Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Accurate cell-level cytokine activity characterization is crucial for understanding immune-mediated diseases.
• Existing methods like SCAPE are designed for human data, necessitating a mouse-specific approach.

Purpose of the Study:

• To introduce MouSSE, a novel computational method for estimating cell-level cytokine activity in murine single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) data.
• To provide a robust tool for analyzing cytokine signaling in mouse models of disease.

Main Methods:

• MouSSE utilizes a gene set scoring approach to estimate the activity of 86 distinct cytokines.
• Cytokine-specific gene sets are derived from the Immune Dictionary, and scores are calculated using a modified Variance-adjusted Mahalanobis (VAM) technique.
• The method incorporates positive and negative gene weights for enhanced accuracy.

Main Results:

• MouSSE demonstrated superior performance compared to 10 other cytokine activity estimation methods in external validation datasets.
• Stratified cross-validation using the Immune Dictionary confirmed MouSSE's accuracy and reliability.
• The method effectively estimates cell-level cytokine activity in mouse scRNA-seq and ST data.

Conclusions:

• MouSSE is a highly effective and validated method for cell-level cytokine activity estimation in murine transcriptomics data.
• This tool will advance the study of immune responses and related diseases in mouse models.
• An R package for MouSSE is available for public use.