Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Autor:	Chen H; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Lee YJ; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Ovando-Ricardez JA; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Rosas L; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Rojas M; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Mora AL; Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA., Bar-Joseph Z; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA., Lugo-Martinez J; Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: jlugomar@andrew.cmu.edu.
Jazyk:	angličtina
Zdroj:	Cell reports methods [Cell Rep Methods] 2024 Oct 21; Vol. 4 (10), pp. 100864. Date of Electronic Publication: 2024 Sep 25.
DOI:	10.1016/j.crmeth.2024.100864
Abstrakt:	Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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