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 a...

Full description

Saved in:
Bibliographic Details
Published in:Genome Biology 2024-10, Vol.25 (1), p.278-28, Article 278
Main Authors: Wu, Ying, Zhou, Jia-Yi, Yao, Bofei, Cui, Guanshen, Zhao, Yong-Liang, Gao, Chun-Chun, Yang, Ying, Zhang, Shihua, Yang, Yun-Gui
Format: Article
Language:English
Subjects:
Animals
Cell annotation
Deep Learning
Gene Expression Profiling - methods
Humans
Imputation
Method
Multimodal data integration
Spatial transcriptomics
Transcriptome
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-024-03421-5