Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry
Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-39895-3 |
| _version_ | 1797784541778149376 |
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| author | Qihuang Zhang Shunzhou Jiang Amelia Schroeder Jian Hu Kejie Li Baohong Zhang David Dai Edward B. Lee Rui Xiao Mingyao Li |
| author_facet | Qihuang Zhang Shunzhou Jiang Amelia Schroeder Jian Hu Kejie Li Baohong Zhang David Dai Edward B. Lee Rui Xiao Mingyao Li |
| author_sort | Qihuang Zhang |
| collection | DOAJ |
| description | Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data. |
| first_indexed | 2024-03-13T00:41:23Z |
| format | Article |
| id | doaj.art-211cff1dc45b4b52ab015e67533383d8 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T00:41:23Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-211cff1dc45b4b52ab015e67533383d82023-07-09T11:17:39ZengNature PortfolioNature Communications2041-17232023-07-0114111910.1038/s41467-023-39895-3Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEryQihuang Zhang0Shunzhou Jiang1Amelia Schroeder2Jian Hu3Kejie Li4Baohong Zhang5David Dai6Edward B. Lee7Rui Xiao8Mingyao Li9Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill UniversityStatistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaStatistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaDepartment of Human Genetics, School of Medicine, Emory UniversityResearch Department, Biogen, Inc.Research Department, Biogen, Inc.Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of PennsylvaniaTranslational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of PennsylvaniaStatistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaStatistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaAbstract Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.https://doi.org/10.1038/s41467-023-39895-3 |
| spellingShingle | Qihuang Zhang Shunzhou Jiang Amelia Schroeder Jian Hu Kejie Li Baohong Zhang David Dai Edward B. Lee Rui Xiao Mingyao Li Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry Nature Communications |
| title | Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry |
| title_full | Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry |
| title_fullStr | Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry |
| title_full_unstemmed | Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry |
| title_short | Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry |
| title_sort | leveraging spatial transcriptomics data to recover cell locations in single cell rna seq with celery |
| url | https://doi.org/10.1038/s41467-023-39895-3 |
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