SCHNAPPs - Single Cell sHiNy APPlication(s)

SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software

Single-cell transcriptomics techniques, such as scRNA-seq, attempt to characterize gene expression profiles in each cell of a heterogeneous sample individually. Due to growing amounts of data generated and the increasing complexity of the computational protocols needed to process the resulting datasets, the demand for dedicated training in mathematical and programming skills may preclude the use of these powerful techniques by many teams. In order to help close that gap between wet-lab and dry-lab capabilities we have developed SinglePointRNA, a shiny-based R application that provides a graphic interface for different publicly available tools to analyze single cell RNA-seq data. The aim of SinglePointRNA is to provide an accessible and transparent tool set to researchers that allows them to perform detailed and custom analysis of their data autonomously. SinglePointRNA is structured in a context-driven framework that prioritizes providing the user with solid qualitative guidance at each step of the analysis process and interpretation of the results. Additionally, the rich user guides accompanying the software are intended to serve as a point of entry for users to learn more about computational techniques applied to single cell data analysis. The SinglePointRNA app, as well as case datasets for the different tutorials are available at www.github.com/ScienceParkMadrid/SinglePointRNA.

scX: a user-friendly tool for scRNAseq exploration

Motivation

Single-cell RNA sequencing (scRNAseq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data.

Results

In this article, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis.

Availability and implementation

Source code can be downloaded from https://github.com/chernolabs/scX. A docker image is available from dockerhub as chernolabs/scx.

Metabolic Landscape of Osteosarcoma: Reprogramming of Lactic Acid Metabolism and Metabolic Communication

BACKGROUND

Lactic acid, previously regarded only as an endpoint of glycolysis, has emerged as a major regulator of tumor invasion, growth, and the tumor microenvironment. In this study, we aimed to explore the reprogramming of lactic acid metabolism relevant to osteosarcoma (OS) microenvironment by decoding the underlying lactic acid metabolic landscape of OS cells and intercellular signaling alterations.

METHODS

The landscape of OS metabolism was evaluated using single-cell gene expression data, lactic acid metabolism clustering, and screening of the hub genes in lactic acid metabolism of OS samples using transcriptome data. The role of the hub gene NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 6 (NDUFAF6) was validated with in vitro studies and patient experiments.

RESULTS

Single-cell RNA sequencing data validated a lactic acid metabolismhigh subcluster in OS. Further investigation of intercellular communications revealed a unique metabolic communication pattern between the lactic acid metabolismhigh subcluster and other subclusters. Next, two lactic acid metabolic reprogramming phenotypes were defined, and six lactic acid metabolism-related genes (LRGs), including the biomarker NDUFAF6, were screened in OS. In vitro studies and patient experiments confirmed that NDUFAF6 is a crucial lactic acid metabolism-associated gene in OS.

CONCLUSIONS

The patterns of lactic acid metabolism in OS suggested metabolic reprogramming phenotypes relevant to the tumor microenvironment (TME) and identified NDUFAF6 as an LRG prognostic biomarker.

Understanding the cell fate and behavior of progenitors at the origin of the mouse cardiac mitral valve

Congenital heart malformations include mitral valve defects, which remain largely unexplained. During embryogenesis, a restricted population of endocardial cells within the atrioventricular canal undergoes an endothelial-to-mesenchymal transition to give rise to mitral valvular cells. However, the identity and fate decisions of these progenitors as well as the behavior and distribution of their derivatives in valve leaflets remain unknown. We used single-cell RNA sequencing (scRNA-seq) of genetically labeled endocardial cells and microdissected mouse embryonic and postnatal mitral valves to characterize the developmental road. We defined the metabolic processes underlying the specification of the progenitors and their contributions to subtypes of valvular cells. Using retrospective multicolor clonal analysis, we describe specific modes of growth and behavior of endocardial cell-derived clones, which build up, in a proper manner, functional valve leaflets. Our data identify how both genetic and metabolic mechanisms specifically drive the fate of a subset of endocardial cells toward their distinct clonal contribution to the formation of the valve.

scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data

BACKGROUND

Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise.

RESULTS

We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function.

CONCLUSIONS

The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website.

scViewer: An Interactive Single-Cell Gene Expression Visualization Tool

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.

Manual cell selection in single cell transcriptomics using scSELpy supports the analysis of immune cell subsets

Introduction

Single cell RNA sequencing plays an increasing and indispensable role in immunological research such as in the field of inflammatory bowel diseases (IBD). Professional pipelines are complex, but tools for the manual selection and further downstream analysis of single cell populations are missing so far.

Methods

We developed a tool called scSELpy, which can easily be integrated into Scanpy-based pipelines, allowing the manual selection of cells on single cell transcriptomic datasets by drawing polygons on various data representations. The tool further supports the downstream analysis of the selected cells and the plotting of results.

Results

Taking advantage of two previously published single cell RNA sequencing datasets we show that this tool is useful for the positive and negative selection of T cell subsets implicated in IBD beyond standard clustering. We further demonstrate the feasibility for subphenotyping T cell subsets and use scSELpy to corroborate earlier conclusions drawn from the dataset. Moreover, we also show its usefulness in the context of T cell receptor sequencing.

Discussion

Collectively, scSELpy is a promising additive tool fulfilling a so far unmet need in the field of single cell transcriptomic analysis that might support future immunological research.

SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data

SEQUIN is a web-based application (app) that allows fast and intuitive analysis of RNA sequencing data derived for model organisms, tissues, and single cells. Integrated app functions enable uploading datasets, quality control, gene set enrichment, data visualization, and differential gene expression analysis. We also developed the iPSC Profiler, a practical gene module scoring tool that helps measure and compare pluripotent and differentiated cell types. Benchmarking to other commercial and non-commercial products underscored several advantages of SEQUIN. Freely available to the public, SEQUIN empowers scientists using interdisciplinary methods to investigate and present transcriptome data firsthand with state-of-the-art statistical methods. Hence, SEQUIN helps democratize and increase the throughput of interrogating biological questions using next-generation sequencing data with single-cell resolution.