ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

Single-cell transcriptomic profiling provides insights into retinal endothelial barrier properties

Purpose

To better characterize retinal endothelial barrier properties through analysis of individual transcriptomes of primary bovine retinal microvascular endothelial cells (RMECs).

Methods

Individual RMECs were captured on the Fluidigm C1 system, cDNA libraries were prepared using a Nextera XT kit, and sequencing was performed on a NextSeq system (Illumina). Data analysis was performed using R packages Scater, SC3, and Seurat, and the browser application Automated Single-cell Analysis Pipeline (ASAP). Alternative splicing events in single cells were quantified with Outrigger. Cytoscape was used for network analyses.

Results

Application of a single-cell RNA sequencing (scRNA-seq) analysis workflow showed that RMECs form a relatively homogeneous population in culture, with the main differences related to proliferation status. Expression of markers from along the arteriovenous tree suggested that most cells originated from capillaries. Average gene expression levels across all cells were used to develop an in silico model of the inner blood-retina barrier incorporating junctional proteins not previously reported within the retinal vasculature. Correlation of barrier gene expression among individual cells revealed a subgroup of genes highly correlated with PECAM-1 at the center of the correlation network. Numerous alternative splicing events involving exons within microvascular barrier genes were observed, and in many cases, individual cells expressed one isoform exclusively.

Conclusions

We optimized a workflow for single-cell transcriptomics in primary RMECs. The results provide fundamental insights into the genes involved in formation of the retinal-microvascular barrier.

Adult Neural Stem Cells Are Alerted by Systemic Inflammation through TNF-α Receptor Signaling

Adult stem cells (SCs) transit between the cell cycle and a poorly defined quiescent state. Single neural SCs (NSCs) with quiescent, primed-for-activation, and activated cell transcriptomes have been obtained from the subependymal zone (SEZ), but the functional regulation of these states under homeostasis is not understood. Here, we develop a multilevel strategy to analyze these NSC states with the aim to uncover signals that regulate their level of quiescence/activation. We show that transitions between states occur in vivo and that activated and primed, but not quiescent, states can be captured and studied in culture. We also show that peripherally induced inflammation promotes a transient activation of primed NSCs (pNSCs) mediated by tumor necrosis factor α (TNF-α) acting through its receptor, TNF receptor 2 (TNFR2), and a return to quiescence in a TNF receptor 1 (TNFR1)-dependent manner. Our data identify a signaling pathway promoting NSC alertness and add to the emerging concept that SCs can respond to the systemic milieu.

Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell Heterogeneity

White adipose tissue (WAT) is a cellularly heterogeneous endocrine organ that not only serves as an energy reservoir, but also actively participates in metabolic homeostasis. Among the main constituents of adipose tissue are adipocytes, which arise from adipose stem and progenitor cells (ASPCs). While it is well known that these ASPCs reside in the stromal vascular fraction (SVF) of adipose tissue, their molecular heterogeneity and functional diversity is still poorly understood. Driven by the resolving power of single-cell transcriptomics, several recent studies provided new insights into the cellular complexity of ASPCs among different mammalian fat depots. In this review, we present current knowledge on ASPCs, their population structure, hierarchy, fat depot-specific nature, function, and regulatory mechanisms, and discuss not only the similarities, but also the differences between mouse and human ASPC biology.

Single-Cell Sequencing in Human Genital Infections

Human genital infections are one of the most concerning issues worldwide and can be categorized into sexually transmitted, urinary tract and vaginal infections. These infections, if left untreated, can disseminate to the other parts of the body and cause more complicated illnesses such as pelvic inflammatory disease, urethritis, and anogenital cancers. The effective treatment against these infections is further complicated by the emergence of antimicrobial resistance in the genital infection causing pathogens. Furthermore, the development and applications of single-cell sequencing technologies have open new possibilities to study the drug resistant clones, cell to cell variations, the discovery of acquired drug resistance mutations, transcriptional diversity of a pathogen across different infection stages, to identify rare cell types and investigate different cellular states of genital infection causing pathogens, and to develop novel therapeutical strategies. In this chapter, I will provide a complete review of the applications of single-cell sequencing in human genital infections before discussing their limitations and challenges.

