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Abstract Spatially resolved transcriptomics technologies have significantly enhanced our ability to understand cellular characteristics within tissue contexts. However, they present a trade-off between spatial resolution and transcriptome coverage. This limitation, compounded with analytical tools treating cell type inference and cellular neighbourhood identification as separate processes, hinders a unified understanding of tissue features across scales. Our computational framework, SPARROW, infers cell types and delineates cellular organization patterns as microenvironment zones using an interconnected architecture. SPARROW algorithmically achieves single cell spatial resolution and whole transcriptome coverage by integrating spatially resolved transcriptomics and scRNA-seq data. Using SPARROW, we identified established and novel microenvironment zone-specific ligand-receptor mediated interactions in human tonsils, discoveries that would not be possible using either modality alone. Moreover, SPARROW uncovered novel cell states in the mouse hypothalamus, underscoring the influence of microenvironment zones on cell identities. Lastly, through its common latent spaces that facilitate cross-tissue comparisons, SPARROW revealed distinct inflammation states between different lymph node tissues. Overall, SPARROW integrates cellular gene expression with spatial organization, providing a comprehensive characterization of tissue features across scales and samples.
Zhao et al. (Sat,) studied this question.