ReviewNucleic acids research2025
A comprehensive review of spatial transcriptomics data alignment and integration.
Review in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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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.
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
23 citing papers in PubMed.
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- CoexpressDeconvolve enables reference-free single-cell-resolution deconvolution from spot-based spatial transcriptomics.iScience · 2026Article
- OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data.PLoS computational biology · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- SINTER3D: continuous 3D reconstruction of spatial transcriptomics via implicit neural representations.Genome biology · 2026Article
- Spatial Immunology in Translation: Linking Immune Organisation to Therapeutic Outcome.Medical sciences (Basel, Switzerland) · 2026Review
- Review
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro.bioRxiv : the preprint server for biology · 2026Article
- STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.bioRxiv : the preprint server for biology · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- The cribriform plate: A dynamic central nervous system-immune hub.The Journal of experimental medicine · 2026Review
- Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data.BMC bioinformatics · 2026Article
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- Multi-Omics Insights into Chronic Rhinosinusitis with Nasal Polyps: A Review of Lipidomics, Proteomics, and Transcriptomics.Journal of inflammation research · 2026Review
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
Abstract
Spatial data acquisition technologies enable high-throughput quantification of molecular expression in tissue sections maintaining spatial context information. However, performing downstream analysis on a whole tissue section requires the alignment and integration of multiple tissue slices. This is a nontrivial task due to tissue heterogeneity and plasticity. Although manual solutions exist, they are time-consuming and require technical expertise. Hence, automated and robust alignment and integration of multiple slices within and across datasets, individuals, and experiments becomes essential. This study aims to (i) present a comprehensive review of methodologies for spatial transcriptomics (ST) data alignment and integration, (ii) explain the problem, its scope and challenges, and (iii) propose a general pipeline. We review 24 tools addressing multi-slice ST alignment and integration, and tackling key challenges through downstream validation. Tools focusing solely on single-slice ST analyses or multi-omics integration are excluded. We categorize these approaches by methodology (statistical mapping, image processing and registration, and graph-based) in accordance with the generalized pipeline. We evaluate their strengths, limitations, and real-world applications based on task scope and their potential to advance biological insights. Despite improved spatial resolution and 3D tissue reconstruction, significant challenges persist in achieving robust alignment and integration across heterogeneous tissue slices.
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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.