Spatial reconstruction of single-cell gene expression data
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures glo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2016
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| Online Access: | http://hdl.handle.net/1721.1/105746 https://orcid.org/0000-0001-8567-2049 |
| _version_ | 1811085372638101504 |
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| author | Satija, Rahul Farrell, Jeffrey A Gennert, David Schier, Alexander F Regev, Aviv |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Satija, Rahul Farrell, Jeffrey A Gennert, David Schier, Alexander F Regev, Aviv |
| author_sort | Satija, Rahul |
| collection | MIT |
| description | Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. |
| first_indexed | 2024-09-23T13:08:32Z |
| format | Article |
| id | mit-1721.1/105746 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:08:32Z |
| publishDate | 2016 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1057462022-10-01T13:16:56Z Spatial reconstruction of single-cell gene expression data Satija, Rahul Farrell, Jeffrey A Gennert, David Schier, Alexander F Regev, Aviv Massachusetts Institute of Technology. Department of Biology Regev, Aviv Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. Howard Hughes Medical Institute Klarman Cell Observatory National Human Genome Research Institute (U.S.) (Centers for Excellence in Genomics Science 1P50HG006193) 2016-12-07T20:58:05Z 2016-12-07T20:58:05Z 2015-04 2014-09 Article http://purl.org/eprint/type/JournalArticle 1087-0156 1546-1696 http://hdl.handle.net/1721.1/105746 Satija, Rahul et al. “Spatial Reconstruction of Single-Cell Gene Expression Data.” Nature Biotechnology 33.5 (2015): 495–502. https://orcid.org/0000-0001-8567-2049 en_US http://dx.doi.org/10.1038/nbt.3192 Nature Biotechnology Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Nature Publishing Group PMC |
| spellingShingle | Satija, Rahul Farrell, Jeffrey A Gennert, David Schier, Alexander F Regev, Aviv Spatial reconstruction of single-cell gene expression data |
| title | Spatial reconstruction of single-cell gene expression data |
| title_full | Spatial reconstruction of single-cell gene expression data |
| title_fullStr | Spatial reconstruction of single-cell gene expression data |
| title_full_unstemmed | Spatial reconstruction of single-cell gene expression data |
| title_short | Spatial reconstruction of single-cell gene expression data |
| title_sort | spatial reconstruction of single cell gene expression data |
| url | http://hdl.handle.net/1721.1/105746 https://orcid.org/0000-0001-8567-2049 |
| work_keys_str_mv | AT satijarahul spatialreconstructionofsinglecellgeneexpressiondata AT farrelljeffreya spatialreconstructionofsinglecellgeneexpressiondata AT gennertdavid spatialreconstructionofsinglecellgeneexpressiondata AT schieralexanderf spatialreconstructionofsinglecellgeneexpressiondata AT regevaviv spatialreconstructionofsinglecellgeneexpressiondata |