Comparison of visualization tools for single-cell RNAseq data

In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a single cell to millions of cells. Due to its high dimensionality, it is not always feasible to visualize scRNAseq data and share it in a scientific report or an article publication format. Recently, many interactive analysis and visualization tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review several of the currently available scRNAseq visualization tools and benchmark the subset that allows to visualize the data on the web and share it with others. We consider the memory and time required to prepare datasets for sharing as the number of cells increases, and additionally review the user experience and features available in the web interface. To address the problem of format compatibility we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualization.

Single Cell Transcriptome Analysis of Niemann-Pick Disease, Type C1 Cerebella

Niemann-Pick disease, type C1 (NPC1) is a lysosomal disease characterized by endolysosomal storage of unesterified cholesterol and decreased cellular cholesterol bioavailability. A cardinal symptom of NPC1 is cerebellar ataxia due to Purkinje neuron loss. To gain an understanding of the cerebellar neuropathology we obtained single cell transcriptome data from control (Npc1^+/+) and both three-week-old presymptomatic and seven-week-old symptomatic mutant (Npc1^-/-) mice. In seven-week-old Npc1^-/- mice, differential expression data was obtained for neuronal, glial, vascular, and myeloid cells. As anticipated, we observed microglial activation and increased expression of innate immunity genes. We also observed increased expression of innate immunity genes by other cerebellar cell types, including Purkinje neurons. Whereas neuroinflammation mediated by microglia may have both neuroprotective and neurotoxic components, the contribution of increased expression of these genes by non-immune cells to NPC1 pathology is not known. It is possible that dysregulated expression of innate immunity genes by non-immune cells is neurotoxic. We did not anticipate a general lack of transcriptomic changes in cells other than microglia from presymptomatic three-week-old Npc1^-/- mice. This observation suggests that microglia activation precedes neuronal dysfunction. The data presented in this paper will be useful for generating testable hypotheses related to disease progression and Purkinje neurons loss as well as providing insight into potential novel therapeutic interventions.

ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in 'big data' management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

alona: a web server for single-cell RNA-seq analysis

SUMMARY

Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes.

AVAILABILITY AND IMPLEMENTATION

The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA-seq clustering: datasets, models, and algorithms

Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgroups and cell dynamics at the group level. Two important aspects of scRNA-seq data analysis were introduced and discussed in the present review: relevant datasets and analytical tools. In particular, we reviewed popular scRNA-seq datasets and discussed scRNA-seq clustering models including K-means clustering, hierarchical clustering, consensus clustering, and so on. Seven state-of-the-art scRNA clustering methods were compared on five public available datasets. Two primary evaluation metrics, the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI), were used to evaluate these methods. Although unsupervised models can effectively cluster scRNA-seq data, these methods also have challenges. Some suggestions were provided for future research directions.

Single-cell expression and Mendelian randomization analyses identify blood genes associated with lifespan and chronic diseases

The human lifespan is a heritable trait, which is intricately linked to the development of disorders. Here, we show that genetic associations for the parental lifespan are enriched in open chromatin of blood cells. By using blood expression quantitative trait loci (eQTL) derived from 31,684 samples, we identified for the lifespan 125 cis- and 559 trans-regulated expressed genes (eGenes) enriched in adaptive and innate responses. Analysis of blood single-cell expression data showed that eGenes were enriched in dendritic cells (DCs) and the modelling of cell ligand-receptor interactions predicted crosstalk between DCs and a cluster of monocytes with a signature of cytotoxicity. In two-sample Mendelian randomization (MR), we identified 16 blood cis-eGenes causally associated with the lifespan. In MR, the majority of cis-eGene-disorder association pairs had concordant effects with the lifespan. The present work underlined that the lifespan is linked with the immune response and identifies eGenes associated with the lifespan and disorders.

