sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens. Single cell genomics data have been transforming our understanding of the diverse cell types, and complex intra- and inter-cellular complex biological processes in disease microenvironments (ME). It can guide the development of novel precise targeted and immunotherapy treatments that can effectively perturb the complex multi-cell signaling interactions within disease ME. However, it remains challenging and nontrivial to conduct complex single cell genomics data analysis tasks, which often consist of multiple complex analysis modules and diverse supportive datasets. Herein, our goal is to facilitate biomedical researchers conducting single cell genomics data-driven studies, by developing a publicly accessible open-source tool, sc2MeNetDrug. It provides a user-friendly graphical interface, encapsulating step-wise analysis modules, diverse supportive datasets, visualization functions, and novel analysis modules for identifying cell-cell communication networks, and predicting effective drugs that can potentially perturb the multi-cell signaling interactions within disease ME. It enables users to interactively conduct comprehensive single cell RNA-seq data analysis tasks.