Abstract 2485: A Bayesian framework for unraveling disease biology from spatially resolved single-cell omics datasets by combining unbiased approaches with biological priors for cell-based microdomain discovery

Abstract Background: The emergence of single-cell spatial omics platforms generating high-plex proteomic and transcriptomic measurements sets the foundation for accurate downstream biologically interpretable analyses. This includes biased and unbiased approaches for cell typing, cell-cell interactions and the discovery of microdomains, complex multicellular microenvironments, underlying disease progression. Problem: There is a lack of a computational framework for optimizing unbiased approaches with biological priors for unraveling a deeper understanding of disease mechanisms. This requires innovative integrational methods that assign confidence and robustness to data-driven biological hypotheses, e.g., presence of transition cells and other rare cell types, emergent microdomains and spatially modulated network biology. Solution: We present a Bayesian computational approach, SpaceIQ™, that not only elucidates potential novel hypotheses driving disease mechanisms, but also cross-references existing working hypotheses from alternative methods. SpaceIQ is agnostic to imaging platforms with the ability to ingest any combinatorial forms of spatial data (e.g., transmitted light, proteomics and transcriptomics). Results: To illustrate our approach, we compare the results from SpaceIQ to the interpretative analyses presented in He et al. Nat Biotech 2022, a publicly available 960-plex RNA data from CosMx platform on NSCLC samples. We aim to provide additional mechanistic insights underlying Stage II/III progression that can augment the biological annotations reported. We have identified 16 unbiased cell types with probabilistic representation of a priori phenotypes annotated as myeloid, lymphocytes, endothelial, epithelial/tumor, and fibroblast. IFI27, SOX4, MALAT1, TYK2, CD74, HLADRB1, and COL3A1 were among the highly-expressed discriminatory genes among cell types. 28 significant spatial interactions (p<0.1) were found resulting in 7 microdomains. Comparison of microdomains to niche neighborhoods defined in He et al. shows that macrophages in the stroma and TLS play a role in tumor progression. Ligand-Receptor analysis on microdomains identified IL-2 signaling, GPCR ligand binding, TNF activity, and TGF regulation to be significant cell-cell communication channels in NSCLC progression (p<1e-04). Conclusions: We proposed a Bayesian integration framework to further the biological interpretations of the spatially-resolved high-plex single-cell CosMx dataset on NSCLC patients presented in He et al. We’ve found unbiased cell types linked to known cell phenotypes in addition to potentially novel gene signatures. These unbiased cell types formulate microdomains with identified key signaling events that are associated with cancer progression. Citation Format: Brian Falkenstein, Raymond Yan, Shannon Quinn, Akif Burak Tosun, S. Chakra Chennubhotla, Arutha Kulasinghe, Filippo Pullara. A Bayesian framework for unraveling disease biology from spatially resolved single-cell omics datasets by combining unbiased approaches with biological priors for cell-based microdomain discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2485.