The START App: a web-based RNAseq analysis and visualization resource

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Phantasus, a web application for visual and interactive gene expression analysis

Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license.

Protocol for identifying differentially expressed genes using the RumBall RNA-seq analysis platform

Here, we present a protocol for the identification of differentially expressed genes through RNA sequencing analysis. Starting with FASTQ files from public datasets, this protocol leverages RumBall within a self-contained Docker system. We describe the steps for software setup, obtaining data, read mapping, sample normalization, statistical modeling, and gene ontology enrichment. We then detail procedures for interpreting results with plots and tables. RumBall internally utilizes popular tools, ensuring a comprehensive understanding of the analysis process.

Glioblastoma biomarkers in urinary extracellular vesicles reveal the potential for a 'liquid gold' biopsy

BACKGROUND

Biomarkers that reflect glioblastoma tumour activity and treatment response are urgently needed to help guide clinical management, particularly for recurrent disease. As the urinary system is a major clearance route of circulating extracellular vesicles (EVs; 30-1000 nm nanoparticles) we explored whether sampling urinary-EVs could serve as a simple and non-invasive liquid biopsy approach for measuring glioblastoma-associated biomarkers.

METHODS

Fifty urine specimens (15-60 ml) were collected from 24 catheterised glioblastoma patients immediately prior to primary (n = 17) and recurrence (n = 7) surgeries, following gross total resection (n = 9), and from age/gender-matched healthy participants (n = 14). EVs isolated by differential ultracentrifugation were characterised and extracted proteomes were analysed by high-resolution data-independent acquisition liquid chromatography tandem mass spectrometry (DIA-LC-MS/MS).

RESULTS

Overall, 6857 proteins were confidently identified in urinary-EVs (q-value ≤ 0.01), including 94 EV marker proteins. Glioblastoma-specific proteomic signatures were determined, and putative urinary-EV biomarkers corresponding to tumour burden and recurrence were identified (FC ≥ | 2 | , adjust p-val≤0.05, AUC > 0.9).

CONCLUSION

In-depth DIA-LC-MS/MS characterisation of urinary-EVs substantiates urine as a viable source of glioblastoma biomarkers. The promising 'liquid gold' biomarker panels described here warrant further investigation.

An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD.

Lineage specific extracellular vesicle-associated protein biomarkers for the early detection of high grade serous ovarian cancer

High grade serous ovarian carcinoma (HGSOC) accounts for ~ 70% of ovarian cancer cases. Non-invasive, highly specific blood-based tests for pre-symptomatic screening in women are crucial to reducing the mortality associated with this disease. Since most HGSOCs typically arise from the fallopian tubes (FT), our biomarker search focused on proteins found on the surface of extracellular vesicles (EVs) released by both FT and HGSOC tissue explants and representative cell lines. Using mass spectrometry, 985 EV proteins (exo-proteins) were identified that comprised the FT/HGSOC EV core proteome. Transmembrane exo-proteins were prioritized because these could serve as antigens for capture and/or detection. With a nano-engineered microfluidic platform, six newly discovered exo-proteins (ACSL4, IGSF8, ITGA2, ITGA5, ITGB3, MYOF) plus a known HGSOC associated protein, FOLR1 exhibited classification performance ranging from 85 to 98% in a case-control study using plasma samples representative of early (including stage IA/B) and late stage (stage III) HGSOCs. Furthermore, by a linear combination of IGSF8 and ITGA5 based on logistic regression analysis, we achieved a sensitivity of 80% with 99.8% specificity and a positive predictive value of 13.8%. Importantly, these exo-proteins also can accurately discriminate between ovarian and 12 types of cancers commonly diagnosed in women. Our studies demonstrate that these lineage-associated exo-biomarkers can detect ovarian cancer with high specificity and sensitivity early and potentially while localized to the FT when patient outcomes are more favorable.

Deletion of taf1 and taf5 in zebrafish capitulate cardiac and craniofacial abnormalities associated with TAFopathies through perturbations in metabolism

Intellectual disability is a neurodevelopmental disorder that affects 2-3% of the general population. Syndromic forms of intellectual disability frequently have a genetic basis and are often accompanied by additional developmental anomalies. Pathogenic variants in components of TATA-binding protein associated factors (TAFs) have recently been identified in a subset of patients with intellectual disability, craniofacial hypoplasia, and congenital heart disease. This syndrome has been termed as a TAFopathy and includes mutations in TATA binding protein (TBP), TAF1, TAF2, and TAF6. The underlying mechanism by which TAFopathies give rise to neurodevelopmental, craniofacial, and cardiac abnormalities remains to be defined. Through a forward genetic screen in zebrafish, we have recovered a recessive mutant phenotype characterized by craniofacial hypoplasia, ventricular hypoplasia, heart failure at 96 h post-fertilization and lethality, and show it is caused by a nonsense mutation in taf5. CRISPR/CAS9 mediated gene editing revealed that these defects where phenocopied by mutations in taf1 and taf5. Mechanistically, taf5-/- zebrafish displayed misregulation in metabolic gene expression and metabolism as evidenced by RNA sequencing, respiration assays, and metabolite studies. Collectively, these findings suggest that the TAF complex may contribute to neurologic, craniofacial, and cardiac development through regulation of metabolism.

