scDOT: optimal transport for mapping senescent cells in spatial transcriptomics.

Autor: Nguyen ND; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 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, USA., Khaliullin T; 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, USA., Jiang P; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA., Hasanaj E; Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 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, USA., Bueno M; Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA., Rahman I; Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA., Pryhuber GS; Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA., Li D; Department of Clinical and Translational Research, University of Rochester Medical Center, Rochester, NY, USA., Ma Q; Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, USA., Finkel T; Aging Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA., Königshoff M; Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA., Eickelberg O; Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 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, 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, USA., Lugo-Martinez J; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. jlugomar@andrew.cmu.edu., Bar-Joseph Z; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. zivbj@andrew.cmu.edu.; Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. zivbj@andrew.cmu.edu.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2024 Nov 08; Vol. 25 (1), pp. 288. Date of Electronic Publication: 2024 Nov 08.
DOI: 10.1186/s13059-024-03426-0
Abstrakt: The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to lung spatial transcriptomics data improves on prior methods and allows the identification of the spatial organization of senescent cells, their neighboring cells and novel genes involved in cell-cell interactions that may be driving senescence.
(© 2024. The Author(s).)
Databáze: MEDLINE
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