ArticleNature communications2025
stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Spatial Omics in High-Grade Gliomas: Mapping Immune-Tumor Niches for Precision Therapy.Cancer medicine · 2026Review
- Integrative spatial multi-omics reveals the tumor ecosystem: From mechanistic insights to therapeutic strategies.iScience · 2026Review
- Cancer Heterogeneity and Cancer Cell Plasticity: Molecular Mechanisms and Precision Therapy.MedComm · 2026Review
- Control of genes by self-organizing multicellular interaction networks.NPJ systems biology and applications · 2026Review
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.Genome medicine · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Research trends of tumor-associated macrophages in colorectal cancer: a bibliometric analysis.International journal of surgery (London, England) · 2026Article
- SDMap: a comprehensive database of spatial drug perturbation maps.Nucleic acids research · 2026Article
- COPD-Lung Cancer Comorbidity: Mechanistic Insights and Precision Oncology Implications.International journal of chronic obstructive pulmonary disease · 2026Review
- Targeting Tumor-Associated Macrophages to Reshape the Immuno-Mechanical Landscape: Molecular Mechanisms and Therapeutic Strategies.International journal of biological sciences · 2026Review
- Vector-guided graph learning for spatial multi-slice multi-omics alignment.Cell reports methods · 2025Article
- stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.
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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 Socratic graph.