ArticleNature biotechnology2018
Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 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
138 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing.Journal for immunotherapy of cancer · 2025Guideline
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Spatial Transcriptomics for Dissecting Cellular and Molecular Heterogeneity in the Aging and Diseased Brain.International journal of molecular sciences · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Optimal gene panel selection for targeted spatial transcriptomics experiments.Nucleic acids research · 2026Article
- Engineered CRISPR/Cas12a2 Nanoprobe Imaging in Living Cells for Precise Tumor Diagnosis.Small methods · 2026Article
- Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.Bioinformatics (Oxford, England) · 2026Article
- Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro.bioRxiv : the preprint server for biology · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial Transcriptomics of TMJ Reveals a Remodeling Fibroblast-Immune Microenvironment Driving Arthritis Pain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps.Nature communications · 2026Article
- Advancing adoptive T cell therapy in ovarian cancer: barriers, innovations, and emerging platforms.Journal for immunotherapy of cancer · 2026Review
- Changes in neural progenitor lineage composition during astrocytic differentiation of human iPSCs.eLife · 2025Article
- Optimal Gene Panel Selection for Targeted Spatial Transcriptomics Experiments.bioRxiv : the preprint server for biology · 2025Article
- DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.Nature methods · 2025Article
- OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.Cell genomics · 2025Article
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
78 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
5 authors.
Funding
Abstract
How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images.
Identifiers
What 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.