Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets available. In the field of parasitology, these datasets typically describe the gene expression patterns of a given parasite species under specific experimental conditions, in specific hosts or tissues, or at different life-cycle stages. However, while this wealth of available data represents a significant resource for further research, the analysis of these datasets often requires significant computational skills, preventing a considerable proportion of the parasitology community from meaningfully incorporating existing single-cell data into their work. Here, we present paraCell, a novel software tool that automates the advanced analysis of published single-cell data without requiring any programming ability. On our free web server, we demonstrated how to visualise data, re-analyse published Plasmodium and Trypanosoma datasets, and present novel Toxoplasma-mouse and Theileria-cow atlases to study the impact of IFN-γ and host genetic susceptibility.
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Agboraw et al. (2024) studied this question.
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