Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images

Summary Spatial proteomic and transcriptomic technologies enable high-throughput phenotyping of cells in situ, enabling quantification of spatial relationships among diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method that considers alternative frames of reference for contextualizing spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells that are consistent across regions. By modeling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and transcriptomics datasets that modeling spatial relationships this way is biologically meaningful and can improve the prediction of patient prognosis in a classification setting.

• Publication year

  2025

• Venue

  Cell Reports Methods

• Publication date

  2025-09-01

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1016/j.crmeth.2025.101175PMID 40957414PMCID 12570319
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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