ArticleNature genetics2026
Impact and correction of segmentation errors in spatial transcriptomics.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- Cell atlas of the developing human meninges reveals a dura-like nature of meningiomas.Nature cell biology · 2026Article
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
- Single-nucleus multimodal spatial transcriptomics reveals spatial colocalization of neoantigen-expressing tumor cells and cognate T cells.Nature biotechnology · 2026Article
- Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.Nature methods · 2026Article
- Review
- Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.bioRxiv : the preprint server for biology · 2026Article
- A new paradigm: 46 structural cell types function as environment-supporting innate immune cells, with endothelial and vascular smooth muscle cells as key prototypes.iNew medicine · 2026Article
- Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart.bioRxiv : the preprint server for biology · 2026Article
- ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.Briefings in bioinformatics · 2026Article
- RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.Nature communications · 2026Article
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Smoothie: efficient inference and integration of spatial co-expression networks from denoised spatial transcriptomics data.Communications biology · 2026Article
- Spatial transcriptomic landscape of invasion patterns in human papillomavirus-associated endocervical adenocarcinoma.Scientific reports · 2026Article
- FishFeats: streamlined quantification of multimodal labeling at the single-cell level in 3D tissues.Bioinformatics (Oxford, England) · 2026Article
- Cell Segmentation as Strategic Decision Making.Research (Washington, D.C.) · 2026Article
- A Transcriptional Map of Human Tonsil Architecture: Beyond the Sum of (Single Cell) Parts.European journal of immunology · 2026Article
- Spatial omics of neuroinflammation: insights across brain diseases.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics aims to elucidate how cells coordinate within tissues by connecting cellular states to their native microenvironments. Imaging-based assays are especially promising, capturing molecular and cellular features at subcellular resolution in three dimensions. Interpretation of such data, however, hinges on accurate cell segmentation. Assigning individual molecules to the correct cells remains challenging. Here we re-analyze data from multiple tissues and platforms to find that segmentation errors currently confound most downstream analysis of cellular state, including differential expression, neighbor influence and ligand-receptor interactions. The extent to which misassigned molecules impact the results can be striking, frequently dominating the results. Thus, we show that matrix factorization of local molecular neighborhoods can effectively identify and isolate such molecular admixtures, thereby reducing their impact on downstream analyses, in a manner analogous to doublet filtering in single-cell RNA sequencing. As the applications of spatial transcriptomics assays become more widespread, accounting for segmentation errors will be important for resolving molecular mechanisms of tissue biology.
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