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Spatial reconstruction of single-cell gene expression data
Open accessAbstract
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.
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“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood35.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [35]
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In the citing paper
“Batch effects were corrected with the FindIntegrationAnchor() and the IntegrateData() function in the reciprocal PCA approach of the Seurat workflow.”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [76]
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In the citing paper
“Critically, such methods can only detect variations that are captured by differences between discrete groups.”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [3]
Experimental — accuracy not yet measuredIn the citing paper
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“even when a cell population partitions into discrete types, hybrid cells—single cells that are a mix of two or more types—may be observed95,97”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“One approach used existing in situ hybridisation maps of spatially restricted genes as a “barcode” to which the complete expression profiles of individual cells can be matched.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“This was applied by two groups to reconstruct expression patterns in zebrafish embryos (Satija et al, 2015), and to the brain of the marine annelid Platynereis dumerilii (Achim et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“Notably, the down-regulation of ribosomal genes and bulk protein synthesis has been long considered as a hallmark of aging 54,55 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
In the citing paper
“Thus, while there may be hallmarks of aging that occur in most cell types, such as mitochondrial dysfunction and loss of proteostasis 1,4,54 , our data argue against the hypothesis that aging induces a single universal molecular program in all cells and tissues 16 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
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Background
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Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Self-citationIntegrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Self-citationSCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
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Self-citationHafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
“To facilitate users applying these methods to their own datasets, our approach is freely available as an open-source R package sctransform (github.com/ChristophH/sctransform), with an accompanying interface to our single-cell R toolkit Seurat [16–18].”
MethodSelf-citation
Metadata
- Authors
- Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Schier, Alexander F
- Regev, Aviv
- Venue
- Nature Biotechnology
- Published
- 2015-04-13
- Volume
- 33
- Issue
- 5
- Pages
- 495-502
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4430369
- OpenAlex
- W2069089843
- Cited by
- 7,055
- References
- 50
- Citations by year
- 2026: 931 · 2025: 1220 · 2024: 1142 · 2023: 987 · 2022: 784 · 2021: 640 · 2020: 510 · 2019: 341 · 2018: 278 · 2017: 126 · 2016: 64 · 2015: 27
- FWCI
- 71.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- edoc (University of Basel) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Jane Coffin Childs Memorial Fund for Medical Research
- National Institutes of Health
- Klarman Cell Observatory, Broad Institute
- National Institute of Mental Health (U01MH105960)
- National Human Genome Research Institute (P50HG006193)
- National Institute of General Medical Sciences (R01GM056211)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial reconstruction, single-cell RNA sequencing, subcellular localization, spatial transcriptomics, zebrafish embryos, spatial patterning, spatial markers, Seurat
- MeSH terms
- Animals, Image Processing, Computer-Assisted, Zebrafish, In Situ Hybridization, Fluorescence, Gene Expression Regulation, Developmental, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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