ArticleCell systems2022
Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain.
Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Estimation of cell lineages in tumors from spatial transcriptomics data.Nature communications · 2023Trial
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.Bioinformatics advances · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Restraint of cancer cell plasticity by spatial homotypic clustering.Cancer cell · 2025Article
- sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies.Genome biology · 2025Article
- SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.Genome biology · 2025Article
- Article
- Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics.Micromachines · 2025Review
- Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.Genes & development · 2025Review
- scGIST: gene panel design for spatial transcriptomics with prioritized gene sets.Genome biology · 2024Article
- Analysis of community connectivity in spatial transcriptomics data.Frontiers in applied mathematics and statistics · 2024Article
- Article
- Technical Advances and Applications of Spatial Transcriptomics.GEN biotechnology · 2023Article
- Important Cells and Factors from Tumor Microenvironment Participated in Perineural Invasion.Cancers · 2023Review
- Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.Cell systems · 2022Article
- A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.Briefings in bioinformatics · 2022Review
- Hippo signaling in cardiac fibroblasts during development, tissue repair, and fibrosis.Current topics in developmental biology · 2022Review
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4 authors.
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Abstract
Single-cell spatial transcriptomics (sc-ST) holds the promise to elucidate architectural aspects of complex tissues. Such analyses require modeling cell types in sc-ST datasets through their integration with single-cell RNA-seq datasets. However, this integration, is nontrivial since the two technologies differ widely in the number of profiled genes, and the datasets often do not share many marker genes for given cell types. We developed a neural network model, spatial transcriptomics cell-types assignment using neural networks (STANN), to overcome these challenges. Analysis of STANN's predicted cell types in mouse olfactory bulb (MOB) sc-ST data delineated MOB architecture beyond its morphological layer-based conventional description. We find that cell-type proportions remain consistent within individual morphological layers but vary significantly between layers. Notably, even within a layer, cellular colocalization patterns and intercellular communication mechanisms show high spatial variations. These observations imply a refinement of major cell types into subtypes characterized by spatially localized gene regulatory networks and receptor-ligand usage.
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