A team from the University of Auckland and Fudan University presents an online resource for analyzing and visualizing single-cell RNA sequencing data. The interactive web server, known as ICARUS, is designed for investigators who may not have programming experience, the researchers note, explaining that the platform offers "logical and stepwise interpretation of scRNA-seq data" without new software installation. At the moment, ICARUS supports scRNA-seq data for nearly a dozen organisms, including humans, domestic and farm animals, and model organisms such as the fruit fly, nematode worm, zebrafish, and yeast. "Compared to other similar tools," the authors report, "ICARUS has flexible functionality and includes additional tools to enable biological interpretation and accommodate the rapidly evolving scRNA-seq research field."
Rutgers Cancer Institute of New Jersey investigators present Neighbor-seq, a computational method for untangling cellular interactions and signaling features by tapping into so-called multiplet features from cells that are not fully dissociated in single-cell RNA sequencing experiments. "We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single-cell sequencing data," they write, adding that the approach "provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics." Along with analyses of spatial transcriptomic and cell-cell interaction features in small intestine, respiratory tract, and spleen samples, the team applied Neighbor-seq to scRNA-seq data for pancreatic and skin cancers.
Researchers in Germany, the UK, the Netherlands, Egypt, and Denmark describe a web-based oncology-focused drug interaction tool called the Cancer Driver Drug Interaction Explorer (CADDIE). The application brings together information from half a dozen gene-gene or drug-gene interaction databases, the team notes, together with cancer-specific data on driver genes, mutation frequencies, gene expression profiles, related genetic conditions, and drugs known for targeting cancer. "The comprehensive visualization in CADDIE allows for intuitive explorative analysis and highlights the interplay between interacting molecules," the authors write, adding that the oncology-centered drug repurposing platform "offers access to a broad set of resources and allows biomedical researchers to suggest and prioritize drug targets and to identify suitable drug repurposing candidates using state-of-the-art network medicine algorithms.