NF-κB drives epithelial-mesenchymal mechanisms of lung fibrosis in a translational lung cell model.

In the progression phase of idiopathic pulmonary fibrosis (IPF), the normal alveolar structure of the lung is lost and replaced by remodeled fibrotic tissue and by bronchiolized cystic airspaces. Although these are characteristic features of IPF, knowledge of specific interactions between these pathological processes is limited. Here, the interaction of lung epithelial and lung mesenchymal cells was investigated in a coculture model of human primary airway epithelial cells (EC) and lung fibroblasts (FB). Single-cell RNA sequencing revealed that the starting EC population was heterogenous and enriched for cells with a basal cell signature. Furthermore, fractions of the initial EC and FB populations adopted distinct pro-fibrotic cell differentiation states upon cocultivation, resembling specific cell populations that were previously identified in lungs of patients with IPF. Transcriptomic analysis revealed active NF-κB signaling early in the cocultured EC and FB, and the identified NF-κB expression signatures were found in "HAS1 High FB" and "PLIN2+ FB" populations from IPF patient lungs. Pharmacological blockade of NF-κB signaling attenuated specific phenotypic changes of EC and prevented FB-mediated interleukin-6, interleukin-8, and CXC chemokine ligand 6 cytokine secretion, as well as collagen α-1(I) chain and α-smooth muscle actin accumulation. Thus, we identified NF-κB as a potential mediator, linking epithelial pathobiology with fibrogenesis.

Orbit Image Analysis: An open-source whole slide image analysis tool.

We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machine-learning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney.

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots.

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following H&E staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (≥60% RBCs), Mixed and Fibrin dominant (≥60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (ρ = 0.944**, p < 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods.

Novel high-throughput myofibroblast assays identify agonists with therapeutic potential in pulmonary fibrosis that act via EP2 and EP4 receptors.

Pathological features of pulmonary fibrosis include accumulation of myofibroblasts and increased extracellular matrix (ECM) deposition in lung tissue. Contractile α-smooth muscle actin (α-SMA)-expressing myofibroblasts that produce and secrete ECM are key effector cells of the disease and therefore represent a viable target for potential novel anti-fibrotic treatments. We used primary normal human lung fibroblasts (NHLF) in two novel high-throughput screening assays to discover molecules that inhibit or revert fibroblast-to-myofibroblast differentiation. A phenotypic high-content assay (HCA) quantified the degree of myofibroblast differentiation, whereas an impedance-based assay, multiplexed with MS / MS quantification of α-SMA and collagen 1 alpha 1 (COL1) protein, provided a measure of contractility and ECM formation. The synthetic prostaglandin E1 (PGE1) alprostadil, which very effectively and potently attenuated and even reversed TGF-ß1-induced myofibroblast differentiation, was identified by screening a library of approved drugs. In TGF-ß1-induced myofibroblasts the effect of alprostadil was attributed to activation of prostanoid receptor 2 and 4 (EP2 and EP4, respectively). However, selective activation of the EP2 or the EP4 receptor was already sufficient to prevent or reverse TGF-ß1-induced NHLF myofibroblast transition. Our high-throughput assays identified chemical structures with potent anti-fibrotic properties acting through potentially novel mechanisms.

A fully automated image analysis method to quantify lung fibrosis in the bleomycin-induced rat model.

Intratracheal administration of bleomycin induces fibrosis in the lung, which is mainly assessed by histopathological grading that is subjective. Current literature highlights the need of reproducible and quantitative pulmonary fibrosis analysis. If some quantitative studies looked at fibrosis parameters separately, none of them quantitatively assessed both aspects: lung tissue remodeling and collagenization. To ensure reliable quantification, support vector machine learning was used on digitalized images to design a fully automated method that analyzes two important aspects of lung fibrosis: (i) areas having substantial tissue remodeling with appearance of dense fibrotic masses and (ii) collagen deposition. Fibrotic masses were identified on low magnification images and collagen detection was performed at high magnification. To insure a fully automated application the tissue classifier was trained on several independent studies that were performed over a period of four years. The detection method generates two different values that can be used to quantify lung fibrosis development: (i) percent area of fibrotic masses and (ii) percent of alveolar collagen. These two parameters were validated using independent studies from bleomycin- and saline-treated animals. A significant change of these lung fibrosis quantification parameters- increased amount of fibrotic masses and increased collagen deposition- were observed upon intratracheal administration of bleomycin and subsequent significant beneficial treatments effects were observed with BIBF-1120 and pirfenidone.

A high-content EMT screen identifies multiple receptor tyrosine kinase inhibitors with activity on TGFß receptor.

