paraCell: a novel software tool for the interactive analysis and visualization of standard and dual host-parasite...

Area of Science:

• Parasitology
• Computational Biology
• Genomics

Background:

• Single-cell transcriptomic datasets are rapidly increasing in parasitology.
• Analyzing these datasets typically requires advanced computational expertise, limiting accessibility for many researchers.
• Existing data offers valuable insights into parasite biology, host interactions, and disease mechanisms.

Purpose of the Study:

• To develop a user-friendly software tool, paraCell, for visualizing and analyzing single-cell transcriptomic data in parasitology.
• To enable researchers without programming skills to access and interpret complex single-cell datasets.
• To facilitate the study of pathogen-host interactions using single-cell RNA sequencing (scRNA-seq) data.

Main Methods:

• Development of paraCell, a novel, free, and remotely installable software tool.
• Demonstration of paraCell's functionality using published Plasmodium and Trypanosoma datasets.
• Generation and analysis of novel Toxoplasma-mouse and Theileria-cow scRNA-seq datasets to showcase pathogen-host interaction analysis.

Main Results:

• ParaCell allows users to visualize and re-analyze pre-loaded single-cell data without coding.
• The tool successfully processed and visualized published Plasmodium and Trypanosoma datasets.
• Analysis of new datasets revealed host interferon-γ responses and gene expression profiles linked to disease susceptibility for intracellular parasites.

Conclusions:

• ParaCell democratizes the analysis of single-cell transcriptomic data in parasitology.
• The software empowers a broader range of researchers to leverage existing and new scRNA-seq data.
• ParaCell facilitates deeper understanding of parasite biology and host-pathogen dynamics.