Testing for differential abundance in mass cytometry data.

When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.This work was supported by Cancer Research UK (core funding to J.C.M., award no. A17197), the University of Cambridge and Hutchison Whampoa Limited. J.C.M. was also supported by core funding from EMBL

Intravenously Administered Alphavirus Vector VA7 Eradicates Orthotopic Human Glioma Xenografts in Nude Mice

VA7 is a neurotropic alphavirus vector based on an attenuated strain of Semliki Forest virus. We have previously shown that VA7 exhibits oncolytic activity against human melanoma xenografts in immunodeficient mice. The purpose of this study was to determine if intravenously administered VA7 would be effective against human glioma.In vitro, U87, U251, and A172 human glioma cells were infected and killed by VA7-EGFP. In vivo, antiglioma activity of VA7 was tested in Balb/c nude mice using U87 cells stably expressing firefly luciferase in subcutaneous and orthotopic tumor models. Intravenously administered VA7-EGFP completely eradicated 100% of small and 50% of large subcutaneous U87Fluc tumors. A single intravenous injection of either VA7-EGFP or VA7 expressing Renilla luciferase (VA7-Rluc) into mice bearing orthotopic U87Fluc tumors caused a complete quenching of intracranial firefly bioluminescence and long-term survival in total 16 of 17 animals. In tumor-bearing mice injected with VA7-Rluc, transient intracranial and peripheral Renilla bioluminescence was observed. Virus was well tolerated and no damage to heart, liver, spleen, or brain was observed upon pathological assessment at three and ninety days post injection, despite detectable virus titers in these organs during the earlier time point.VA7 vector is apathogenic and can enter and destroy brain tumors in nude mice when administered systemically. This study warrants further elucidation of the mechanism of tumor destruction and attenuation of the VA7 virus

Normalizing single-cell RNA sequencing data: challenges and opportunities

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users

Are Happiness and Life Satisfaction Different Across Religious groups? Exploring Determinants of Happiness and Life Satisfaction

This study explores whether different religions experience different levels of happiness and life satisfaction and in case this is affected by country economic and cultural environment. Using World Value Survey (from 1981 to 2014), this study found that individual religiosity and country level of development play a significant role in shaping people’s subjective well-being (SWB). Protestants, Buddhists and Roman Catholic were happier and most satisfied with their lives compared to other religious groups. Orthodox has the lowest SWB. Health status, household’s financial satisfaction and freedom of choice are means by which religious groups and governments across the globe can improve the SWB of their citizens. Keywords: happiness; life satisfaction; religion; religious differences; cultur

The Role of Perfusion Computed Tomography in the Prediction of Cerebral Hyperperfusion Syndrome

Hyperperfusion syndrome (HPS) following carotid angioplasty with stenting (CAS) is associated with significant morbidity and mortality. At present, there are no reliable parameters to predict HPS. The aim of this study was to clarify whether perfusion computed tomography (CT) is a feasible and reliable tool in predicting HPS after CAS.We performed a retrospective case-control study of 54 patients (11 HPS patients and 43 non-HPS) with unilateral severe stenosis of the carotid artery who underwent CAS. We compared the prevalence of vascular risk factors and perfusion CT parameters including regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), and time to peak (TTP) within seven days prior to CAS. Demographic information, risk factors for atherosclerosis, and perfusion CT parameters were evaluated by multivariable logistic regression analysis. The rCBV index was calculated as [(ipsilateral rCBV - contralateral rCBV)/contralateral rCBV], and indices of rCBF and TTP were similarly calculated. We found that eleven patients had HPS, including five with intracranial hemorrhages (ICHs) of whom three died. After a comparison with non-HPS control subjects, independent predictors of HPS included the severity of ipsilateral carotid artery stenosis, 3-hour mean systolic blood pressure (3 h SBP) after CAS, pre-stenting rCBV index >0.15 and TTP index >0.22.The combination of severe ipsilateral carotid stenosis, 3 h SBP after CAS, rCBV index and TTP index provides a potential screening tool for predicting HPS in patients with unilateral carotid stenosis receiving CAS. In addition, adequate management of post-stenting blood pressure is the most important treatable factor in preventing HPS in these high risk patients

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance

Developmental Transcriptomic Features of the Carcinogenic Liver Fluke, Clonorchis sinensis

Clonorchis sinensis is the causative agent of the life-threatening disease endemic to China, Korea, and Vietnam. It is estimated that about 15 million people are infected with this fluke. C. sinensis provokes inflammation, epithelial hyperplasia, and periductal fibrosis in bile ducts, and may cause cholangiocarcinoma in chronically infected individuals. Accumulation of a large amount of biological information about the adult stage of this liver fluke in recent years has advanced our understanding of the pathological interplay between this parasite and its hosts. However, no developmental gene expression profiles of C. sinensis have been published. In this study, we generated gene expression profiles of three developmental stages of C. sinensis by analyzing expressed sequence tags (ESTs). Complementary DNA libraries were constructed from the adult, metacercaria, and egg developmental stages of C. sinensis. A total of 52,745 ESTs were generated and assembled into 12,830 C. sinensis assembled EST sequences, and then these assemblies were further categorized into groups according to biological functions and developmental stages. Most of the genes that were differentially expressed in the different stages were consistent with the biological and physical features of the particular developmental stage; high energy metabolism, motility and reproduction genes were differentially expressed in adults, minimal metabolism and final host adaptation genes were differentially expressed in metacercariae, and embryonic genes were differentially expressed in eggs. The higher expression of glucose transporters, proteases, and antioxidant enzymes in the adults accounts for active uptake of nutrients and defense against host immune attacks. The types of ion channels present in C. sinensis are consistent with its parasitic nature and phylogenetic placement in the tree of life. We anticipate that the transcriptomic information on essential regulators of development, bile chemotaxis, and physico-metabolic pathways in C. sinensis that presented in this study will guide further studies to identify novel drug targets and diagnostic antigens

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Understanding Streptococcus suis serotype 2 infection in pigs through a transcriptional approach

<p>Abstract</p> <p>Background</p> <p><it>Streptococcus suis </it>serotype 2 (<it>S. suis </it>2) is an important pathogen of pigs. <it>S suis 2 </it>infections have high mortality rates and are characterized by meningitis, septicemia and pneumonia. <it>S. suis </it>2 is also an emerging zoonotic agent and can infect humans that are exposed to pigs or their by-products. To increase our knowledge of the pathogenesis of meningitis, septicemia and pneumonia in pigs caused by <it>S. suis </it>2, we profiled the response of peripheral blood mononuclear cells <b>(</b>PBMC), brain and lung tissues to infection with <it>S. suis </it>2 strain SC19 using the Affymetrix Porcine Genome Array.</p> <p>Results</p> <p>A total of 3,002 differentially expressed transcripts were identified in the three tissues, including 417 unique genes in brain, 210 in lung and 213 in PBMC. These genes showed differential expression (DE) patterns on analysis by visualization and integrated discovery (DAVID). The DE genes involved in the immune response included genes related to the inflammatory response (CD163), the innate immune response (TLR2, TLR4, MYD88, TIRAP), cell adhesion (CD34, SELE, SELL, SELP, ICAM-1, ICAM-2, VCAM-1), antigen processing and presentation (MHC protein complex) and angiogenesis (VEGF), together with genes encoding cytokines (interleukins). Five selected genes were validated by qRT-PCR analysis.</p> <p>Conclusions</p> <p>We studied the response to infection with <it>S. suis </it>2 strain SC19 by microarray analysis. Our findings confirmed some genes identified in previous studies and discovered numerous additional genes that potentially function in <it>S. suis </it>2 infections in vivo. This new information will form the foundation of future investigations into the pathogenesis of <it>S. suis</it>.</p