Occurrence of late-apoptotic symptoms in porcine preimplantation embryos upon exposure of oocytes to perfluoroalkyl substances (PFASs) under in vitro meiotic maturation

The objectives of this study were to evaluate the effect of perfluoroalkyl substances on early embryonic development and apoptosis in blastocysts using a porcine in vitro model. Porcine oocytes (N = 855) collected from abattoir ovaries were subjected to perfluorooctane sulfonic acid (PFOS) (0.1 μg/ml) and perfluorohexane sulfonic acid (PFHxS) (40 μg/ml) during in vitro maturation (IVM) for 45 h. The gametes were then fertilized and cultured in vitro, and developmental parameters were recorded. After 6 days of culture, resulting blastocysts (N = 146) were stained using a terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and imaged as stacks using confocal laser scanning microscopy. Proportion of apoptotic cells as well as total numbers of nuclei in each blastocyst were analyzed using objective image analysis. The experiment was run in 9 replicates, always with a control present. Effects on developmental parameters were analyzed using logistic regression, and effects on apoptosis and total numbers of nuclei were analyzed using linear regression. Higher cell count was associated with lower proportion of apoptotic cells, i.e., larger blastocysts contained less apoptotic cells. Upon PFAS exposure during IVM, PFHxS tended to result in higher blastocyst rates on day 5 post fertilization (p = 0.07) and on day 6 post fertilization (p = 0.05) as well as in higher apoptosis rates in blastocysts (p = 0.06). PFHxS resulted in higher total cell counts in blastocysts (p = 0.002). No effects attributable to the concentration of PFOS used here was seen. These findings add to the evidence that some perfluoroalkyl substances may affect female reproduction. More studies are needed to better understand potential implications for continued development as well as for human health

Bovine oocyte exposure to perfluorohexane sulfonate (PFHxS) induces phenotypic, transcriptomic, and DNA methylation changes in resulting embryos in vitro

Knowledge on the effects of perfluorohexane sulfonate (PFHxS) on ovarian function is limited. In the current study, we investigated the sensitivity of oocytes to PFHxS during in vitro maturation (IVM), including conse-quences on embryo development at the morphological, transcriptomic, and epigenomic levels. Bovine cumulus-oocyte complexes (COCs) were exposed to PFHxS during 22 h IVM. Following fertilisation, developmental competence was recorded until day 8 of culture. Two experiments were conducted: 1) exposure of COCs to 0.01 mu g mL(-1) -100 mu g mL(-1) PFHxS followed by confocal imaging to detect neutral lipids and nuclei, and 2) exposure of COCs to 0.1 mu g mL(-1) PFHxS followed by analysis of transcriptomic and DNA methylation changes in blastocysts. Decreased oocyte developmental competence was observed upon exposure to & nbsp;>= 40 mu g mL(-1) PFHxS and altered lipid distribution was observed in the blastocysts upon exposure to 1-10 mu g mL(-1) PFHxS (not observed at lower or higher concentrations). Transcriptomic data showed that genes affected by 0.1 mu g mL(-1) PFHxS were enriched for pathways related to increased synthesis and production of reactive oxygen species. Enrichment for peroxisome proliferator-activated receptor-gamma and oestrogen pathways was also observed. Genes linked to DNA methylation changes were enriched for similar pathways. In conclusion, exposure of the bovine oocyte to PFHxS during the narrow window of IVM affected subsequent embryonic development, as reflected by morphological and mo- lecular changes. This suggests that PFHxS interferes with the final nuclear and cytoplasmic maturation of the oocyte leading to decreased developmental competence to blastocyst stage

Automatic quantification of microvessel density in urinary bladder carcinoma

Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis technique were applied in immunostainings, performed according to an optimized staining protocol. In 12 cases a comparison of biopsy material and the corresponding cystectomy specimen were tested, showing a good correlation in 11 of 12 cases (92%). The techniques proved reproducible for both nMVD and aMVD quantifications related to total tumour area. However, the automatic method was dependent on high immunostaining quality. Simultaneous, semi-automatic quantification of microvessels, stroma and epithelial fraction resulted in a decreased reproducibility. Quantification in ten images, selected in a descending order of MVD by subjective visual judgement, showed a poor observer capacity to estimate and rank MVD. Based on our results we propose quantification of MVD related to one tissue compartment. When staining quality is of high standard, automatic quantification is applicable, which facilitates quantification of multiple areas and thus, should minimize selection variability. © 1999 Cancer Research Campaig

