SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics

Background The integration of single-cell RNA sequencing (scRNA-seq) and high-resolution spatial transcriptomics (ST) could improve our understanding of both tissue architecture and cellular heterogeneity simultaneously. The key to accomplishing this goal mainly relies on effectively co-embedding si...

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Vydáno v:	Journal of translational medicine Ročník 23; číslo 1; s. 1188 - 21
Hlavní autoři:	Yuan, Jiaqi, Yu, Junhua, Yi, Qianbei, Ye, Zheng, Xu, Peng, Liu, Wenbin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 29.10.2025 BioMed Central Ltd BMC
Témata:	Anopheles Attention mechanism Biomedical and Life Sciences Biomedicine Cancer microenvironment Conditional variational autoencoder (CVAE) Gene imputation Gene mutations Genes Genetic research Genetic transcription Genomics Health aspects Identification and classification Medicine/Public Health Methods Neural networks Neurons RNA sequencing Single-cell RNA sequencing Spatial transcriptomics Technology application China Attention mechanism Conditional variational autoencoder (CVAE) Spatial transcriptomics Single-cell RNA sequencing Gene imputation
ISSN:	1479-5876, 1479-5876
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Shrnutí:	Background The integration of single-cell RNA sequencing (scRNA-seq) and high-resolution spatial transcriptomics (ST) could improve our understanding of both tissue architecture and cellular heterogeneity simultaneously. The key to accomplishing this goal mainly relies on effectively co-embedding similar cells with consistent representations from the two types of data. Methods In this paper, we construct a conditional variational autoencoder (CVAE) architecture, named SpateCV, to explicitly regularize the embedding alignment of similar cells from scRNA-seq and ST data in a shared latent through a clustering loss. Results Benchmark results across twelve datasets demonstrate that SpateCV achieves superior performance in spatial gene imputation and spatial patterns reconstruction. Critically, SpateCV translates this technical accuracy into biological insight. With the imputed genome-wide expression, our method enables the identification of novel spatially differentially expressed genes, such as the astrocyte marker Hepacam, and facilitates the inference of layer-specific intercellular communication networks, identifying corpus callosum cells as key signaling hubs in the mouse visual cortex. Additionally, SpateCV enables the in silico spatial mapping of neuronal subtypes by integrating spatial context into scRNA-seq data. Conclusion SpateCV provides a robust framework for extracting biological knowledge from multimodal spatial-omics data.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1479-5876 1479-5876
DOI:	10.1186/s12967-025-07245-0