A web-based integrative transcriptome analysis, RNAseqChef, uncovers cell/tissue type-dependent action of sulforaphane

RNA sequencing (RNA-seq) is a powerful technique for understanding cellular state and dynamics. However, comprehensive transcriptomic characterization of multiple RNA-seq datasets is laborious without bioinformatics training and skills. To remove the barriers to sequence data analysis in the research community, we have developed "RNAseqChef" (RNA-seq data controller highlighting expression features), a web-based platform of systematic transcriptome analysis that can automatically detect, integrate, and visualize differentially expressed genes and their biological functions. To validate its versatile performance, we examined the pharmacological action of sulforaphane (SFN), a natural isothiocyanate, on various types of cells and mouse tissues using multiple datasets in vitro and in vivo. Notably, SFN treatment upregulated the ATF6-mediated unfolded protein response in the liver and the NRF2-mediated antioxidant response in the skeletal muscle of diet-induced obese mice. In contrast, the commonly downregulated pathways included collagen synthesis and circadian rhythms in the tissues tested. On the server of RNAseqChef, we simply evaluated and visualized all analyzing data and discovered the NRF2-independent action of SFN. Collectively, RNAseqChef provides an easy-to-use open resource that identifies context-dependent transcriptomic features and standardizes data assessment.

Lineage specific extracellular vesicle-associated protein biomarkers for the early detection of high grade serous ovarian cancer

High grade serous ovarian carcinoma (HGSOC) accounts for ~ 70% of ovarian cancer cases. Non-invasive, highly specific blood-based tests for pre-symptomatic screening in women are crucial to reducing the mortality associated with this disease. Since most HGSOCs typically arise from the fallopian tubes (FT), our biomarker search focused on proteins found on the surface of extracellular vesicles (EVs) released by both FT and HGSOC tissue explants and representative cell lines. Using mass spectrometry, 985 EV proteins (exo-proteins) were identified that comprised the FT/HGSOC EV core proteome. Transmembrane exo-proteins were prioritized because these could serve as antigens for capture and/or detection. With a nano-engineered microfluidic platform, six newly discovered exo-proteins (ACSL4, IGSF8, ITGA2, ITGA5, ITGB3, MYOF) plus a known HGSOC associated protein, FOLR1 exhibited classification performance ranging from 85-98% in a case-control study using plasma samples representative of early (including stage IA/B) and late stage (stage III) HGSOCs. Furthermore, by linear combination of IGSF8 and ITGA5 based on logistic regression analysis, we achieved a sensitivity of 80% (99.8% specificity). These lineage-associated exo-biomarkers have potential to detect cancer while localized to the FT when patient outcomes are more favorable.

FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery

Bioinformatics analysis and visualization of high-throughput gene expression data require extensive computer programming skills, posing a bottleneck for many wet-lab scientists. In this work, we present an intuitive user-friendly platform for gene expression data analysis and visualization called FungiExpresZ. FungiExpresZ aims to help wet-lab scientists with little to no knowledge of computer programming to become self-reliant in bioinformatics analysis and generating publication-ready figures. The platform contains many commonly used data analysis tools and an extensive collection of pre-processed public ribonucleic acid sequencing (RNA-seq) datasets of many fungal species, including important human, plant and insect pathogens. Users may analyse their data alone or in combination with public RNA-seq data for an integrated analysis. The FungiExpresZ platform helps wet-lab scientists to overcome their limitations in genomics data analysis and can be applied to analyse data of any organism. FungiExpresZ is available as an online web-based tool (https://cparsania.shinyapps.io/FungiExpresZ/) and an offline R-Shiny package (https://github.com/cparsania/FungiExpresZ).

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

Investigating chemical and microbial functional indicators of nutrient retention capacity in greenhouse stormwater retention ponds in southwestern Ontario, Canada

The tributaries flowing through Leamington, Ontario are unique in the Canadian Lake Erie watershed due to the broad spatial extent of greenhouse operations, which more than doubled in size and density from 2011 to 2022. These greenhouse operations are considered to be potential nutrient point sources with respect to observed nutrient concentrations in tributaries adjacent to greenhouse stormwater retention ponds (GSWPs). Identifying causal factors of nutrient release, whether this be chemical or biological, within these ponds may be critical for mitigating their impact on the watershed and ultimately the receiving waters of Lake Erie. Specifically, phosphorus and nitrogen accumulation in freshwater ponds can contribute to environmental damage proximal to adjacent streams, serving as a potential catalyst for algal blooms and eutrophication. This study compared correlations between the water column N:P stoichiometry, sediment nutrient retention capacity, and drivers of microbial metabolism within GSWP sediments. Correlations between water column TN:TP ratios and sediment nutrient retention capacity were observed, suggesting an interplay between N and P in terms of nutrient limitation. Further, clear shifts were observed in the bacterial metabolic pathways analyzed through metatranscriptomics. Specifically, genes related to nitrogen fixation, nitrification and denitrification, and other metabolic processes involving sulfur and methane showed differential expression depending on the condition of the respective pond (i.e., naturalized wetland vs. dredged, eutrophic pond). Collectively, this research serves to highlight the interconnected role of chemical-biological processes particularly as they relate to significant ecosystem processes such as nutrient loading and retention dynamics in impaired freshwater systems.

Myosin VI regulates the spatial organisation of mammalian transcription initiation

During transcription, RNA Polymerase II (RNAPII) is spatially organised within the nucleus into clusters that correlate with transcription activity. While this is a hallmark of genome regulation in mammalian cells, the mechanisms concerning the assembly, organisation and stability remain unknown. Here, we have used combination of single molecule imaging and genomic approaches to explore the role of nuclear myosin VI (MVI) in the nanoscale organisation of RNAPII. We reveal that MVI in the nucleus acts as the molecular anchor that holds RNAPII in high density clusters. Perturbation of MVI leads to the disruption of RNAPII localisation, chromatin organisation and subsequently a decrease in gene expression. Overall, we uncover the fundamental role of MVI in the spatial regulation of gene expression.

GXP: Analyze and Plot Plant Omics Data in Web Browsers

Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction)

Molecular Subtyping Resource: a user-friendly tool for rapid biological discovery from transcriptional data

Generation of transcriptional data has dramatically increased in the past decade, driving the development of analytical algorithms that enable interrogation of the biology underpinning the profiled samples. However, these resources require users to have expertise in data wrangling and analytics, reducing opportunities for biological discovery by 'wet-lab' users with a limited programming skillset. Although commercial solutions exist, costs for software access can be prohibitive for academic research groups. To address these challenges, we have developed an open source and user-friendly data analysis platform for on-the-fly bioinformatic interrogation of transcriptional data derived from human or mouse tissue, called Molecular Subtyping Resource (MouSR). This internet-accessible analytical tool, https://mousr.qub.ac.uk/, enables users to easily interrogate their data using an intuitive 'point-and-click' interface, which includes a suite of molecular characterisation options including quality control, differential gene expression, gene set enrichment and microenvironmental cell population analyses from RNA sequencing. The MouSR online tool provides a unique freely available option for users to perform rapid transcriptomic analyses and comprehensive interrogation of the signalling underpinning transcriptional datasets, which alleviates a major bottleneck for biological discovery. This article has an associated First Person interview with the first author of the paper.

DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets

High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

Establishment and characterization of prostate organoids from treatment-naïve patients with prostate cancer

Three-dimensional (3D) organoid culture systems are emerging as potential reliable tools to investigate basic developmental processes of human disease, especially cancer. The present study used established and modified culture conditions to report successful generation and characterization of patient-derived organoids from fresh primary tissue specimens of patients with treatment-naïve prostate cancer (PCa). Fresh tissue specimens were collected, digested enzymatically and the resulting cell suspensions were plated in a 3D environment using Matrigel as an extracellular matrix. Previously established 12-factor medium for organoid culturing was modified to create a minimal 5-factor medium. Organoids and corresponding tissue specimens were characterized using transcriptomic analysis, immunofluorescent analysis, and immunohistochemistry. Furthermore, patient-derived organoids were used to assess the drug response. Treatment-naïve patient-derived PCa organoids were obtained from fresh radical prostatectomy specimens. These PCa organoids mimicked the heterogeneity of corresponding parental tumor tissue. Histopathological analysis demonstrated similar tissue architecture and cellular morphology, as well as consistent immunohistochemical marker expression. Also, the results confirmed the potential of organoids as an in vitro model to assess potential personalized treatment responses as there was a differential drug response between different patient samples. In conclusion, the present study investigated patient-derived organoids from a cohort of treatment-naïve patients. Derived organoids mimicked the histological features and prostate lineage profiles of their corresponding parental tissue and may present a potential model to predict patient-specific treatment response in a pre-clinical setting.

Mouse Embryonic Fibroblast Adipogenic Potential: A Comprehensive Transcriptome Analysis

Our understanding of adipose tissue has progressed from an inert tissue for energy storage to be one of the largest endocrine organs regulating metabolic homoeostasis through its ability to synthesize and release various adipokines that regulate a myriad of pathways. The field of adipose tissue biology is growing due to this association with various chronic metabolic diseases. An important process in the regulation of adipose tissue biology is adipogenesis, which is the formation of new adipocytes. Investigating adipogenesis in vitro is currently a focus for identifying factors that might be utilized in clinically. A powerful tool for such work is high-throughput sequencing which can rapidly identify changes at gene expression level. Various cell models exist for studying adipogenesis and has been used in high-throughput studies, yet little is known about transcriptome profile that underlies adipogenesis in mouse embryonic fibroblasts. This study utilizes RNA-sequencing and computational analysis with DESeq2, gene ontology, protein–protein networks, and robust rank analysis to understand adipogenesis in mouse embryonic fibroblasts in-depth. Our analyses confirmed the requirement of mitotic clonal expansion prior to adipogenesis in this cell model and highlight the role of Cebpa and Cebpb in regulating adipogenesis through interactions of large numbers of genes.

GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

Platelet proteome dynamics in hibernating 13-lined ground squirrels

Hibernating mammals undergo a dramatic drop in temperature and blood flow during torpor, yet avoid stasis blood clotting through mechanisms that remain unspecified. The effects of hibernation on hemostasis are especially complex, as cold temperatures generally activate platelets, resulting in platelet clearance and cold storage lesions in the context of blood transfusion. With a hibernating body temperature of 4°C-8°C, 13-lined ground squirrels (Ictidomys tridecemlineatus) provide a model to study hemostasis as well as platelet cold storage lesion resistance during hibernation. Here, we quantified and systematically compared proteomes of platelets collected from ground squirrels at summer (active), fall (entrance), and winter (topor) to elucidate how molecular-level changes in platelets may support hemostatic adaptations in torpor. Platelets were isolated from a total of 11 squirrels in June, October, and January. Platelet lysates from each animal were digested with trypsin prior to 11-plex tandem mass tag (TMT) labeling, followed by LC-MS/MS analysis for relative protein quantification. We measured >700 proteins with significant variations in abundance in platelets over the course of entrance, torpor, and activity-including systems of proteins regulating translation, secretion, metabolism, complement, and coagulation cascades. We also noted species-specific differences in levels of hemostatic, secretory, and inflammatory regulators in ground squirrel platelets relative to human platelets. Altogether, we provide the first ever proteomic characterization of platelets from hibernating animals, where systematic changes in metabolic, hemostatic, and other proteins may account for physiological adaptations in torpor and also inform translational effort to improve cold storage of human platelets for transfusion.

Transcriptome-Wide Analysis Reveals a Role for Extracellular Matrix and Integrin Receptor Genes in Otic Neurosensory Differentiation from Human iPSCs

We analyzed transcriptomic data from otic sensory cells differentiated from human induced pluripotent stem cells (hiPSCs) by a previously described method to gain new insights into the early human otic neurosensory lineage. We identified genes and biological networks not previously described to occur in the human otic sensory developmental cell lineage. These analyses identified and ranked genes known to be part of the otic sensory lineage program (SIX1, EYA1, GATA3, etc.), in addition to a number of novel genes encoding extracellular matrix (ECM) (COL3A1, COL5A2, DCN, etc.) and integrin (ITG) receptors (ITGAV, ITGA4, ITGA) for ECM molecules. The results were confirmed by quantitative PCR analysis of a comprehensive panel of genes differentially expressed during the time course of hiPSC differentiation in vitro. Immunocytochemistry validated results for select otic and ECM/ITG gene markers in the in vivo human fetal inner ear. Our screen shows ECM and ITG gene expression changes coincident with hiPSC differentiation towards human otic neurosensory cells. Our findings suggest a critical role of ECM-ITG interactions with otic neurosensory lineage genes in early neurosensory development and cell fate determination in the human fetal inner ear.

Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function

Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer's disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53's transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent anticancer agent.

Gsx1 promotes locomotor functional recovery after spinal cord injury

Promoting residential cells, particularly endogenous neural stem and progenitor cells (NSPCs), for tissue regeneration represents a potential strategy for the treatment of spinal cord injury (SCI). However, adult NSPCs differentiate mainly into glial cells and contribute to glial scar formation at the site of injury. Gsx1 is known to regulate the generation of excitatory and inhibitory interneurons during embryonic development of the spinal cord. In this study, we show that lentivirus-mediated expression of Gsx1 increases the number of NSPCs in a mouse model of lateral hemisection SCI during the acute stage. Subsequently, Gsx1 expression increases the generation of glutamatergic and cholinergic interneurons and decreases the generation of GABAergic interneurons in the chronic stage of SCI. Importantly, Gsx1 reduces reactive astrogliosis and glial scar formation, promotes serotonin (5-HT) neuronal activity, and improves the locomotor function of the injured mice. Moreover, RNA sequencing (RNA-seq) analysis reveals that Gsx1-induced transcriptome regulation correlates with NSPC signaling, NSPC activation, neuronal differentiation, and inhibition of astrogliosis and scar formation. Collectively, our study provides molecular insights for Gsx1-mediated functional recovery and identifies the potential of Gsx1 gene therapy for injuries in the spinal cord and possibly other parts of the central nervous system.

ideal: an R/Bioconductor package for interactive differential expression analysis

BACKGROUND

RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.

RESULTS

We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.

CONCLUSION

ideal is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/ideal/ ), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

Gbx2 Identifies Two Amacrine Cell Subtypes with Distinct Molecular, Morphological, and Physiological Properties

Our understanding of nervous system function is limited by our ability to identify and manipulate neuronal subtypes within intact circuits. We show that the Gbx2^{CreERT2-IRES-EGFP} mouse line labels two amacrine cell (AC) subtypes in the mouse retina that have distinct morphological, physiological, and molecular properties. Using a combination of RNA-seq, genetic labeling, and patch clamp recordings, we show that one subtype is GABAergic that receives excitatory input from On bipolar cells. The other population is a non-GABAergic, non-glycinergic (nGnG) AC subtype that lacks the expression of standard neurotransmitter markers. Gbx2⁺ nGnG ACs have smaller, asymmetric dendritic arbors that receive excitatory input from both On and Off bipolar cells. Gbx2⁺ nGnG ACs also exhibit spatially restricted tracer coupling to bipolar cells (BCs) through gap junctions. This study identifies a genetic tool for investigating the two distinct AC subtypes, and it provides a model for studying synaptic communication and visual circuit function.

Investigations into neural circuit development and function are limited by the lack of genetic tools to label and perturb individual neuronal subtypes. Using the Gbx2^CreERT2 mouse line, Kerstein et al. identify two amacrine cell subtypes in the mouse retina and explore their distinct molecular, morphological, and physiological characteristics.

