ArticleNature biotechnology2015
Spatial reconstruction of single-cell gene expression data.
Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4,340 papers.
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4,340 citing papers in PubMed.
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- Spatiotemporal development of sparse excitatory neuronal types within the deep mouse cortex.iScience · 2026Article
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- scDGCN: A Graph-Based Deep Clustering Framework for Single-Cell Transcriptomics via Neighborhood-Enhanced Context Representation Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- Single-cell RNA sequencing unravels mosquito biology and host-pathogen interactions.Parasites & vectors · 2026Review
- Rates of Evolution Differ Between Cell Types Identified by Single-Cell RNAseq in Threespine Stickleback.Molecular ecology · 2026Article
- A human single-cell atlas identifies OLR1Nature genetics · 2026Article
- A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.PLoS computational biology · 2026Article
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Spatially resolved chromatin architectures in mammalian brain tissues.Nature methods · 2026Article
4,280 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
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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