Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

作者：Zixiang Zhou, Yunshan Zhong, Zemin Zhang, Xianwen Ren · 发表于：Nature Communications · 年份：2023 · DOI：10.1038/s41467-023-43600-9 · 被引用次数：76 · 研究领域：Single-cell and spatial transcriptomics、T-cell and B-cell Immunology、Immune cells in cancer

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.