ArticleNucleic acids research2023
SPASCER: spatial transcriptomics annotation at single-cell resolution.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Diffusion-based representation integration for foundation models improves spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.Nature communications · 2026Article
- Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2026Article
- Spatial2GWAS: a database for linking spatial transcriptomic regions with GWAS traits.Nucleic acids research · 2026Article
- So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.Nucleic acids research · 2026Article
- scBrainScope: cross-species multidimensional brain atlas.Nucleic acids research · 2026Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics.Journal of translational medicine · 2025Article
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.Genome biology · 2025Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 2025Article
- SOAR elucidates biological insights and empowers drug discovery through spatial transcriptomics.Science advances · 2025Article
- Article
- HISSTA: a human in situ single-cell transcriptome atlas.Bioinformatics (Oxford, England) · 2025Article
- Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics.Nucleic acids research · 2025Article
- Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
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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.