Spatially informed cell-type deconvolution for spatial transcriptomics

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 (CAR...

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Bibliographic Details
Published in:Nature biotechnology 2022-09, Vol.40 (9), p.1349-1359
Main Authors: Ma, Ying, Zhou, Xiang
Format: Article
Language:English
Subjects:
Cancer
Composition
Datasets
Deconvolution
Gene expression
Gene sequencing
Heterogeneity
Localization
Pancreatic cancer
Spatial analysis
Spatial discrimination
Tissues
Transcriptomics
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Summary: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.
ISSN:1087-0156
1546-1696
DOI:10.1038/s41587-022-01273-7