Epidermal Growth Factor Is Essential for the Maintenance of Novel Prostate Epithelial Cells Isolated From Patient-Derived Organoids

Prostate cancer (PCa) is the second leading cause of cancer-related mortality and morbidity among males worldwide. Deciphering the biological mechanisms and molecular pathways involved in PCa pathogenesis and progression has been hindered by numerous technical limitations mainly attributed to the limited number of cell lines available, which do not recapitulate the diverse phenotypes of clinical disease. Indeed, PCa has proven problematic to establish as cell lines in culture due to its heterogeneity which remains a challenge, despite the various in vitro and in vivo model systems available. Growth factors have been shown to play a central role in the complex regulation of cell proliferation among hormone sensitive tumors, such as PCa. Here, we report the isolation and characterization of novel patient-derived prostate epithelial (which we named as AUB-PrC) cells from organoids culture system. We also assessed the role of epidermal growth factor (EGF) in culturing those cells. We profiled the AUB-PrC cells isolated from unaffected and tumor patient samples via depicting their molecular and epithelial lineage features through immunofluorescence staining and quantitative real-time PCR (qRT-PCR), as well as through functional assays and transcriptomic profiling through RNA sequencing. In addition, by optimizing a previously established prostate organoids culture system, we were able to grow human prostate epithelial cells using growth medium and EGF only. With these data collected, we were able to gain insight at the molecular architecture of novel human AUB-PrC cells, which might pave the way for deciphering the mechanisms that lead to PCa development and progression, and ultimately improving prognostic abilities and treatments.

β-actin contributes to open chromatin for activation of the adipogenic pioneer factor CEBPA during transcriptional reprograming

Adipogenesis is regulated by a cascade of signals that drive transcriptional reprogramming in adipocytes. Here, we report that nuclear actin regulates the chromatin states that establish tissue- specific expression during adipogenesis. To study the role of β-actin in adipocyte differentiation, we conducted RNA sequencing on wild-type and β-actin knockout mouse embryonic fibroblasts (MEFs) after reprograming to adipocytes. We found that β-actin depletion affects induction of several adipogenic genes during transcriptional reprograming. This impaired regulation of adipogenic genes is linked to reduced expression of the pioneer factor Cebpa and is rescued by reintroducing NLS-tagged β-actin. ATAC-Seq in knockout MEFs revealed that actin-dependent reduction of Cebpa expression correlates with decreased chromatin accessibility and loss of chromatin association of the ATPase Brg1. This, in turn, impairs CEBPB's association with its Cebpa promoter-proximal binding site during adipogenesis. We propose a role for the nuclear β-actin pool in maintaining open chromatin for transcriptional reprogramming during adipogenic differentiation.

Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq

RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years, even though, there are several programs or websites that assist researchers to analyze and visualize NGS results, they have several limitations. Therefore, Shiny-DEG, a web application that facilitates the exploration and visualization of differentially expressed genes from RNA-seq, was developed. It integrates multi-factor design experiments, allows users to modify the parameters interactively according to experiments purpose and all analysis results can be downloaded directly, aiming to further assisting the interpretation and explanation of the biological questions. Therefore, it serves better for biologists without programming skills. Overall, this project is of great significance to reveal the mechanism of transcriptome differences.

NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization

Background

As high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).

Results

NASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.

Conclusions

NASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

Automated Hypothesis Generation to Identify Signals Relevant in the Development of Mammalian Cell and Tissue Bioprocesses, With Validation in a Retinal Culture System

We have developed an accessible software tool (receptoR) to predict potentially active signaling pathways in one or more cell type(s) of interest from publicly available transcriptome data. As proof-of-concept, we applied it to mouse photoreceptors, yielding the previously untested hypothesis that activin signaling pathways are active in these cells. Expression of the type 2 activin receptor (Acvr2a) was experimentally confirmed by both RT-qPCR and immunochemistry, and activation of this signaling pathway with recombinant activin A significantly enhanced the survival of magnetically sorted photoreceptors in culture. Taken together, we demonstrate that our approach can be easily used to mine publicly available transcriptome data and generate hypotheses around receptor expression that can be used to identify novel signaling pathways in specific cell types of interest. We anticipate that receptoR (available at https://www.ucalgary.ca/ungrinlab/receptoR) will enable more efficient use of limited research resources.

BEAVR: a browser-based tool for the exploration and visualization of RNA-seq data

BACKGROUND

The use of RNA-sequencing (RNA-seq) in molecular biology research and clinical settings has increased significantly over the past decade. Despite its widespread adoption, there is a lack of simple and interactive tools to analyze and explore RNA-seq data. Many established tools require programming or Unix/Bash knowledge to analyze and visualize results. This requirement presents a significant barrier for many researchers to efficiently analyze and present RNA-seq data.

RESULTS

Here we present BEAVR, a Browser-based tool for the Exploration And Visualization of RNA-seq data. BEAVR is an easy-to-use tool that facilitates interactive analysis and exploration of RNA-seq data. BEAVR is developed in R and uses DESeq2 as its engine for differential gene expression (DGE) analysis, but assumes users have no prior knowledge of R or DESeq2. BEAVR allows researchers to easily obtain a table of differentially-expressed genes with statistical testing and then visualize the results in a series of graphs, plots and heatmaps. Users are able to customize many parameters for statistical testing, dealing with variance, clustering methods and pathway analysis to generate high quality figures.