CD29 identifies IFN-γ-producing human CD8+ T cells with an increased cytotoxic potential

Cytotoxic CD8+ T cells can effectively kill target cells by producing cytokines, chemokines, and granzymes. Expression of these effector molecules is however highly divergent, and tools that identify and preselect CD8+ T cells with a cytotoxic expression profile are lacking. Human CD8+ T cells can be divided into IFN-γ- and IL-2-producing cells. Unbiased transcriptomics and proteomics analysis on cytokine-producing fixed CD8+ T cells revealed that IL-2+ cells produce helper cytokines, and that IFN-γ+ cells produce cytotoxic molecules. IFN-γ+ T cells expressed the surface marker CD29 already prior to stimulation. CD29 also marked T cells with cytotoxic gene expression from different tissues in single-cell RNA-sequencing data. Notably, CD29+ T cells maintained the cytotoxic phenotype during cell culture, suggesting a stable phenotype. Preselecting CD29-expressing MART1 TCR-engineered T cells potentiated the killing of target cells. We therefore propose that CD29 expression can help evaluate and select for potent therapeutic T cell products.

Single-Cell Transcriptomic Analysis

Single-cell sequencing measures the sequence information from individual cells using optimized single-cell isolation protocols and next-generation sequencing technologies. Recent advancement in single-cell sequencing has transformed biomedical research, providing insights into diverse biological processes such as mammalian development, immune system function, cellular diversity and heterogeneity, and disease pathogenesis. In this article, we introduce and describe popular commercial platforms for single-cell RNA sequencing, general workflow for data analysis, repositories and databases, and applications for these approaches in biomedical research. © 2020 American Physiological Society. Compr Physiol 10:767-783, 2020.

Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data

As the cost of sequencing drops rapidly, the amount of 'omics data increases exponentially, making data visualization and interpretation-'tertiary' analysis a bottleneck. Specialized analytical tools requiring technical expertise are available. However, consolidated and multi-faceted tools that are easy to use for life scientists is highly needed and currently lacking. Here we present Omics Playground, a user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualization and interpretation of transcriptomics and proteomics data. It provides a large number of different tools in which special attention has been paid to single cell data. With Omics Playground, life scientists can easily perform complex data analysis and visualization without coding, and significantly reduce the time to discovery.

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.

iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data

Summary

Interactive platform for single-cell RNA-sequencing (iS-CellR) is a web-based Shiny application that is designed to provide user-friendly, comprehensive analysis of single-cell RNA sequencing data. iS-CellR has the capability to run on any modern web browser and provides an accessible graphical user interface that enables the user to perform complex single-cell RNA-sequencing analysis without requiring programming skills.

Availability and implementation

iS-CellR is open source and available through GitHub at https://github.com/immcore/iS-CellR. iS-CellR is implemented in Docker and can be launched on any operating system with Docker installed.

Supplementary information

Supplementary data are available at Bioinformatics online.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data

Background

Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools.

Results

Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches.

Conclusions

Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Single-Cell RNA-Sequencing and Metabolomics Analyses Reveal the Contribution of Perivascular Adipose Tissue Stem Cells to Vascular Remodeling

OBJECTIVE

Perivascular adipose tissue (PVAT) plays a vital role in maintaining vascular homeostasis. However, most studies ascribed the function of PVAT in vascular remodeling to adipokines secreted by the perivascular adipocytes. Whether mesenchymal stem cells exist in PVAT and play a role in vascular regeneration remain unknown. Approach and Results: Single-cell RNA-sequencing allowed direct visualization of the heterogeneous PVAT-derived mesenchymal stem cells (PV-ADSCs) at a high resolution and revealed 2 distinct subpopulations, among which one featured signaling pathways crucial for smooth muscle differentiation. Pseudotime analysis of cultured PV-ADSCs unraveled their smooth muscle differentiation trajectory. Transplantation of cultured PV-ADSCs in mouse vein graft model suggested the contribution of PV-ADSCs to vascular remodeling through smooth muscle differentiation. Mechanistically, treatment with TGF-β1 (transforming growth factor β1) and transfection of microRNA (miR)-378a-3p mimics induced a similar metabolic reprogramming of PV-ADSCs, including upregulated mitochondrial potential and altered lipid levels, such as increased cholesterol and promoted smooth muscle differentiation.

