ArticleCell systems2023
Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed.
- Tumor microenvironment-mediated resistance to immune checkpoint inhibitors in non-small cell lung cancer: Mechanisms, combination strategies and clinical perspectives (Review).Molecular and clinical oncology · 2026Review
- Enhanced recurrence risk stratification for stages Ⅱ-Ⅲ breast cancer using MRI-based structure habitat imaging.European radiology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- DeSpaST: deconvoluting spatial transcriptomics signals to cell-level resolution using histology images.Briefings in bioinformatics · 2026Article
- SP-printer: reconstruction of tumor stage-specific microenvironments via phenotype-integrated spatial transcriptomics.Journal of translational medicine · 2026Article
- MGCL-ST: multi-view graph contrastive learning for spatial transcriptomics imputation.Genome medicine · 2026Article
- When effective anticancer therapies are, in fact, destabilizing the tumor's Group Phenotypic Composition.NPJ precision oncology · 2026Review
- FOXA1 loss drives basal/squamous de-differentiation of prostate cancer and induces an immunosuppressive tumor microenvironment.Nature communications · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- TUSCAN: Tumor segmentation and classification analysis in spatial transcriptomics.PLoS computational biology · 2026Article
- The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.Nature cell biology · 2026Article
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
- Prognostic integration of tumor microenvironment and parthanatos-related genes in gastric cancer: a machine learning-driven risk model and immune landscape profiling.Frontiers in immunology · 2026Article
- Spatially resolving cancer: from cell states to therapy.Trends in cancer · 2026Review
- Targeting the C3 signaling axis of the complement system: immune microenvironment regulation and emerging therapeutic strategies for glioblastoma.Frontiers in immunology · 2026Review
- Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues.Nature cell biology · 2025Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
5 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.
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