ArticleNature methods2026
Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Fishing with two lines: a hybrid approach to spatial transcriptomics discovery.Life science alliance · 2026Article
- spaGFM is a scalable graph foundation model for spatial transcriptomics analyses.Research square · 2026Article
- Kintsugi decides, gene by gene, where spatial transcriptomics borrows information.bioRxiv : the preprint server for biology · 2026Article
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- Anti-amyloid immunotherapy drives APOE4 specific increases in glial reactivity, perivascular immune activation, and ARIA-like events.bioRxiv : the preprint server for biology · 2026Article
- Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.bioRxiv : the preprint server for biology · 2026Article
- Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications.Biology · 2026Review
- Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.bioRxiv : the preprint server for biology · 2026Article
- Insights into the maternal-fetal interface from spatial multi-omics.Frontiers in cell and developmental biology · 2026Review
- Denoising image-based spatial transcriptomics data with DenoIST.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Spatial transcriptomics enables high-resolution gene expression mapping in intact tissues. Xenium is widely adopted for its reliability, accessibility and data quality, yet the properties and limitations of Xenium-derived data remain poorly characterized. Here we present one of the most comprehensive Xenium datasets so far, encompassing over 40 breast and lung tumor sections profiled using diverse gene panels. Leveraging this resource, we systematically dissect technical noise-including transcript spillover-along with assay specificity, panel performance and segmentation strategies. We demonstrate that single-nucleus RNA sequencing enables precise quantification of transcript contamination. Building on these insights, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a method that improves signal purity by resolving mixed transcriptomic signals. SPLIT enhances background correction and cell-type resolution and enables the revelation of T-cell exhaustion signatures associated with malignant cell colocalization-signals that would otherwise remain obscured. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement.
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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.