MOTIVATION: Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. RESULTS: A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. AVAILABILITY AND IMPLEMENTATION: The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reactive oxygen species (ROS) are produced as metabolic by-products throughout the cell, including within the mitochondria by, among others, the electron transport chain (ETC). ROS are important signalling molecules, but their accumulation can lead to oxidative stress. As experimental protocols to measure cellular ROS levels can suffer from limitations, computational models can complement experimental measurements to elucidate mechanisms underlying ROS metabolism. Phenomenological models utilise reduced equation sets to relate aspects of ROS metabolism to measured bioenergetic parameters such as the mitochondrial membrane potential or NADH concentration, and often generate experimentally testable hypotheses. Integrating multiple bioenergetic parameters, we here describe a phenomenological equation-based model of ETC-mediated ROS metabolism implemented by expanding an existing thermodynamic model of the ETC. We demonstrate that this model can reproduce experimentally observed behaviour, and explore how the model can be applied to gain insight into ROS metabolism in the presence of pathology. Model predictions suggest that failure of scavenging may be more harmful to cells than respiratory complex impairment.

MOTIVATION: tRNAs were originally considered uni-functional RNA molecules involved in the delivery of amino acids to growing peptide chains on the ribosome. More recently, the liberation of tRNA fragments from tRNAs via specific enzyme cleavage has been characterized. Detection of tRNA fragments in sequencing data is difficult due to tRNA sequence redundancy and the short length of both tRNAs and their fragments. RESULTS: Here, we introduce tsRNAsearch, a Nextflow pipeline for the identification of differentially abundant tRNA fragments and other non-coding RNAs from small RNA-sequencing data. tsRNAsearch is intended for use when comparing two groups of datasets, such as control and treatment groups. tsRNAsearch comparatively searches for tRNAs and ncRNAs with irregular read distribution profiles (a proxy for RNA cleavage) using a combined score made up of four novel methods and a differential expression analysis, and reports the top ranked results in simple PDF and TEXT files. In this study, we used publicly available small RNA-seq data to replicate the identification of tsRNAs from chronic hepatitis-infected liver tissue data. In addition, we applied tsRNAsearch to pancreatic ductal adenocarcinoma (PDAC) and matched healthy pancreatic tissue small RNA-sequencing data. Our results support the identification of miR135b from the original study as a potential biomarker of PDAC and identify other potentially stronger miRNA biomarkers of PDAC. AVAILABILITY AND IMPLEMENTATION: https://github.com/GiantSpaceRobot/tsRNAsearch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ligand binding to death receptors activates apoptosis in cancer cells. Stimulation of death receptors results in the formation of intracellular multiprotein platforms that either activate the apoptotic initiator Caspase-8 to trigger cell death, or signal through kinases to initiate inflammatory and cell survival signalling. Two of these platforms, the Death-Inducing Signalling Complex (DISC) and the RIPoptosome, also initiate necroptosis by building filamentous scaffolds that lead to the activation of mixed lineage kinase domain-like pseudokinase. To explain cell decision making downstream of death receptor activation, we developed a semi-stochastic model of DISC/RIPoptosome formation. The model is a hybrid of a direct Gillespie stochastic simulation algorithm for slow assembly of the RIPoptosome and a deterministic model of downstream caspase activation. The model explains how alterations in the level of death receptor-ligand complexes, their clustering properties and intrinsic molecular fluctuations in RIPoptosome assembly drive heterogeneous dynamics of Caspase-8 activation. The model highlights how kinetic proofreading leads to heterogeneous cell responses and results in fractional cell killing at low levels of receptor stimulation. It reveals that the noise in Caspase-8 activation-exclusively caused by the stochastic molecular assembly of the DISC/RIPoptosome platform-has a key function in extrinsic apoptotic stimuli recognition.

Summary: The miRBase database is the central and official repository for miRNAs and the current release is miRBase version 21.0. Name changes in different miRBase releases cause inconsistencies in miRNA names from version to version. When working with only a small number of miRNAs the translation can be done manually. However, with large sets of miRNAs, the necessary correction of such inconsistencies becomes burdensome and error-prone. We developed miRNAmeConverter , available as a Bioconductor R package and web interface that addresses the challenges associated with mature miRNA name inconsistencies. The main algorithm implemented enables high-throughput automatic translation of species-independent mature miRNA names to user selected miRBase versions. The web interface enables users less familiar with R to translate miRNA names given in form of a list or embedded in text and download of the results. Availability and Implementation: The miRNAmeConverter R package is open source under the Artistic-2.0 license. It is freely available from Bioconductor ( http://bioconductor.org/packages/miRNAmeConverter ). The web interface is based on R Shiny and can be accessed under the URL http://www.systemsmedicineireland.ie/tools/mirna-name-converter/ . The database that miRNAmeConverter depends on is provided by the annotation package miRBaseVersions.db and can be downloaded from Bioconductor ( http://bioconductor.org/packages/miRBaseVersions.db ). Minimum R version 3.3.0 is required. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.

The pro-apoptotic proteins Bax and Bak are essential for executing programmed cell death (apoptosis), yet the mechanism of their activation is not properly understood at the structural level. For the first time in cell death research, we calculated intra-protein charge transfer in order to study the structural alterations and their functional consequences during Bax activation. Using an electronegativity equalization model, we investigated the changes in the Bax charge profile upon activation by a functional peptide of its natural activator protein, Bim. We found that charge reorganizations upon activator binding mediate the exposure of the functional sites of Bax, rendering Bax active. The affinity of the Bax C-domain for its binding groove is decreased due to the Arg94-mediated abrogation of the Ser184-Asp98 interaction. We further identified a network of charge reorganizations that confirms previous speculations of allosteric sensing, whereby the activation information is conveyed from the activation site, through the hydrophobic core of Bax, to the well-distanced functional sites of Bax. The network was mediated by a hub of three residues on helix 5 of the hydrophobic core of Bax. Sequence and structural alignment revealed that this hub was conserved in the Bak amino acid sequence, and in the 3D structure of folded Bak. Our results suggest that allostery mediated by charge transfer is responsible for the activation of both Bax and Bak, and that this might be a prototypical mechanism for a fast activation of proteins during signal transduction. Our method can be applied to any protein or protein complex in order to map the progress of allosteric changes through the proteins' structure.

UNLABELLED: We have developed a web service that provides a comprehensive analysis of the susceptibility of cells to undergo apoptosis in response to an activation of the mitochondrial apoptotic pathway. Based on ordinary differential equations, (pre-determined) protein concentrations and release kinetics of mitochondrial pro-apoptotic factors, a network of 52 reactions and 19 reaction partners can be employed as a tool to display temporal protein profiles, to identify key regulatory proteins and to determine critical threshold concentrations required for the execution of apoptosis in HeLa cancer cells or other cell types. The web service also provides an interactive database function for the deposition of cell-type-specific quantitative data. In addition, the web service provides an output that can be compared directly to experimental results obtained from real-time single-cell experiments, making this a widely applicable systems biology tool for apoptosis and cancer researchers. AVAILABILITY: http://systemsbiology.rcsi.ie/apopto-cell.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.