Peter Bankhead

Fast retinal vessel detection and measurement using wavelets and edge location refinement.

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.

Dynamic Oscillation of Translation and Stress Granule Formation Mark the Cellular Response to Virus Infection

Virus infection-induced global protein synthesis suppression is linked to assembly of stress granules (SGs), cytosolic aggregates of stalled translation preinitiation complexes. To study long-term stress responses, we developed an imaging approach for extended observation and analysis of SG dynamics during persistent hepatitis C virus (HCV) infection. In combination with type 1 interferon, HCV infection induces highly dynamic assembly/disassembly of cytoplasmic SGs, concomitant with phases of active and stalled translation, delayed cell division, and prolonged cell survival. Double-stranded RNA (dsRNA), independent of viral replication, is sufficient to trigger these oscillations. Translation initiation factor eIF2α phosphorylation by protein kinase R mediates SG formation and translation arrest. This is antagonized by the upregulation of GADD34, the regulatory subunit of protein phosphatase 1 dephosphorylating eIF2α. Stress response oscillation is a general mechanism to prevent long-lasting translation repression and a conserved host cell reaction to multiple RNA viruses, which HCV may exploit to establish persistence.

Digital pathology and image analysis in tissue biomarker research.

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

QuPath: Open source software for digital pathology image analysis

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

Dengue Virus Inhibition of Autophagic Flux and Dependency of Viral Replication on Proteasomal Degradation of the Autophagy Receptor p62

ABSTRACT Autophagic flux involves formation of autophagosomes and their degradation by lysosomes. Autophagy can either promote or restrict viral replication. In the case of Dengue virus (DENV), several studies report that autophagy supports the viral replication cycle, and describe an increase of autophagic vesicles (AVs) following infection. However, it is unknown how autophagic flux is altered to result in increased AVs. To address this question and gain insight into the role of autophagy during DENV infection, we established an unbiased, image-based flow cytometry approach to quantify autophagic flux under normal growth conditions and in response to activation by nutrient deprivation or the mTOR inhibitor Torin1. We found that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Early after infection, basal and activated autophagic flux was enhanced. However, during established replication, basal and Torin1-activated autophagic flux was blocked, while autophagic flux activated by nutrient deprivation was reduced, indicating a block to AV formation and reduced AV degradation capacity. During late infection AV levels increased as a result of inefficient fusion of autophagosomes with lysosomes. In addition, endolysosomal trafficking was suppressed, while lysosomal activities were increased. We further determined that DENV infection progressively reduced levels of the autophagy receptor SQSTM1/p62 via proteasomal degradation. Importantly, stable overexpression of p62 significantly suppressed DENV replication, suggesting a novel role for p62 as a viral restriction factor. Overall, our findings indicate that in the course of DENV infection, autophagy shifts from a supporting to an antiviral role, which is countered by DENV. IMPORTANCE Autophagic flux is a dynamic process starting with the formation of autophagosomes and ending with their degradation after fusion with lysosomes. Autophagy impacts the replication cycle of many viruses. However, thus far the dynamics of autophagy in case of Dengue virus (DENV) infections has not been systematically quantified. Therefore, we used high-content, imaging-based flow cytometry to quantify autophagic flux and endolysosomal trafficking in response to DENV infection. We report that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Further, lysosomal activity was increased, but endolysosomal trafficking was suppressed confirming the block of autophagic flux. Importantly, we provide evidence that p62, an autophagy receptor, restrict DENV replication and was specifically depleted in DENV-infected cells via increased proteasomal degradation. These results suggest that during DENV infection autophagy shifts from a proviral to an antiviral cellular process, which is counteracted by the virus.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

BackgroundModern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.ResultscudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.ConclusionEmerging ‘omics’ technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Endothelin 1 Stimulates Ca2+-Sparks and Oscillations in Retinal Arteriolar Myocytes via IP3R and RyR-Dependent Ca2+ Release

PURPOSE To investigate endothelin 1 (Et1)-dependent Ca(2+)-signaling at the cellular and subcellular levels in retinal arteriolar myocytes. METHODS Et1 responses were imaged from Fluo-4-loaded smooth muscle in isolated segments of rat retinal arteriole using confocal laser microscopy. RESULTS Basal [Ca(2+)](i), subcellular Ca(2+)-sparks, and cellular Ca(2+)-oscillations were all increased during exposure to Et1 (10 nM). Ca(2+)-spark frequency was also increased by 90% by 10 nM Et1. The increase in oscillation frequency was concentration dependent and was inhibited by the EtA receptor (Et(A)R) blocker BQ123 but not by the EtB receptor antagonist BQ788. Stimulation of Ca(2+)-oscillations by Et1 was inhibited by a phospholipase C blocker (U73122; 10 μM), two inhibitors of inositol 1,4,5-trisphosphate receptors (IP(3)Rs), xestospongin C (10 μM), 2-aminoethoxydiphenyl borate (100 μM), and tetracaine (100 μM), a blocker of ryanodine receptors (RyRs). CONCLUSIONS Et1 stimulates Ca(2+)-sparks and oscillations through Et(A)Rs. The underlying mechanism involves the activation of phospholipase C and both IP(3)Rs and RyRs, suggesting crosstalk between these Ca(2+)-release channels. These findings suggest that phasic Ca(2+)-oscillations play an important role in the smooth muscle response to Et1 within the retinal microvasculature and support an excitatory, proconstrictor role for Ca(2+)-sparks in these vessels.

Automated tumor analysis for molecular profiling in lung cancer

The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics.

Evaluation of PTGS2 Expression, PIK3CA Mutation, Aspirin Use and Colon Cancer Survival in a Population-Based Cohort Study

Objectives:The association between aspirin use and improved survival after colorectal cancer diagnosis may be more pronounced in tumors that have PIK3CA mutations or high PTGS2 expression. However, the evidence of a difference in association by biomarker status lacks consistency. In this population-based colon cancer cohort study the interaction between these biomarkers, aspirin use, and survival was assessed.Methods:The cohort consisted of 740 stage II and III colon cancer patients diagnosed between 2004 and 2008. Aspirin use was determined through clinical note review. Tissue blocks were retrieved to determine immunohistochemical assessment of PTGS2 expression and the presence of PIK3CA mutations. Cox proportional hazards models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for colorectal cancer-specific and overall survival.Results:In this cohort aspirin use was associated with a 31% improvement in cancer-specific survival compared to non-use (adjusted HR=0.69, 95% CI 0.47–0.98). This effect was more pronounced in tumors with high PTGS2 expression (PTGS2-high adjusted HR=0.55, 95% CI 0.32–0.96) compared to those with low PTGS2 expression (PTGS2-low adjusted HR=1.19, 95% CI 0.68–2.07, P for interaction=0.09). The aspirin by PTGS2 interaction was significant for overall survival (PTGS2-high adjusted HR=0.64, 95% CI 0.42–0.98 vs. PTGS2-low adjusted HR=1.28, 95% CI 0.80–2.03, P for interaction=0.04). However, no interaction was observed between aspirin use and PIK3CA mutation status for colorectal cancer-specific or overall survival.Conclusions:Aspirin use was associated with improved survival outcomes in this population-based cohort of colon cancer patients. This association differed according to PTGS2 expression but not PIK3CA mutation status. Limiting adjuvant aspirin trials to PIK3CA-mutant colorectal cancer may be too restrictive.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high‐throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.