Abstract
Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets. In the field of parasitology, these datasets typically describe the gene expression patterns of a given parasite species at the single-cell level under experimental conditions, in specific hosts or tissues, or at different life cycle stages. However, while this wealth of available data represents a significant resource, analysing these datasets often requires expert computational skills, preventing a considerable proportion of the parasitology community from meaningfully integrating existing single-cell data into their work. Here, we present paraCell, a novel software tool that allows the user to visualize and analyse pre-loaded single-cell data without requiring any programming ability. The source code is free to allow remote installation. On our web server, we demonstrated how to visualize and re-analyse published Plasmodium and Trypanosoma datasets. We have also generated Toxoplasma-mouse and Theileria-cow scRNA-seq datasets to highlight the functionality of paraCell for pathogen-host interaction. The analysis of the data highlights the impact of the host interferon-γ response and gene expression profiles associated with disease susceptibility by these intracellular parasites, respectively.