An epithelial to mesenchymal transition (EMT) enables epithelial tumor cells to break out of the primary tumor mass and to metastasize. Understanding the molecular mechanisms driving EMT in more detail will provide important tools to interfere with the metastatic process. To identify pharmacological modulators and druggable targets of EMT, we have established a novel multi-parameter, high-content, microscopy-based assay and screened chemical compounds with activities against known targets. Out of 3423 compounds, we have identified 19 drugs that block transforming growth factor beta (TGFß)-induced EMT in normal murine mammary gland epithelial cells (NMuMG). The active compounds include inhibitors against TGFß receptors (TGFBR), Rho-associated protein kinases (ROCK), myosin II, SRC kinase and uridine analogues. Among the EMT-repressing compounds, we identified a group of inhibitors targeting multiple receptor tyrosine kinases, and biochemical profiling of these multi-kinase inhibitors reveals TGFBR as a thus far unknown target of their inhibitory spectrum. These findings demonstrate the feasibility of a multi-parameter, high-content microscopy screen to identify modulators and druggable targets of EMT. Moreover, the newly discovered "off-target" effects of several receptor tyrosine kinase inhibitors have important consequences for in vitro and in vivo studies and might beneficially contribute to the therapeutic effects observed in vivo.

A semi-automated method to assess intraepidermal nerve fibre density in human skin biopsies.

AIMS: Evaluation of intraepidermal nerve fibres (IENFs) in skin biopsies is used in the diagnosis of small-fibre neuropathies. The number of IENFs is assessed manually under a microscope, with an inter-rater variability of ~25%. Unless the images are digitized, there is no documentation. Our aim was to develop a method for standardized semi-automated quantification (SAQ) and documentation of IENF density. METHODS AND RESULTS: We analysed samples from four different university centres that were immunostained according to local protocols. Images were acquired through the Z-plane with a whole slide scanner. orbit image analysis software was used to create an analysable image and develop a reliable algorithm for IENF detection. Rebuilt images revealed well-contrasted nerves, allowing detection of IENFs (automated). The software presented the detected nerves for confirmation by the operator (manual). As compared with the conventional microscopy count, the SAQ achieved correlation coefficients of 0.99 and 0.96 and interfacility variabilities of 19% and 23%, respectively. We found better reproducibility with fluorescence-stained specimens than with bright-field images. CONCLUSIONS: The new semi-automated method has high experimenter-independent reproducibility when based on nerve detection by fluorescence and is easy to perform, even by untrained users. The IENF counting is electronically well documented.

Identification of cathepsin L as a potential sex-specific biomarker for renal damage.

The renin-angiotensin system is a well-known regulator of blood pressure and plays an important role in the pathogenesis of cardiovascular disease and renal damage. Genetic factors, including single nucleotide polymorphisms and sex, are increasingly recognized as potential risk factors for the development of cardiovascular disease. Double transgenic rats (dTGRs), harboring human renin and angiotensinogen genes, were used in this study to investigate potential sex differences influencing renal function and renal gene expression. dTGR males and females had comparable increases in blood pressure, whereas body weight, albuminuria/proteinuria, and urine flow rate were higher in males. At 8 weeks of age, renal plasma flow and glomerular filtration rate were proportionally lower in males, and renal vascular resistance tended to be higher. Males developed more severe tubulointerstitial and vascular lesions. By the end of week 8, 40%of the males but none of the females had died. Genome expression studies were performed with RNA from kidneys of 7-week-old male and female dTGRs and control rats to further investigate the sex-related differences on a molecular level. Forty-five genes showed sex-dependent expression patterns in dTGRs that were significantly different compared to controls. Cathepsin L, one of the genes differentially expressed between the sexes, was also shown to be strongly associated with the degree of renal injury. In dTGRs, urinary cathepsin L at week 7 was higher in males (nanograms per 24 hours: male, 512±163; female, 132±70). These results reveal a potential new biomarker for the personalized diagnosis and management of chronic kidney disease.

Integration of distributed computing into the drug discovery process.

Grid computing offers an opportunity to gain massive computing power at low costs. We give a short introduction into the drug discovery process and exemplify the use of grid computing for image processing, docking and 3D pharmacophore descriptor calculations. The principle of a grid and its architecture are briefly explained. More emphasis is laid on the issues related to a company-wide grid installation and embedding the grid into the research process. The future of grid computing in drug discovery is discussed in the expert opinion section. Most needed, besides reliable algorithms to predict compound properties, is embedding the grid seamlessly into the discovery process. User friendly access to powerful algorithms without any restrictions, that is, by a limited number of licenses, has to be the goal of grid computing in drug discovery.