# singIST: An integrative method for comparative single-cell transcriptomics between disease models and humans

## TL;DR

singIST is a new method that compares single-cell gene activity in disease models to human data, helping researchers understand how well models mimic human diseases.

## Contribution

singIST introduces an explainable framework for comparing single-cell transcriptomics between disease models and human conditions at multiple biological levels.

## Key findings

- singIST identifies which models best mimic human disease at pathway, cell type, and gene levels.
- In atopic dermatitis models, singIST recovers known biology and highlights model-specific divergences.
- T-cell stimulation in hidradenitis suppurativa models improves only pre-existing well-matched pathways.

## Abstract

Disease models are fundamental tools in drug discovery and early-stage drug development, but they only approximate human disease, and selecting a suitable model is challenging. Quantitative computational methods exist to assess molecular resemblance to human conditions, but approaching that work at single-cell resolution, and doing so in an explainable and generalizable way, remain very limited.

We 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 the human reference at the pathway, cell type and gene levels. These measures jointly account for gene orthology, cell type presence in the model, cell type and gene importance in the human condition, and gene level fold changes in the model, within a unifying framework that controls for the intrinsic complexities of single-cell data. We first test singIST in three well-characterized murine models against moderate-to-severe Atopic Dermatitis, showing that it recapitulates established biology while generating new hypotheses. We then apply it to Hidradenitis Suppurativa, comparing in vivo human lesions with ex vivo skin explants with and without CD3/CD28 stimulation, and show that stimulation selectively improves pathways that already recapitulate the human signal. Finally, we perform simulation studies that: (i) unit-test the implementation and behaviour of the algorithm under controlled scenarios and (ii) compare singIST against a naïve baseline based on overlapping differentially expressed genes.

Animal, in vitro and ex vivo models are essential for understanding human disease and developing new treatments, but no model is a perfect copy of the human condition. Many disease mechanisms act in specific cell types, and current approaches do not fully exploit single-cell data to ask: how similar is this model to humans, and in which pathways and cell types? We developed singIST, a method that compares single-cell gene expression from disease models with human data and quantifies how well each model reproduces human disease at the level of pathways, cell types and individual genes. The outputs are designed to be interpretable, highlighting not only which model is closest to humans overall, but also which cell types and genes drive good or poor recapitulation. We first applied singIST to three mouse models with an atopic dermatitis-like phenotype and showed that it recovers known biology while pinpointing where each model diverges from human disease. We then used it in hidradenitis suppurativa, comparing human skin lesions with human skin explants with and without T-cell stimulation, and found that stimulation improves only those pathways that were already well-recapitulated without stimulation. Finally, simulation studies confirmed that the implementation behaves as expected and performs better than a simple baseline. Together, these results show that singIST can help researchers choose and interpret disease models in a more systematic and transparent way.