ArticleNature methods2023
SCS: cell segmentation for high-resolution spatial transcriptomics.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Article
- SMURF: soft-segmentation for single-cell reconstruction and topological analysis of spatial transcriptomic data.Nature communications · 2026Article
- Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics.Nature genetics · 2026Article
- Review
- Decoding spatial transcriptomics across multicellular and subcellular resolutions.Nature communications · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- StrucGAP: a modular, streamlined and traceable data mining platform for structural and site-specific glycoproteomics.Nature communications · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Exploring the human brain: spatial transcriptomics challenges and approaches in post-mortem analysis.Brain : a journal of neurology · 2026Review
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- Spatial transcriptomics: challenges and future directions in musculoskeletal diseases.Current opinion in rheumatology · 2026Review
- Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.Nature biotechnology · 2026Article
- RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics.Genome biology · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to subcellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present subcellular spatial transcriptomics cell segmentation (SCS), which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new subcellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells and provided more realistic cell size estimation. Subcellular analysis of RNAs using SCS spot assignments provides information on RNA localization and further supports the segmentation results.
Indexed as
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37429992What Socratic holds
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.