CONCLUSION

BEAVR simplifies analysis for novice users but also streamlines the RNA-seq analysis process for experts by automating several steps. BEAVR and its documentation can be found on GitHub at https://github.com/developerpiru/BEAVR. BEAVR is available as a Docker container at https://hub.docker.com/r/pirunthan/beavr.

A Precision Medicine Approach Uncovers a Unique Signature of Neutrophils in Patients With Brushite Kidney Stones

Introduction

We have previously found that papillary histopathology differs greatly between calcium oxalate and brushite stone formers (SF); the latter have much more papillary mineral deposition, tubular cell injury, and tissue fibrosis.

Methods

In this study, we applied unbiased orthogonal omics approaches on biopsied renal papillae and extracted stones from patients with brushite or calcium oxalate (CaOx) stones. Our goal was to discover stone type-specific molecular signatures to advance our understanding of the underlying pathogenesis.

Results

Brushite SF did not differ from CaOx SF with respect to metabolic risk factors for stones but did exhibit increased tubule plugging in their papillae. Brushite SF had upregulation of inflammatory pathways in papillary tissue and increased neutrophil markers in stone matrix compared with those with CaOx stones. Large-scale 3-dimensional tissue cytometry on renal papillary biopsies showed an increase in the number and density of neutrophils in the papillae of patients with brushite versus CaOx, thereby linking the observed inflammatory signatures to the neutrophils in the tissue. To explain how neutrophil proteins appear in the stone matrix, we measured neutrophil extracellular trap (NET) formation—NETosis—and found it significantly increased in the papillae of patients with brushite stones compared with CaOx stones.

Conclusion

We show that increased neutrophil infiltration and NETosis is an unrecognized factor that differentiates brushite and CaOx SF and may explain the markedly increased scarring and inflammation seen in the papillae of patients with brushite stones. Given the increasing prevalence of brushite stones, the role of neutrophil activation in brushite stone formation requires further study.

CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors

Depleting regulatory T cells (T_reg cells) to counteract immunosuppressive features of the tumor microenvironment (TME) is an attractive strategy for cancer treatment; however, autoimmunity due to systemic impairment of their suppressive function limits its therapeutic potential. Elucidating approaches that specifically disrupt intratumoral T_reg cells is direly needed for cancer immunotherapy. We found that CD36 was selectively upregulated in intrautumoral T_reg cells as a central metabolic modulator. CD36 fine-tuned mitochondrial fitness via peroxisome proliferator-activated receptor-β signaling, programming T_reg cells to adapt to a lactic acid-enriched TME. Genetic ablation of Cd36 in T_reg cells suppressed tumor growth accompanied by a decrease in intratumoral T_reg cells and enhancement of antitumor activity in tumor-infiltrating lymphocytes without disrupting immune homeostasis. Furthermore, CD36 targeting elicited additive antitumor responses with anti-programmed cell death protein 1 therapy. Our findings uncover the unexplored metabolic adaptation that orchestrates the survival and functions of intratumoral T_reg cells, and the therapeutic potential of targeting this pathway for reprogramming the TME.

Modeling population heterogeneity from microbial communities to immune response in cells

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this "data revolution" requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models

BACKGROUND

RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis.

RESULTS

DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress.

CONCLUSIONS

DiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results.

WIlsON: Web-based Interactive Omics VisualizatioN

Motivation

High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization.

Results

Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots.

Availability and implementation

We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.

omicplotR: visualizing omic datasets as compositions

BACKGROUND

Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method.

RESULTS

omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset.

CONCLUSIONS

omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

GENAVi: a shiny web application for gene expression normalization, analysis and visualization

BACKGROUND

The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge.

RESULTS

We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis.

CONCLUSION

The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation

Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

BACKGROUND

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.

RESULTS

We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components.

CONCLUSION

pcaExplorer is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/pcaExplorer/ ), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.

HRI coordinates translation necessary for protein homeostasis and mitochondrial function in erythropoiesis

Iron and heme play central roles in the production of red blood cells, but the underlying mechanisms remain incompletely understood. Heme-regulated eIF2α kinase (HRI) controls translation by phosphorylating eIF2α. Here, we investigate the global impact of iron, heme, and HRI on protein translation in vivo in murine primary erythroblasts using ribosome profiling. We validate the known role of HRI-mediated translational stimulation of integratedstressresponse mRNAs during iron deficiency in vivo. Moreover, we find that the translation of mRNAs encoding cytosolic and mitochondrial ribosomal proteins is substantially repressed by HRI during iron deficiency, causing a decrease in cytosolic and mitochondrial protein synthesis. The absence of HRI during iron deficiency elicits a prominent cytoplasmic unfolded protein response and impairs mitochondrial respiration. Importantly, ATF4 target genes are activated during iron deficiency to maintain mitochondrial function and to enable erythroid differentiation. We further identify GRB10 as a previously unappreciated regulator of terminal erythropoiesis.

Red blood cells use a molecule called hemoglobin to transport oxygen around the body. To make hemoglobin, cells require iron to build a component called heme. If an individual does not get enough iron in their diet, the body cannot produce enough red blood cells, or the cells lack hemoglobin. This condition is known as iron deficiency anemia, and it affects around one-third of the world’s population.

