Phylospatial optimal transport for single-cell and spatial transcriptomics data integration

The spatial organization of evolving cell populations in tumors influences how they progress and respond to treatment. Spatially resolved transcriptomics data may be used to examine the locations of these diverse cell states in tumor sections. However, these technologies often lack single-cell resolution and instead capture groups of cells in spatial spots, making it difficult to resolve cancer clones. In contrast, dissociated single-cell transcriptomics data are better suited for obtaining cell lineages, but do not capture the locations of the cells in the tissue. Here, we introduce SpOTr, an optimal transport approach that leverages the concordance between the evolutionary tree and the spatial distribution of cancer cells to integrate single-cell and spatial transcriptomics data from tumors. SpOTr combines a spot deconvolution model with Fused Gromov-Wasserstein optimal transport, in which cells that are close in the tree are drawn to nearby spots. In simulations and in real spatial lineage tracing data, SpOTr recovered the spatial locations of clones better than methods that ignore the tree. In a human colorectal liver metastasis, SpOTr mapped copy number clones from dissociated cells to spatial spots, showing that it successfully maps the evolutionary history of tumors in space.