Work

stTransfer enables transfer of single-cell annotations to spatial transcriptomics with single-cell resolution

Year: 2025

Type: article

Abstract: Spatial transcriptomics (ST) enables in situ analysis of gene expression patterns and spatial microenvironments. However, current ST technologies are limited by detection sensitivity and gene coverage, posing significant challenges for precise cell type annotation at the single-cell level. To address this, we present stTransfer, a method that integrates reference single-cell RNA sequencing (scRNA-seq) data with ST context using a graph autoencoder and transfer learning. This approach minimizes information transfer loss between scRNA-seq and ST datasets. Benchmark analyses on publicly available spatial transcriptomic datasets demonstrate that stTransfer outperforms existing methods in both accuracy and robustness for cell type annotation. Lastly, we apply stTransfer to annotate neuronal populations in a high-precision Stereo-seq dataset of the zebra finch optic tectum.

Source: Cell Reports Methods

DOI: 10.1016/j.crmeth.2025.101205

Authors: Tao Zhou, Lin Xiang, Kuo Liao, Youzhe He, Zhenkun Zhuang, Shiping Liu

Institutions: University of Chinese Academy of Sciences, BGI Research, South China University of Technology

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,824

Funder: Science and Technology Department of Zhejiang Province