Researchers did not know exactly how iron levels control red blood cell production, though several proteins had been identified to play important roles. Heme forms in the cell's mitochondria: the compartments in the cell that supply it with energy. When heme levels in a developing red blood cell are low, a protein called HRI reduces the production of many proteins, most importantly the proteins that make up hemoglobin. HRI also boosts the production of a protein called ATF4, which switches on a set of genes that help both the cell and its mitochondria to adapt to the lack of heme. In turn, HRI and ATF4 reduce the activity of a signaling pathway called mTORC1, which controls the production of proteins that help cells to grow and divide.

To understand in more detail how iron and heme regulate the production of new red blood cells, Zhang et al. looked at immature red blood cells from the livers of developing mice. Some of the mice lacked the gene that produces HRI, and some experienced iron deficiency. Comparing gene activity in the different mice revealed that in the developing blood cells of iron-deficient mice, HRI largely reduces the rate of protein production in both the mitochondria and the wider cell. At the same time, the increased activity of ATF4 allows the mitochondria to carry on releasing energy and the cells to continue developing. Zhang et al. also found that a protein that inhibits the mTORC1 signaling pathway needs to be active for the new red blood cells to mature.

Overall, the results suggest that drugs that activate HRI or block the activity of the mTORC1 pathway could help to treat anemia. The next step is to test the effects that such drugs have in anemic mice and cells from anemic people.

PD-1 Dynamically Regulates Inflammation and Development of Brain-Resident Memory CD8 T Cells During Persistent Viral Encephalitis

Programmed cell death-1 (PD-1) receptor signaling dampens the functionality of T cells faced with repetitive antigenic stimulation from chronic infections or tumors. Using intracerebral (i.c.) inoculation with mouse polyomavirus (MuPyV), we have shown that CD8 T cells establish a PD-1^hi, tissue-resident memory population in the brains (bT_RM) of mice with a low-level persistent infection. In MuPyV encephalitis, PD-L1 was expressed on infiltrating myeloid cells, microglia and astrocytes, but not on oligodendrocytes. Engagement of PD-1 on anti-MuPyV CD8 T cells limited their effector activity. NanoString gene expression analysis showed that neuroinflammation was higher in PD-L1^-/- than wild type mice at day 8 post-infection, the peak of the MuPyV-specific CD8 response. During the persistent phase of infection, however, the absence of PD-1 signaling was found to be associated with a lower inflammatory response than in wild type mice. Genetic disruption and intracerebroventricular blockade of PD-1 signaling resulted in an increase in number of MuPyV-specific CD8 bT_RM and the fraction of these cells expressing CD103, the αE integrin commonly used to define tissue-resident T cells. However, PD-L1^-/- mice persistently infected with MuPyV showed impaired virus control upon i.c. re-infection with MuPyV. Collectively, these data reveal a temporal duality in PD-1-mediated regulation of MuPyV-associated neuroinflammation. PD-1 signaling limited the severity of neuroinflammation during acute infection but sustained a level of inflammation during persistent infection for maintaining control of virus re-infection.

Analysis of Global Transcriptome Change in Mouse Embryonic Fibroblasts After dsDNA and dsRNA Viral Mimic Stimulation

The activation of innate immunity by viral nucleic acids present in the cytoplasm plays an essential role in controlling viral infection in both immune and non-immune cells. The dsDNA and dsRNA viral mimics can stimulate the cytosolic nucleic acids sensors and activate the antiviral innate immunity. In this study, taking advantage of dsDNA and dsRNA viral mimics, we investigated the global transcriptome changes after the antiviral immunity activation in mouse embryonic fibroblasts. Results from our data identified a positive feedback up-regulation of sensors (e.g., Tlr2, Tlr3, Ddx58, cGAS), transducers (e.g., Traf2, Tbk1) and transcription factors (e.g., Irf7, Jun, Stat1, Stat2) in multiple pathways involved in detecting viral or microbial infections upon viral mimic stimulation. A group of genes involved in DNA damage response and DNA repair such as Parp9, Dtx3l, Rad52 were also up-regulated, implying the involvement of these genes in antiviral immunity. Molecular function analysis further showed that groups of helicase genes (e.g., Dhx58, Helz2), nuclease genes (e.g., Dnase1l3, Rsph10b), methyltransferase genes (e.g., histone methyltransferase Prdm9, Setdb2; RNA methyltransferase Mettl3, Mttl14), and protein ubiquitin-ligase genes (e.g., Trim genes and Rnf genes) were up-regulated upon antiviral immunity activation. In contrast, viral mimic stimulation down-regulated genes involved in a broad range of general biological processes (e.g., cell division, metabolism), cellular components (e.g., mitochondria and ribosome), and molecular functions (e.g., cell-cell adhesion, microtubule binding). In summary, our study provides valuable information about the global transcriptome changes upon antiviral immunity activation. The identification of novel groups of genes up-regulated upon antiviral immunity activation serves as useful resource for mining new antiviral sensors and effectors.