In vivo imaging of pancreatic tumours and liver metastases using 7 Tesla MRI in a murine orthotopic pancreatic cancer model and a liver metastases model

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is the fourth leading cause of tumour death in the western world. However, appropriate tumour models are scarce. Here we present a syngeneic murine pancreatic cancer model using 7 Tesla MRI and evaluate its clinical relevance and applicability.</p> <p>Methods</p> <p>6606PDA murine pancreatic cancer cells were orthotopically injected into the pancreatic head. Liver metastases were induced through splenic injection. Animals were analyzed by MRI three and five weeks following injection. Tumours were detected using T2-weighted high resolution sequences. Tumour volumes were determined by callipers and MRI. Liver metastases were analyzed using gadolinium-EOB-DTPA and T1-weighted 3D-Flash sequences. Tumour blood flow was measured using low molecular gadobutrol and high molecular gadolinium-DTPA.</p> <p>Results</p> <p>MRI handling and applicability was similar to human systems, resolution as low as 0.1 mm. After 5 weeks tumour volumes differed significantly (p < 0.01) when comparing calliper measurments (n = 5, mean 1065 mm³+/-243 mm³) with MRI (mean 918 mm³+/-193 mm³) with MRI being more precise. Histology (n = 5) confirmed MRI tumour measurements (mean size MRI 38.5 mm²+/-22.8 mm²versus 32.6 mm²+/-22.6 mm²(histology), p < 0,0004) with differences due to fixation and processing of specimens. After splenic injection all mice developed liver metastases with a mean of 8 metastases and a mean volume of 173.8 mm³+/-56.7 mm³after 5 weeks. Lymphnodes were also easily identified. Tumour accumulation of gadobutrol was significantly (p < 0.05) higher than gadolinium-DTPA. All imaging experiments could be done repeatedly to comply with the 3R-principle thus reducing the number of experimental animals.</p> <p>Conclusions</p> <p>This model permits monitoring of tumour growth and metastasis formation in longitudinal non-invasive high-resolution MR studies including using contrast agents comparable to human pancreatic cancer. This multidisciplinary environment enables radiologists, surgeons and physicians to further improve translational research and therapies of pancreatic cancer.</p

Learning Cell Nuclei Segmentation Using Labels Generated With Classical Image Analysis Methods

Creating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use in many applications. Here, we present a study in which we used the output of a classical image analysis pipelineas labels when training a convolutional neural network(CNN). This may not only reduce the time experts spend annotating images but it may also lead to an improvement of results when compared to the output from the classical pipeline used in training. Inour application, i.e.,cell nuclei segmentation,we generated the annotations using CellProfiler(a tool for developing classical image analysis pipelines for biomedical applications)and trained on them a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a 0.84 object-wise Jaccard indexwhich was better than the classical method used for generating the annotations by 0.02and 0.34, respectively. Our experimental results show that in this application, not only such training is feasiblebut also thatthe deep learning segmentationsare a clear improvement compared to the output from the classical pipelineused for generating the annotations

Automatic Quantification of Immunohistochemically Stained Cell Nuclei Using Unsupervised Image Analysis

A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects from the background using dynamic thresholding of the P2/A‐histogram, where P2/A is a global roundness measure. Then the image is transformed into principal component hue, defined as the angle around the first principal component. This image is used to segment positive and negative objects. The method is fully automatic and the principal component approach makes it robust with respect to illumination and focus settings. An independent test set consisting of images grabbed with different focus and illumination for each field of view was used to test the method, and the proposed method showed less variation than the intraoperator variation using supervised Maximum Likelihood classification

A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei

A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a new supervised algorithm for creating a pixel classifier is applied to an image that is typical for the sample. The training phase of the classifier is very user friendly since only a few typical pixels for each class need to be selected. The classifier is robust in that it is non‐parametric and has a built‐in metric that adapts to the colour space. After the training the classifier can be applied to all images from the same staining session. Then, all pixels classified as belonging to nuclei of cells are grouped into individual nuclei through a watershed segmentation and connected component labelling algorithm. This algorithm also separates touching nuclei. Finally, the nuclei are classified according to their fraction of positive pixels