CONCLUSIONS

Single-cell RNA-sequencing allows direct visualization of PV-ADSC heterogeneity at a single-cell level and uncovers 2 subpopulations with distinct signature genes and signaling pathways. The function of PVAT in vascular regeneration is partly attributed to PV-ADSCs and their differentiation towards smooth muscle lineage. Mechanistic study presents miR-378a-3p which is a potent regulator of metabolic reprogramming as a potential therapeutic target for vascular regeneration.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Current best practices in single-cell RNA-seq analysis: a tutorial

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

A Bioinformatic Toolkit for Single-Cell mRNA Analysis

The recent technological developments in the field of single-cell RNA-Seq enable us to assay the transcriptome of up to a million single cells in parallel. However, the analyses of such big datasets present a major challenge. During the last decade, a wide variety of strategies have been proposed covering different steps of the analysis. Here, we introduce a selection of computational tools to provide an overview of a generic analysis pipeline.The first step of every scRNA-Seq experiment is proper study design, which does not require sophisticated experimental or informatics skills but is nonetheless presumably the most important step. The quality of the resulting data strictly depends on the proper planning of the experiment, including the selection of the most suitable technology for the biological question of interest as well as an elaborated study design to minimize the influence of confounding factors. Once the experiment has been conducted, the raw sequencing data needs to be processed to extract the gene expression information for each cell. This task comprises quality assessment of the sequenced reads, alignment against a reference genome, demultiplexing of the cell barcodes, and quantification of the reads/transcripts per gene. As any other transcriptomics technology, single-cell mRNA-Seq requires data normalization to assure sample-to-sample, here cell-to-cell, comparability and the consideration of confounding factors.Once gene expression values have been extracted from the reads and normalized, the researcher has the agony of choosing between a plethora of analysis approaches to investigate diverse aspects of the single-cell transcriptomes, such as dimensionality reduction and clustering to explore cellular heterogeneity or trajectory analysis to model differentiation processes.In this chapter, we present a wrap-up of the abovementioned steps to conduct single-cell RNA-Seq analyses and present a selection of existing tools.

Simplified Drop-seq workflow with minimized bead loss using a bead capture and processing microfluidic chip

Single-cell RNA-sequencing (scRNA-seq) has revolutionized biomedical research by enabling the in-depth analysis of cell-to-cell heterogeneity of tissues with unprecedented resolution. One of the catalyzing technologies is single cell droplet microfluidics, which has massively increased the overall cell throughput, routinely allowing the analysis of thousands of cells per experiment at a relatively low cost. Among several existing droplet-based approaches, the Drop-seq platform has emerged as one of the most widely used systems. Yet, this has surprisingly not incentivized major refinements of the method, thus restricting any lab implementation to the original Drop-seq setup, which is known to suffer from up to 80% bead loss during the process. In this study, we present a systematic re-engineering and optimization of Drop-seq: first, we re-designed the original dropleting device to be compatible with both air-pressure systems and syringe pumps, thus increasing the overall flexibility of the platform. Second, we devised an accompanying chip for post-encapsulation bead processing, which simplifies and massively increases Drop-seq's cell processing efficiency. Taken together, the presented optimization efforts result in a more flexible and efficient Drop-seq version.

BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing

Despite its widespread use, RNA-seq is still too laborious and expensive to replace RT-qPCR as the default gene expression analysis method. We present a novel approach, BRB-seq, which uses early multiplexing to produce 3' cDNA libraries for dozens of samples, requiring just 2 hours of hands-on time. BRB-seq has a comparable performance to the standard TruSeq approach while showing greater tolerance for lower RNA quality and being up to 25 times cheaper. We anticipate that BRB-seq will transform basic laboratory practice given its capacity to generate genome-wide transcriptomic data at a similar cost as profiling four genes using RT-qPCR.

iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

BACKGROUND

RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis.