Genome-wide association study in frontal fibrosing alopecia identifies four susceptibility loci including HLA-B*07:02

Frontal fibrosing alopecia (FFA) is a recently described inflammatory and scarring type of hair loss affecting almost exclusively women. Despite a dramatic recent increase in incidence the aetiopathogenesis of FFA remains unknown. We undertake genome-wide association studies in females from a UK cohort, comprising 844 cases and 3,760 controls, a Spanish cohort of 172 cases and 385 controls, and perform statistical meta-analysis. We observe genome-wide significant association with FFA at four genomic loci: 2p22.2, 6p21.1, 8q24.22 and 15q2.1. Within the 6p21.1 locus, fine-mapping indicates that the association is driven by the HLA-B*07:02 allele. At 2p22.1, we implicate a putative causal missense variant in CYP1B1, encoding the homonymous xenobiotic- and hormone-processing enzyme. Transcriptomic analysis of affected scalp tissue highlights overrepresentation of transcripts encoding components of innate and adaptive immune response pathways. These findings provide insight into disease pathogenesis and characterise FFA as a genetically predisposed immuno-inflammatory disorder driven by HLA-B*07:02.

Frontal fibrosing alopecia (FFA) features lichenoid cutaneous inflammation and scarring hair loss. Here, Tziotzios et al. identify four genetic loci associated with FFA by GWAS followed by Bayesian fine-mapping, co-localisation and HLA imputation which highlights HLA-B*07:02 as a risk factor.

iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform

BACKGROUND

The use of microarrays and RNA-seq technologies is ubiquitous for transcriptome analyses in modern biology. With proper analysis tools, the differential gene expression analysis process can be significantly accelerated. Many open-source programs provide cutting-edge techniques, but these often require programming skills and lack intuitive and interactive or graphical user interfaces. To avoid bottlenecks impeding seamless analysis processing, we have developed an Interactive Gene Expression Analysis Kit, we term iGEAK, focusing on usability and interactivity. iGEAK is designed to be a simple, intuitive, light-weight that contrasts with heavy-duty programs.

RESULTS

iGEAK is an R/Shiny-based client-side desktop application, providing an interactive gene expression data analysis pipeline for microarray and RNA-seq data. Gene expression data can be intuitively explored using a seamless analysis pipeline consisting of sample selection, differentially expressed gene prediction, protein-protein interaction, and gene set enrichment analyses. For each analysis step, users can easily alter parameters to mine more relevant biological information.

CONCLUSION

iGEAK is the outcome of close collaboration with wet-bench biologists who are eager to easily explore, mine, and analyze new or public microarray and RNA-seq data. We designed iGEAK as a gene expression analysis pipeline tool to provide essential analysis steps and a user-friendly interactive graphical user interface. iGEAK enables users without programing knowledge to comfortably perform differential gene expression predictions and downstream analyses. iGEAK packages, manuals, tutorials, sample datasets are available at the iGEAK project homepage ( https://sites.google.com/view/iGEAK ).

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.

iSeq: Web-Based RNA-seq Data Analysis and Visualization

Transcriptome sequencing (RNA-seq) is becoming a standard experimental methodology for genome-wide characterization and quantification of transcripts at single base-pair resolution. However, downstream analysis of massive amount of sequencing data can be prohibitively technical for wet-lab researchers. A functionally integrated and user-friendly platform is required to meet this demand. Here, we present iSeq, an R-based Web server, for RNA-seq data analysis and visualization. iSeq is a streamlined Web-based R application under the Shiny framework, featuring a simple user interface and multiple data analysis modules. Users without programming and statistical skills can analyze their RNA-seq data and construct publication-level graphs through a standardized yet customizable analytical pipeline. iSeq is accessible via Web browsers on any operating system at http://iseq.cbi.pku.edu.cn .

Mesenchymal stromal cells from infants with simple polydactyly modulate immune responses more efficiently than adult mesenchymal stromal cells

Bone marrow-derived stromal cells or mesenchymal stromal cells (BMSCs or MSCs, as we will call them in this work) are multipotent progenitor cells that can differentiate into osteoblasts, adipocytes and chondrocytes. In addition, MSCs have been shown to modulate the function of a variety of immune cells. Donor age has been shown to affect the regenerative potential, differentiation, proliferation and anti-inflammatory potency of MSCs; however, the impact of donor age on their immunosuppressive activity is unknown. In this study, we evaluated the ability of MSCs derived from very young children and adults on T-cell suppression and cytokine secretion by monocytes/macrophages. MSCs were obtained from extra digits of children between 10 and 21 months and adults between 28 and 64 years of age. We studied cell surface marker expression, doubling time, lineage differentiation potential and immunosuppressive function of the MSCs. Young MSCs double more quickly and differentiate into bone and fat cells more efficiently than those from older donors. They also form more and dense colonies of fibroblasts (colony forming unit-fibroblast [CFU-F]). MSCs from both young and adult subjects suppressed T-cell proliferation in a mitogen-induced assay at 1:3 and 1:30 ratios. At a 1:30 ratio, however, MSCs from adults did not, but MSCs from infants did suppress T-cell proliferation. In the mixed lymphocyte reaction assay, MSCs from infants produced similar levels of suppression at all three MSC/T-cell ratios, but adult MSCs only inhibited T-cell proliferation at a 1:3 ratio. Cytokine analyses of co-cultures of MSCs and macrophages showed that both adult and young MSCs suppress tumor necrosis factor alpha (TNF-α) and induce interleukin-10 (IL-10) production in macrophage co-culture assay in a similar manner. Overall, this work shows that developing MSCs display a higher level of immunosuppression than mature MSCs.