ReviewCommunications biology2020
From whole-mount to single-cell spatial assessment of gene expression in 3D.
Review in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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
75 citing papers in PubMed.
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Application of spatial transcriptomics across organoids for a high-resolution, spatial whole-transcriptome benchmarking dataset.iScience · 2026Article
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Spatial transcriptomics: challenges and future directions in musculoskeletal diseases.Current opinion in rheumatology · 2026Review
- Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy.Nature biotechnology · 2026Article
- Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain.bioRxiv : the preprint server for biology · 2026Article
- 3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network.bioRxiv : the preprint server for biology · 2026Article
- Wavelength Selection for Periodic Travelling Waves: An Unsolved Problem.Bulletin of mathematical biology · 2026Article
- 3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network.Briefings in bioinformatics · 2026Article
- Article
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics.Genome biology · 2025Article
- Reg2ST: recognizing potential patterns from gene expression for spatial transcriptomics prediction.Briefings in bioinformatics · 2025Article
- DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.Genome biology · 2025Article
- Spatial Transcriptomics in Human Cardiac Tissue.International journal of molecular sciences · 2025Review
- Application of Spatial Omics in the Cardiovascular System.Research (Washington, D.C.) · 2025Review
- Article
- Fhl1, a new spatially specific protein, regulates vein graft neointimal hyperplasia.Clinical and translational medicine · 2024Article
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
15 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.
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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.