biostudies-literatureZhou ZNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (National Science Foundation of China)7930https://www.ebi.ac.uk/biostudies/studies/S-EPMC10692090biostudies-literatureUnknown14(1)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.Nature communicationsSpatial transcriptomics deconvolution at single-cell resolution using Redeconve.PMC106920909215930531991171Ren XZhang ZZhong YZhou ZfalseSpatial transcriptomics deconvolution at single-cell resolution using Redeconve.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.2023-01-01T00:00:00Z2023 Dec2026-05-28T08:57:14.791Z2025-02-19T01:18:34.518ZS-EPMC106920903804076810.1038/s41467-023-43600-9