjugated bile acids (BA), C. albicans infection caused accumulation of conjugated BA independent of the route of infection. Hepatic dye clearance and transporter expression revealed reduced hepatic uptake in fungal infections vs. defects in secretion following polybacterial infection. Conclusion: Molecular phenotypes of lipid accumulation and cholestasis allow differentiation between pathogens as well as routes of infection at early stages in mice. Targeted metabolomics could be a useful tool for the profiling of infected/septic patients and the type of pathogen, with subsequent customization and targeting of therapy.

Modeling Hemolytic-Uremic Syndrome: In-Depth Characterization of Distinct Murine Models Reflecting Different Features of Human Disease.

Diarrhea-positive hemolytic-uremic syndrome (HUS) is a renal disorder that results from infections with Shiga-toxin (Stx)-producing Escherichia coli. The aim of this study was to establish well-defined refined murine models of HUS that can serve as preclinical tools to elucidate molecular mechanisms of disease development. C57BL/6J mice were subjected to different doses of Stx2 purified from an E. coli O157:H7 patient isolate. Animals received 300 ng/kg Stx2 and were sacrificed on day 3 to establish an acute model with fast disease progression. Alternatively, mice received 25 ng/kg Stx2 on days 0, 3, and 6, and were sacrificed on day 7 to establish a subacute model with moderate disease progression. Indicated by a rise in hematocrit, we observed dehydration despite volume substitution in both models, which was less pronounced in mice that underwent the 7-day regime. Compared with sham-treated animals, mice subjected to Stx2 developed profound weight loss, kidney dysfunction (elevation of plasma urea, creatinine, and neutrophil gelatinase-associated lipocalin), kidney injury (tubular injury and loss of endothelial cells), thrombotic microangiopathy (arteriolar microthrombi), and hemolysis (elevation of plasma bilirubin, lactate dehydrogenase, and free hemoglobin). The degree of complement activation (C3c deposition), immune cell invasion (macrophages and T lymphocytes), apoptosis, and proliferation were significantly increased in kidneys of mice subjected to the 7-day but not in kidneys of mice subjected to the 3-day regime. However, glomerular and kidney volume remained mainly unchanged, as assessed by 3D analysis of whole mount kidneys using CD31 staining with light sheet fluorescence microscopy. Gene expression analysis of kidneys revealed a total of only 91 overlapping genes altered in both Stx2 models. In conclusion, we have developed two refined mouse models with different disease progression, both leading to hemolysis, thrombotic microangiopathy, and acute kidney dysfunction and damage as key clinical features of human HUS. While intrarenal changes (apoptosis, proliferation, complement deposition, and immune cell invasion) mainly contribute to the pathophysiology of the subacute model, prerenal pathomechanisms (hypovolemia) play a predominant role in the acute model. Both models allow the further study of the pathomechanisms of most aspects of human HUS and the testing of distinct novel treatment strategies.