singIST: an integrative method for comparative single-cell trancriptomics between disease models and humans

Abstract

MotivationDisease models serve as fundamental tools in drug discovery and early-stage drug development. However, these models are not a perfect reflection of human disease, and selecting a suitable model can be challenging. Existing computational approaches for molecular validation of pathophysiological resemblance to human conditions at single-cell resolution remain limited. Although quantitative computational methods exist to inform this selection, they are very limited at the single-cell resolution, which can be critical for model selection. Quantifying the resemblance of disease models to the human condition with single-cell technologies in an explainable, integrative, and generalizable manner remains a significant challenge. ResultsWe present singIST, a computational method for comparative single-cell transcriptomics analysis between disease models and human conditions. singIST provides explainable quantitative measures on disease model similarity to human condition at both pathway and cell type levels, highlighting the importance of each gene in the latter. These measures account for orthology, cell type presence in the disease model, cell type and gene importance in human condition, and gene changes in the disease model measured as fold change. This is achieved within a unifying framework that controls for the intrinsic complexities of single-cell data. We tested our method using three well-characterized murine models of moderate-to-severe Atopic Dermatitis, demonstrating its ability to recapitulate established biological knowledge while generating novel hypothesis through pathway-level analysis. Availability and implementationR library at https://github.com/DataScienceRD-Almirall/singIST and implementation code at https://github.com/DataScienceRD-Almirall/singIST_paper_results Author SummaryIn this study, we set out a method to improve how researchers evaluate disease models - tools that are essential for understanding human illness and developing new treatments. A major limitation of current methods is that they overlook the fine-scale differences between disease models and human conditions at the single-cell level. Yet, many diseases are driven by changes that occur in specific cell types. To address this, we developed singIST, a method that allows us to compare disease models to human diseases using single-cell gene expression data. What makes singIST unique is its ability to provide clear, interpretable measures of similarity between a disease model and the human condition, specifically at the level of cell types and groups of functionally related genes. We demonstrated the method using three well-characterized mouse models of atopic dermatitis and showed that singIST could both reflect known disease biology and uncover new insights into disease mechanisms. Further, we developed statistical tests to validate the trained models. Our approach is flexible and broadly applicable, making it a valuable tool for improving the selection and understanding of disease models in biomedical research.