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Spatially informed cell-type deconvolution for spatial transcriptomics

Nature Biotechnology volume 40, pages 1349–1359 (2022)Cite this article

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Abstract

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.

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Fig. 1: Schematic overview of CARD.
Fig. 2: Comparison of deconvolution accuracy of different methods in simulations under analysis scenarios 1–5.
Fig. 3: Analyzing MOB data.
Fig. 4: Analyzing the PDAC data.
Fig. 5: Analyzing the hippocampus region in Slide-seqV2 and 10x Visium mouse brain (coronal) data.

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Data availability

This study made use of publicly available datasets. These include the MOB dataset (http://www.spatialtranscriptomicsresearch.org), human PDAC dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672), mouse hippocampus Slide-seqV2 dataset (https://singlecell.broadinstitute.org/single_cell/study/SCP948/robust-decomposition-of-cell-type-mixtures-in-spatial-transcriptomics) and mouse brain (coronal section) 10x Visium dataset (https://www.10xgenomics.com/resources/datasets/). For the scRNA-seq references used in this study, all are publicly available, with details provided in Supplementary Tables 2 and 3.

Code availability

The CARD software package and source code have been deposited at www.xzlab.org/software.html. All scripts used to reproduce all the analyses are also available at the same website.

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Acknowledgements

This study was supported by the National Institutes of Health (NIH) grants R01GM126553, R01HG011883 and R01GM144960 (all to X.Z.).

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Authors and Affiliations

  1. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Ying Ma & Xiang Zhou

  2. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    Xiang Zhou

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  1. Ying Ma
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  2. Xiang Zhou
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Contributions

X.Z. conceived the idea and provided funding support. Y.M. and X.Z. designed the experiments. Y.M. developed the method, implemented the software, performed simulations and analyzed real data. Y.M. and X.Z. wrote the manuscript.

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Correspondence to Xiang Zhou.

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Supplementary Figs. 1–94, Tables 1–6 and Notes 1–9.

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Ma, Y., Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 40, 1349–1359 (2022). https://doi.org/10.1038/s41587-022-01273-7

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