RESULTS

iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses.

CONCLUSIONS

Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

BioJupies is a web application that enables the automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses. Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, gene expression tables, or fetch data from >9,000 published studies containing >300,000 preprocessed RNA-seq samples. Generated notebooks have the executable code of the entire pipeline, rich narrative text, interactive data visualizations, differential expression, and enrichment analyses. The notebooks are permanently stored in the cloud and made available online through a persistent URL. The notebooks are downloadable, customizable, and can run within a Docker container. By providing an intuitive user interface for notebook generation for RNA-seq data analysis, starting from the raw reads all the way to a complete interactive and reproducible report, BioJupies is a useful resource for experimental and computational biologists. BioJupies is freely available as a web-based application from http://biojupies.cloud.

Oestrogen receptor α AF-1 and AF-2 domains have cell population-specific functions in the mammary epithelium

Oestrogen receptor α (ERα) is a transcription factor with ligand-independent and ligand-dependent activation functions (AF)-1 and -2. Oestrogens control postnatal mammary gland development acting on a subset of mammary epithelial cells (MECs), termed sensor cells, which are ERα-positive by immunohistochemistry (IHC) and secrete paracrine factors, which stimulate ERα-negative responder cells. Here we show that deletion of AF-1 or AF-2 blocks pubertal ductal growth and subsequent development because both are required for expression of essential paracrine mediators. Thirty percent of the luminal cells are ERα-negative by IHC but express Esr1 transcripts. This low level ERα expression through AF-2 is essential for cell expansion during puberty and growth-inhibitory during pregnancy. Cell-intrinsic ERα is not required for cell proliferation nor for secretory differentiation but controls transcript levels of cell motility and cell adhesion genes and a stem cell and epithelial mesenchymal transition (EMT) signature identifying ERα as a key regulator of mammary epithelial cell plasticity.

Oestrogen receptors α (ERα) are expressed in a subset of mammary epithelial cells. Here, the authors identify cells with low-ERα protein levels and show that distinct cell populations have distinct requirements for the AF1 and AF2 domains of the ERα, and ERα acts in a biphasic manner dependent on developmental stage.

anexVis: visual analytics framework for analysis of RNA expression

Summary

Although RNA expression data are accumulating at a remarkable speed, gaining insights from them still requires laborious analyses, which hinder many biological and biomedical researchers. This report introduces a visual analytics framework that applies several well-known visualization techniques to leverage understanding of an RNA expression dataset. Our analyses on glycosaminoglycan-related genes have demonstrated the broad application of this tool, anexVis (analysis of RNA expression), to advance the understanding of tissue-specific glycosaminoglycan regulation and functions, and potentially other biological pathways.

Availability and implementation

The application is accessible at https://anexvis.chpc.utah.edu/, source codes deposited on GitHub.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeting STING with covalent small-molecule inhibitors

Aberrant activation of innate immune pathways is associated with a variety of diseases. Progress in understanding the molecular mechanisms of innate immune pathways has led to the promise of targeted therapeutic approaches, but the development of drugs that act specifically on molecules of interest remains challenging. Here we report the discovery and characterization of highly potent and selective small-molecule antagonists of the stimulator of interferon genes (STING) protein, which is a central signalling component of the intracellular DNA sensing pathway^1,2. Mechanistically, the identified compounds covalently target the predicted transmembrane cysteine residue 91 and thereby block the activation-induced palmitoylation of STING. Using these inhibitors, we show that the palmitoylation of STING is essential for its assembly into multimeric complexes at the Golgi apparatus and, in turn, for the recruitment of downstream signalling factors. The identified compounds and their derivatives reduce STING-mediated inflammatory cytokine production in both human and mouse cells. Furthermore, we show that these small-molecule antagonists attenuate pathological features of autoinflammatory disease in mice. In summary, our work uncovers a mechanism by which STING can be inhibited pharmacologically and demonstrates the potential of therapies that target STING for the treatment of autoinflammatory disease.

