STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

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Publication Details

Journal
Genome biology
Published
2024-10-22
DOI
https://doi.org/10.1186/s13059-024-03421-5
Citations
10
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.34

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STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

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Genome biology
Single-cell and spatial transcriptomics
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STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

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article en
10 citations

Abstract

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

Genome biologyVol. 25(1)
Chinese Academy of Sciences (CN), Beijing Institute of Genomics (CN), National Center for Mathematics and Interdisciplinary Sciences (CN), Sino-Danish Centre for Education and Research (CN), China National Biotechnology (China) (CN), Academy of Mathematics and Systems Science (CN), Institute for Advanced Study (DE), Shanghai Center For Bioinformation Technology (CN), University of Chinese Academy of Sciences (CN), Institute of Zoology (CN)
Innovative Research Group Project of the National Natural Science Foundation of China, National Natural Science Foundation of China, Chinese Academy of Sciences, Beijing Institute of Genomics, Chinese Academy of Sciences
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
2.34
DOIOpen Access PDFOpenAlex
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