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Published on: February 3, 2023
Opportunities and challenges for the application of single-cell RNA sequencing and spatial transcriptomics in
Zhifeng Wang1, Zhihui Hao1, Shuangfeng Xu1
1The First Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, China.
Experimental Neurology
|July 5, 2026
Summary
Single-cell RNA sequencing and spatial transcriptomics reveal dynamic cellular changes and spatial heterogeneity in ischemic stroke (IS). These advanced techniques offer new insights for diagnosing and treating IS, overcoming limitations of traditional methods.
Area of Science:
- Neuroscience
- Genomics
- Biotechnology
Background:
- Ischemic stroke (IS) presents significant morbidity and mortality.
- Traditional bulk sequencing fails to capture cellular heterogeneity in IS pathology.
- Advanced single-cell technologies offer high-resolution insights into IS mechanisms.
Purpose of the Study:
- To review the principles and applications of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in IS research.
- To highlight how scRNA-seq and ST elucidate dynamic cellular evolution and spatial heterogeneity in IS.
- To discuss the clinical translation potential and challenges of these technologies for IS.
Main Methods:
- Systematic review of scRNA-seq and ST principles, platforms, and applications in IS.
- Analysis of breakthroughs in understanding IS cellular dynamics and molecular classification.
- Evaluation of evidence for clinical translation in diagnostics, therapeutics, and classification.
Main Results:
- scRNA-seq and ST reveal dynamic cellular evolution and spatial heterogeneity in IS.
- Key breakthroughs include identifying the LGALS9-CD44 repair axis and optimizing oligodendrocyte precursor cell transplantation.
- Molecular classification of IS has been advanced by these technologies.
Conclusions:
- scRNA-seq and ST are powerful tools for IS research, offering high-resolution insights into cellular dynamics and heterogeneity.
- Clinical translation faces challenges including sample scarcity, technological limitations, and preclinical-to-clinical gaps.
- Future optimization and large-scale human data are crucial for advancing IS diagnosis and treatment.
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