Comparative analysis of single-cell RNA sequencing data from mouse spermatogonial and mesenchymal stem cells to identify differentially expressed genes and transcriptional regulators of germline cells

Identifying effective internal factors for regulating germline commitment during development and for maintaining spermatogonial stem cells (SSCs) self-renewal is important to understand the molecular basis of spermatogenesis process, and to develop new protocols for the production of the germline cells from other cell sources. Therefore, this study was designed to investigate single-cell RNA-sequencing data for identification of differentially expressed genes (DEGs) in 12 mouse-derived single SSCs (mSSCs) in compare with 16 mouse-derived single mesenchymal stem cells. We also aimed to find transcriptional regulators of DEGs. Collectively, 1,584 up-regulated DEGs were identified that are associated with 32 biological processes. Moreover, investigation of the expression profiles of genes including in spermatogenesis process revealed that Dazl, Ddx4, Sall4, Fkbp6, Tex15, Tex19.1, Rnf17, Piwil2, Taf7l, Zbtb16, and Cadm1 are presented in the first 30 up-regulated DEGs. We also found 12 basal transcription factors (TFs) and three sequence-specific TFs that control the expression of DEGs. Our findings also indicated that MEIS1, SMC3, TAF1, KAT2A, STAT3, GTF3C2, SIN3A, BDP1, PHC1, and EGR1 are the main central regulators of DEGs in mSSCs. In addition, we collectively detected two significant protein complexes in the protein-protein interactions network for DEGs regulators. Finally, this study introduces the major upstream kinases for the main central regulators of DEGs and the components of core protein complexes. In conclusion, this study provides a molecular blueprint to uncover the molecular mechanisms behind the biology of SSCs and offers a list of candidate factors for cell type conversion approaches and production of germ cells.

PIVOT: platform for interactive analysis and visualization of transcriptomics data

Background

Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track.

Results

Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced.

Conclusions

PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1994-0) contains supplementary material, which is available to authorized users.

Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

BACKGROUND

Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills.

RESULTS

We have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.

CONCLUSIONS

Granatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app.

Cross-Tissue Identification of Somatic Stem and Progenitor Cells Using a Single-Cell RNA-Sequencing Derived Gene Signature

A long-standing question in biology is whether multipotent somatic stem and progenitor cells (SSPCs) feature molecular properties that could guide their system-independent identification. Population-based transcriptomic studies have so far not been able to provide a definite answer, given the rarity and heterogeneous nature of these cells. Here, we exploited the resolving power of single-cell RNA-sequencing to develop a computational model that is able to accurately distinguish SSPCs from differentiated cells across tissues. The resulting classifier is based on the combined expression of 23 genes including known players in multipotency, proliferation, and tumorigenesis, as well as novel ones, such as Lcp1 and Vgll4 that we functionally validate in intestinal organoids. We show how this approach enables the identification of stem-like cells in still ambiguous systems such as the pancreas and the epidermis as well as the exploration of lineage commitment hierarchies, thus facilitating the study of biological processes such as cellular differentiation, tissue regeneration, and cancer. Stem Cells 2017;35:2390-2402.

Profiling of Single-Cell Transcriptomes

Complex biological systems are composed of multiple cell types whose transcriptional activity can vary due to differences in cell state, environmental stimulation, or intrinsic programs. Conventional bulk analysis methods capture the average transcriptional programs of the cell population, thus missing the unique cellular signature of each single cell. In recent years, the development of single-cell RNA-sequencing (scRNA-seq) technologies has provided a powerful approach to dissect the cellular heterogeneity of complex biological systems. However, such approaches require specialized equipment or are costly. In this article, we describe an improved Smart-seq2-based method to profile the transcriptome of hundreds of single cells simultaneously, without utilizing commercial kits or requiring any specialized single-cell capture/library preparation tools. Moreover, we introduce the Automated Single-cell Analysis Pipeline (ASAP), which allows researchers without strong computational expertise to explore scRNA-seq data using a wide range of commonly used algorithms and sophisticated visualization tools. © 2017 by John Wiley & Sons, Inc.

Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data

An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.