ArticleNature communications2025
Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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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.
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Who cites it
18 citing papers in PubMed.
- Advances in single-cell and spatial multi-omics for plant development and stress responses.Plant diversity · 2026Review
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoding the breast cancer microenvironment by spatial multi-omics: from architecture to clinical translation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.Briefings in bioinformatics · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- MANTIS: Analytics toolkit for spatial metabolomics with matching spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Metabolic borders shape immune resistance.Nature cell biology · 2026Article
- The lung microbiome and asthma: from inflammatory phenotype differentiation to microbe-based immunomodulatory strategies.Frontiers in immunology · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.Frontiers in plant science · 2026Review
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Simultaneous profiling of spatial transcriptomics (ST) and spatial metabolomics (SM) on the same or adjacent tissue sections offers a revolutionary approach to decode tissue microenvironment and identify potential therapeutic targets for cancer immunotherapy. Unlike other spatial omics, cross-modal integration of ST and SM data is challenging due to differences in feature distributions of transcript counts and metabolite intensities, and inherent disparities in spatial morphology and resolution. Furthermore, cross-sample integration is essential for capturing spatial consensus and heterogeneous patterns but is often complicated by batch effects. Here, we introduce SpatialMETA, a conditional variational autoencoder (CVAE)-based framework for cross-modal and cross-sample integration of ST and SM data. SpatialMETA employs tailored decoders and loss functions to enhance modality fusion, batch effect correction and biological conservation, enabling interpretable integration of spatially correlated ST-SM patterns and downstream analysis. SpatialMETA identifies immune spatial clusters with distinct metabolic features in cancer, revealing insights that extend beyond the original study. Compared to existing tools, SpatialMETA demonstrates superior reconstruction capability and fused modality representation, accurately capturing ST and SM feature distributions. In summary, SpatialMETA offers a powerful platform for advancing spatial multi-omics research and refining the understanding of metabolic heterogeneity within the tissue microenvironment.
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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.