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Related Experiment Video

Updated: Apr 2, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Topography-aware optimal transport for alignment of spatial omics data.

Francesco Ceccarelli1, Pietro Liò1, Julio Saez-Rodriguez2

  • 1Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.

Cell Reports Methods
|March 31, 2026
PubMed
Summary

We developed TOAST (topography-aware optimal alignment of spatially resolved tissues), a new method for aligning spatial omics data. TOAST improves tissue slice alignment and single-cell data mapping by considering spatial relationships.

Keywords:
CP: computational biologyCP: systems biologyfused Gromov-Wassersteinmultimodal data integrationoptimal transportspatial alignmentspatial proteomicsspatial transcriptomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial omics technologies offer insights into tissue organization.
  • Challenges exist in aligning spatial slices and integrating diverse omics data.

Purpose of the Study:

  • To introduce TOAST (topography-aware optimal alignment of spatially resolved tissues), an optimal transport-based framework.
  • To improve alignment of spatial omics slices and mapping of single-cell to spatial data.

Main Methods:

  • Developed TOAST, extending the fused Gromov-Wasserstein (FGW) objective.
  • Incorporated "spatial coherence" (neighborhood entropy) and "neighborhood consistency" (preserving expression profiles).
  • Utilized optimal transport (OT) with integrated spatial constraints.

Main Results:

  • TOAST consistently outperformed traditional FGW and other OT-based alignment methods.
  • Demonstrated improved alignment of spatially resolved tissue slices.
  • Showcased enhanced mapping between single-cell and spatial omics data.

Conclusions:

  • TOAST provides a principled approach for spatial omics data analysis.
  • The framework enhances biological interpretability of spatial omics data.
  • Facilitates multimodal data integration in spatial omics research.