2026-04-17T17:33:23Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/469062025-12-12T02:01:37Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Elosua-Bayés, Marc author Nieto, Paula author Mereu, Elisabetta author Gut, Ivo Glynne author Heyn, Holger author 2021-03-23T11:10:46Z 2021-03-23T11:10:46Z 2021 Spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections and further fine-mapped normal and neoplastic cell states. Trained on an external single-cell pancreatic tumor references, we further charted the localization of clinical-relevant and tumor-specific immune cell states, an illustrative example of its flexible application spectrum and future potential in digital pathology. Funding: Ministerio de Ciencia, Innovación y Universidades [SAF2017-89109-P, AEI/FEDER to U.E.]; project (BCLLATLAS) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [810287]; Chan Zuckerberg Initiative (in part); Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya; Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III, the Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement; Spanish Ministry of Science and Innovation with funds from the European Regional Development Fund (ERDF) corresponding to the 2014–2020 Smart Growth Operating Program. Funding for open access charge: Ministerio de Ciencia, Innovación y Universidades [SAF2017-89109-P, AEI/FEDER to U.E.] Genètica Expressió gènica Pàncrees -- Càncer SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes