Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics

By Ying Ma, Xiang Zhou. Nature Biotechnology, 2022

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Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer. Analysis of spatial transcriptomics is improved by correlating cell-type compositions across tissue locations.

Subfields: Biomedical Engineering, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c3bda3ecc8832cf211c88068d3709428c06c5c1e

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