ArticleNature biotechnology2015
High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.
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 222 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
222 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- Integrative single-cell and spatial transcriptomic approaches to decipher the tumor microenvironment and therapeutic resistance in pancreatic cancer.Cancer biology & therapy · 2026Review
- Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics.Genes & diseases · 2026Article
- Translation efficiency covariation identifies conserved coordination patterns across cell types.Nature biotechnology · 2026Article
- DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data.Patterns (New York, N.Y.) · 2026Article
- Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial coherence in DNA barcode networks.Patterns (New York, N.Y.) · 2025Article
- SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets.Communications biology · 2025Article
- ST-deconv: an accurate deconvolution approach for spatial transcriptome data utilizing self-encoding and contrastive learning.NAR genomics and bioinformatics · 2025Article
- Article
- Spatial transcriptomic applications in orthopedics.Connective tissue research · 2025Review
- SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution.BMC bioinformatics · 2025Article
- Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities.Small methods · 2025Review
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2025Article
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Single-cell and spatial transcriptomics integration: new frontiers in tumor microenvironment and cellular communication.Frontiers in immunology · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Spatial Transcriptomics: Integrating Morphology and Molecular Mechanisms of Kidney Diseases.The American journal of pathology · 2025Review
- Multi-omics integration uncovers key transcriptional regulators in triple-negative breast cancer spatial heterogeneity.Frontiers in genetics · 2025Article
162 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.