Faculty, Staff and Student Publications
Publication Date
3-4-2025
Journal
Bioinformatics
Abstract
MOTIVATION: Immune cells undergo cytokine-driven polarization in response to diverse stimuli, altering their transcriptional profiles and functional states. This dynamic process is central to immune responses in health and diseases, yet a systematic approach to assess cytokine-driven polarization in single-cell RNA sequencing data has been lacking.
RESULTS: To address this gap, we developed single-cell unified polarization assessment (Scupa), the first computational method for comprehensive immune cell polarization assessment. Scupa leverages data from the Immune Dictionary, which characterizes cytokine-driven polarization states across 14 immune cell types. By integrating cell embeddings from the single-cell foundation model Universal Cell Embeddings, Scupa effectively identifies polarized cells across different species and experimental conditions. Applications of Scupa in independent datasets demonstrated its accuracy in classifying polarized cells and further revealed distinct polarization profiles in tumor-infiltrating myeloid cells across cancers. Scupa complements conventional single-cell data analysis by providing new insights into dynamic immune cell states, and holds potential for advancing therapeutic insights, particularly in cytokine-based therapies.
AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/bsml320/Scupa.
Keywords
Single-Cell Analysis, Humans, Cytokines, Sequence Analysis, RNA, Computational Biology, Animals, Software, Neoplasms, Mice
DOI
10.1093/bioinformatics/btaf090
PMID
39999031
PMCID
PMC11893155
PubMedCentral® Posted Date
2-25-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes