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 only in monocyte populations and DIP2A only in NK cells associated with immune response to vaccination.
“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]
The Results section on dispersed cell populations discusses gene expression in zebrafish embryos, not cell type-specific eQTLs in human immune cells. Searches for CD52, DIP2A, monocytes, NK cells, and vaccination found no passage in our copy addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredBatch effects were corrected using FindIntegrationAnchor() and IntegrateData() in the reciprocal PCA approach of the Seurat workflow.
“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]
The Online Methods section on principal components analysis describes PCA using R’s prcomp function, not reciprocal PCA integration. Searches for FindIntegrationAnchor, IntegrateData, and batch-effect correction found no passage in our copy addressing the claimed workflow. Page numbers were not available in the indexed passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredANOVA-based methods for differential expression between groups of cells can only detect variations captured by differences between discrete groups.
“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]
The Online Methods section on embryonic subpopulations describes a likelihood-ratio test for differential expression, not ANOVA. Searches for “ANOVA” and for the claimed limitation on detecting variation between discrete groups found no passage in our copy that addresses that limitation; no page numbers are available. (LLM, conf=0.76)
Experimental — accuracy not yet measuredFactors defining cell identity may be discrete categories, continuous spectra, or combinations of discrete and continuous categories.
“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]
True biological hybrid cells may be observed even when a cell population partitions into discrete types.
“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]
Existing in situ hybridisation maps of spatially restricted genes have been used as a barcode to match complete expression profiles of individual cells.
“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
This atlas-based approach was applied to reconstruct expression patterns in zebrafish embryos and in the brain of Platynereis dumerilii.
“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
Down-regulation of ribosomal genes and bulk protein synthesis has long been considered a hallmark of aging.
“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]
While there may be hallmarks of aging that occur in most cell types, such as mitochondrial dysfunction and loss of proteostasis, the data argue against the hypothesis that aging induces a single universal molecular program in all cells and tissues.
“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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Comprehensive Integration of Single-Cell Data
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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
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Molecular diversity and specializations among the cells of the adult mouse brain
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BackgroundSingle-cell RNA sequencing to explore immune cell heterogeneity
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Resolving the fibrotic niche of human liver cirrhosis at single-cell level
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Single-cell chromatin state analysis with Signac
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Self-citationMapping the Mouse Cell Atlas by Microwell-Seq
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Exploring tissue architecture using spatial transcriptomics
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SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
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Organoid modeling of the tumor immune microenvironment
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Single-cell transcriptional diversity is a hallmark of developmental potential
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Eleven grand challenges in single-cell data science
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Single-cell genome sequencing: current state of the science
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Integrative single-cell analysis
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Self-citationUsing clusterProfiler to characterize multiomics data
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A scalable SCENIC workflow for single-cell gene regulatory network analysis
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“Clusters of cells were visualized using T-distributed stochastic neighbor embedding (t-SNE) in Seurat (Satija et al., 2015).”
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Self-citationCell diversity and network dynamics in photosensitive human brain organoids
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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
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Hepatic stellate cells as key target in liver fibrosis
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Assembly of functionally integrated human forebrain spheroids
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Single-Cell Transcriptome Atlas of Murine Endothelial Cells
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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
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“To identify known immune cell populations in PBMCs, we used the Seurat package to perform unbiased clustering on the 2.7k PBMCs from Zheng et al., following the publicly available Guided Clustering Tutorial 17, 53 .”
MethodDeciphering cell–cell interactions and communication from gene expression
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“As more approaches emerge for spatial-based transcriptomics 99–104 and proteomics 133,134 , future studies and algorithms should include this information.”
BackgroundMuseum of spatial transcriptomics
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“However, the legacy of the prequel era still lives on; current era studies still frequently reference prequel atlases [59–61].”
BackgroundSingle-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma
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Self-citationClustering trees: a visualization for evaluating clusterings at multiple resolutions
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BCG Educates Hematopoietic Stem Cells to Generate Protective Innate Immunity against Tuberculosis
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Pooled CRISPR screening with single-cell transcriptome readout.
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Single-cell spatial reconstruction reveals global division of labour in the mammalian liver
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Construction of a human cell landscape at single-cell level
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Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease
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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
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Self-citationTMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes
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A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · see citing works
Orchestrating single-cell analysis with Bioconductor
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Liver tumour immune microenvironment subtypes and neutrophil heterogeneity
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Giotto: a toolbox for integrative analysis and visualization of spatial expression data
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Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
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Single-cell genomics identifies cell type–specific molecular changes in autism
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Splatter: simulation of single-cell RNA sequencing data
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Multiplexed quantification of proteins and transcripts in single cells
Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · see citing works
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
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A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759 · see citing works
Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma
Sun, Y., Wu, L. L., Zhong, Y., Zhou, K., Hou, Y., Wang, Z., Zhang, Z., Xie, J., Wang, C., Chen, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.041 · Cited by 893 · see citing works
Spatially organized multicellular immune hubs in human colorectal cancer
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Self-citationEfficient integration of heterogeneous single-cell transcriptomes using Scanorama
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A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia
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Self-citationSpecification of tissue-resident macrophages during organogenesis
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A molecular census of arcuate hypothalamus and median eminence cell types
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Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis
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Self-citationAn integrated cell atlas of the lung in health and disease
Sikkema, L., Ramírez-Suástegui, C., Strobl, D. C., Gillett, T. E., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ji, Y., Ansari, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02327-2 · Cited by 856 · see citing works
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MethodSeq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S. M., Love, J. C., & Shalek, A. K. (2017) · Nature Methods · doi:10.1038/nmeth.4179 · Cited by 855 · see citing works
Self-citationAn introduction to spatial transcriptomics for biomedical research
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“Seurat maps cells by expression of ‘landmark genes’, inferring probabilities that a cell could have originated from a tissue location whose landmark genes match its own [98, 99].”
BackgroundA human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797 · see citing works
Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly
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Molecular diversity of midbrain development in mouse, human, and stem cells
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The immune cell landscape in kidneys of patients with lupus nephritis
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Whole-organism lineage tracing by combinatorial and cumulative genome editing
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Self-citationDefining cell types and states with single-cell genomics
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A Python library for probabilistic analysis of single-cell omics data
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Identification of region-specific astrocyte subtypes at single cell resolution
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“As a summary of single-cell cell cycle states, we performed a Uniform Manifold Approximation and Projection (UMAP) dimension reduction based on the expression n=199 known cell cycle genes [obtained from Seurat (Satija et al., 2015) and (Adamson et al.”
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Self-citation
- 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 · 2014: 1 · 2013: 1 · 2012: 2
- 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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