ReviewNature reviews. Genetics2015
Spatially resolved transcriptomics and beyond.
Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 279 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
279 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Enhancing RNA Capture Efficiency in Spatial Transcriptomics: A Review of Innovative Technologies and Strategies.International journal of molecular sciences · 2025Pooled it
- A systematic review of single-cell RNA sequencing applications and insights in Oryza sativa response to environmental stresses.Molecular biology reports · 2025Pooled it
- Estimation of cell lineages in tumors from spatial transcriptomics data.Nature communications · 2023Trial
- Article
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- ViMST: vision transformer-based dual modality multi-task graph contrastive network for spatial transcriptomics microenvironments investigation.BMC biology · 2026Article
- Spatial Transcriptomics for Dissecting Cellular and Molecular Heterogeneity in the Aging and Diseased Brain.International journal of molecular sciences · 2026Review
- Topological modeling of gene expression in the brain with Huntington's disease reveals selective disruption of co-expression network.Scientific reports · 2026Article
- miRNA-lncRNA Cross-Regulation Landscape in Cancer: From Molecular Mechanisms to Therapeutic and Diagnostic Applications.Cancers · 2026Review
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification.Genomics, proteomics & bioinformatics · 2026Article
- MORPHE: Bridging Image Generation and Spatial Omics for Tissue Synthesis.bioRxiv : the preprint server for biology · 2026Article
- Emerging tools to advance neuroethology in butterflies and moths.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2026Review
- HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
219 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considerable progress in sequencing technologies makes it now possible to study the genomic and transcriptomic landscape of single cells. However, to better understand the complexity of multicellular organisms, we must devise ways to perform high-throughput measurements while preserving spatial information about the tissue context or subcellular localization of analysed nucleic acids. In this Innovation article, we summarize pioneering technologies that enable spatially resolved transcriptomics and discuss how these methods have the potential to extend beyond transcriptomics to encompass spatially resolved genomics, proteomics and possibly other omic disciplines.
Indexed as
Identifiers
25446315What OpenQuestion holds
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.