With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

Motivation Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value. Results In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. Availability Cellinker is available at http://www.rna-society.org/cellinker/. Supplementary information Supplementary data are available at Bioinformatics online.

Tumor necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) and its receptor death receptor 4 (DR4) have been implicated in the development of endothelial dysfunction and atherosclerosis. However, the signaling mechanism mediating DR4 activation and leading to endothelial injury remains unclear. We recently demonstrated that ceramide production via hydrolysis of membrane sphingomyelin by acid sphingomyelinase (ASM) results in membrane raft (MRs) clustering and formation of important redox signaling platforms, which play a crucial role in amplifying redox signaling in endothelial cells leading to endothelial dysfunction. The present study aims to investigate whether TRAIL triggers MR clustering via lysosome fusion and ASM activation, thereby conducting transmembrane redox signaling and changing endothelial function. Using confocal microscopy, we found that TRAIL induced MR clustering and its co-localization with DR4 in coronary arterial endothelial cells (CAECs) isolated from wild-type (Smpd1+/+) mice. Further, TRAIL triggered ASM translocation, ceramide production and NADPH oxidase aggregation in MR clusters in Smpd1+/+ CAECs, whereas these observations were not found in Smpd1−/− CAECs. Moreover, ASM deficiency reduced TRAIL-induced O2−· production in CAECs and abolished TRAIL-induced impairment on endothelium-dependent vasodilation in small resistance arteries. By measuring fluorescence resonance energy transfer (FRET), we found that Lamp-1 (lysosome membrane marker protein) and ganglioside GM1 (MR marker) were trafficking together in Smpd1+/+ CAECs, which was absent in Smpd1−/− CAECs. Consistently, fluorescence imaging of living cells with specific lysosome probes demonstrated that TRAIL-induced lysosome fusion with membrane was also absent in Smpd1−/− CAECs. Taken together, these results suggest that ASM is essential for TRAIL-induced lysosomal trafficking and fusion with membrane and formation of MR redox signaling platforms, which may play an important role in DR4-mediated redox signaling in CAECs and consequent endothelial dysfunction.

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.