ArticleNature methods2024
Search and match across spatial omics samples at single-cell resolution.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Accurate and well-powered case-control analysis of spatial molecular data.Nature methods · 2026Article
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- Cardiac immunity and heart repair: Mechanism, challenge, and future direction.Chinese medical journal · 2026Review
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
- The choroid plexus is a source of active renin in the central nervous system.Research square · 2026Article
- Fault-tolerant 3D reconstruction from 2D spatial proteomics sections.bioRxiv : the preprint server for biology · 2026Article
- Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.Nature communications · 2026Article
- SINTER3D: continuous 3D reconstruction of spatial transcriptomics via implicit neural representations.Genome biology · 2026Article
- Tissueformer: extending single-cell foundation models to predict population-level phenotypes.BMC bioinformatics · 2026Article
- Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.Nature genetics · 2026Article
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- Alignment of spatial transcriptomics slices across diseases, platforms and conditions.Genome medicine · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues using STARmap PLUS, RIBOmap and TEMPOmap.Nature protocols · 2026Review
- SMART: spatial multi-omic aggregation using graph neural networks and metric learning.Nature communications · 2026Article
- Network model for alignment, stitching and slice-to-volume 3D reconstruction of large-scale spatially resolved slices.Nature communications · 2026Article
- Multimodal spatial alignment and morphology mapping with MOSAICField.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
- SCALPEL: A pipeline for processing large-scale spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
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7 authors.
Funding
Abstract
Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell-cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions.
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