ArticleNucleic acids research2022
Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Comparison of Coculture Models of Eukaryotic Cells With Pathogenic and/or Commensal Bacteria for Tissue Engineering.Biotechnology and applied biochemistry · 2025Review
- Spatial Transcriptomics Decodes Breast Cancer Microenvironment Heterogeneity: From Multidimensional Dynamic Profiling to Precision Therapy Blueprint Construction.Biomolecules · 2025Review
- Cross-expression meta-analysis of 695 brain samples reveals coordinated gene expression across spatially adjacent cells.bioRxiv : the preprint server for biology · 2025Article
- A Review of the Application of Spatial Transcriptomics in Neuroscience.Interdisciplinary sciences, computational life sciences · 2024Review
- Cellular neighborhood analysis in spatial omics reveals new tissue domains and cell subtypes.Nature genetics · 2024Article
- Spatial transcriptomics in breast cancer: providing insight into tumor heterogeneity and promoting individualized therapy.Frontiers in immunology · 2024Review
- Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS.Science advances · 2023Article
- EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning.Bioinformatics (Oxford, England) · 2023Article
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9 authors at 1 institution in 1 country.
Funding
Abstract
Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.
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