ArticleGigaByte (Hong Kong, China)2024
Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images.
Article in GigaByte (Hong Kong, China), 2024. 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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The trial behind it
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Who cites it
10 citing papers in PubMed.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Review
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model.Nature communications · 2026Article
- A molecularly anchored spatial transcriptomic framework for precise CA1-subiculum parcellation and region-resolved analysis in Alzheimer's disease.GigaScience · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Poly pipeline: a polyvalent spatial transcriptomics workflow validated across polyploid and diploid organisms.GigaByte (Hong Kong, China) · 2026Article
- Host microenvironment in potato-Phytophthora infestans interaction revealed by single-cell spatiotemporal transcriptome.Nature plants · 2026Article
- Single-Cell and Spatial Transcriptomic Profiling of Penile Squamous Cell Carcinoma Reveals Dynamics of Tumor Differentiation and Immune Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.BME frontiers · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Availability & Implementation: The source code of STCellbin is available at https://github.com/STOmics/STCellbin.
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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 OpenQuestion graph.