Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data
Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noise, batch effects, and irrelevant biological signals. To address this, we introduce Deep scSTAR (DscSTAR), a deep learning-based tool designed to enhance phenotype-associated features. DscSTAR identified HSP+ FKBP4+ T cells in CD8+ T cells, which linked to immune dysfunction and resistance to immune checkpoint blockade in non-small cell lung cancer. It has also enhanced spatial transcriptomics analysis of renal cell carcinoma, revealing interactions between cancer cells, CD8+ T cells, and tumor-associated macrophages that may promote immune suppression and affect outcomes. In hepatocellular carcinoma, it highlighted the role of S100A12+ neutrophils and cancer-associated fibroblasts in forming tumor immune barriers and potentially contributing to immunotherapy resistance. These findings demonstrate DscSTAR's capacity to model and extract phenotype-specific information, advancing our understanding of disease mechanisms and therapy resistance.
Authors
- Lianchong Gao
- Xinran Zhao (ORCID: https://orcid.org/0000-0003-0107-6077)
- Henry H. Y. Tong (ORCID: https://orcid.org/0000-0003-2687-741X)
- Zheqi Liu (ORCID: https://orcid.org/0000-0003-1587-9698)
- Fulan Deng
- Lei Chen (ORCID: https://orcid.org/0000-0002-8661-9036)
- Yujun Liu (ORCID: https://orcid.org/0000-0003-4584-6870)
- Zhen Zhang (ORCID: https://orcid.org/0000-0001-9401-7230)
- Xin Zou (ORCID: https://orcid.org/0000-0003-3695-0465)
- Jiawei Zou (ORCID: https://orcid.org/0000-0003-1549-0340)
- Yuan Ji (ORCID: https://orcid.org/0000-0002-2631-9082)
- Jie Hao (ORCID: https://orcid.org/0000-0002-0273-8047)
- Huangying Le
Institutions
- Sun Yat-sen University (CN)
- Shanghai Jiao Tong University (CN)
- Chinese Academy of Sciences (CN)
- Fudan University (CN)
- Renji Hospital (CN)
- Cancer Institute (WIA) (IN)
- Fudan University Shanghai Cancer Center (CN)
- Shanghai Ninth People's Hospital (CN)
- Center for Excellence in Molecular Cell Science (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Shanghai Chenshan Plant Science Research Center (CN)
- University of Chinese Academy of Sciences (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Briefings in Bioinformatics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1093/bib/bbaf160
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University
- Fundamental Research Funds for the Central Universities