ArticleNature communications2025
Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.
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 Transcriptomics Analysis of Ewing Sarcoma.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications.Biology · 2026Review
- A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Exploring the human brain: spatial transcriptomics challenges and approaches in post-mortem analysis.Brain : a journal of neurology · 2026Review
- Expansion omics: from expansion microscopy to spatial omics.Molecular systems biology · 2026Review
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.Nature communications · 2025Article
- Cell simulation as cell segmentation.Nature methods · 2025Article
- Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
- Advanced omics approaches in liver transplant settings: current applications and future prospectives.Frontiers in immunology · 2025Review
- Article
- Cell Simulation as Cell Segmentation.bioRxiv : the preprint server for biology · 2024Article
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8 authors.
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Abstract
Single-cell spatial transcriptomics can provide subcellular resolution for a deep understanding of molecular mechanisms. However, accurate segmentation and annotation remain a major challenge that limits downstream analysis. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn spatial colocalization patterns. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. To evaluate performance, we benchmark Bering with state-of-the-art methods and observe better cell segmentation accuracies and more detected transcripts across technologies and tissues. To streamline segmentation processes, we construct expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation. These improved capabilities enable Bering to enhance cell annotations for the rapidly expanding field of spatial omics.
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