Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution

Authors

Bai, Y., Guan, X., Xiong, S., Guo, X., Wang, X., Yi, S., Ju, W., Ma, S., Zhou, D., Dong, Q., liu, w.

Abstract

Spatial technologies that jointly profile RNA and additional molecular layers provide a comprehensive view of tissue organization, yet their limited resolution results in measurements that reflect mixtures of multiple cell types. While RNA-based deconvolution methods are well established, incorporating co-profiled non-transcriptomic modalities without compromising cell-type identifiability remains an open challenge, as reliable references are largely restricted to transcriptomic data. Here we present SMODER, an RNA-anchored framework for spatial multi-omics deconvolution that treats transcriptomes as the primary determinant of cell identity while incorporating complementary molecular modalities in a constrained manner. By adopting an asymmetric integration strategy, SMODER preserves RNA-driven cell-type resolution and enables robust estimation of cell-type compositions across spatial locations. Across simulations and diverse spatial multi-omics datasets spanning protein, chromatin accessibility and histone modification assays, SMODER consistently outperforms existing methods in both accuracy and spatial coherence. Beyond deconvolution, SMODER denoises multi-omic signals and enables cell-type-aware analysis of regulatory programs. Application to spatial CUT&Tag--RNA-seq and {RNA--ATAC-seq} data reveals spatially resolved epigenetic repression and cis-regulatory relationships linked to gene expression.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 16 Oct 2026.

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