Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Zixiang Zhou, Yunshan Zhong, Zemin Zhang and Xianwen Ren ()
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Zixiang Zhou: Changping Laboratory
Yunshan Zhong: Changping Laboratory
Zemin Zhang: Changping Laboratory
Xianwen Ren: Changping Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Date: 2023
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DOI: 10.1038/s41467-023-43600-9

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