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Lookup NU author(s): Dr Emma BriggsORCiD, Dr Domenico Somma
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Agboraw E, Haese-Hill W, Hentzschel F, Briggs EM, Aghabi D, Heawood A, Harding CR, Shiels B, Crouch K, Somma D, Otto TD
Publication type: Article
Publication status: Published
Journal: Nucleic Acids Research
Year: 2025
Volume: 53
Issue: 4
Online publication date: 20/02/2025
Acceptance date: 03/02/2025
Date deposited: 25/03/2026
ISSN (electronic): 1362-4962
Publisher: Oxford University Press
URL: https://doi.org/10.1093/nar/gkaf091
DOI: 10.1093/nar/gkaf091
Data Access Statement: All the datasets used to demonstrate paraCell functionality can be found as publicly available cell atlases, listed in the following list. Included in full text
PubMed id: 39988320
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