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Revolutionary Tools for Decoding the Complexity of Forest Trees: The Application of Single-Cell and Spatial
Xiaoyu Feng1, Rui Han1, Xiaoyu Tang1
1Jilin Provincial Key Laboratory of Tree and Grass Genetics and Breeding, College of Forestry and Grassland, Jilin Agricultural University, Changchun, China.
Physiologia Plantarum
|July 6, 2026
Summary
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) reveal gene expression in forest trees. These powerful tools overcome limitations of traditional methods, offering new insights into woody plant biology and development.
Area of Science:
- Plant biology
- Genomics
- Molecular biology
Background:
- Cellular heterogeneity is fundamental to development, adaptation, and evolution.
- Understanding gene expression and spatial patterns in plant tissues is crucial.
- Traditional transcriptome sequencing lacks the resolution to detect intercellular heterogeneity.
Purpose of the Study:
- To review the development, workflows, and integration of scRNA-seq and ST.
- To highlight cutting-edge applications of these technologies in woody plants.
- To discuss challenges and future directions for forest tree research.
Main Methods:
- Review of single-cell RNA sequencing (scRNA-seq) technologies.
- Review of spatial transcriptomics (ST) methodologies.
- Integration of scRNA-seq and ST for woody plant research.
Main Results:
- scRNA-seq and ST enable detailed analysis of gene expression at the single-cell level within plant tissues.
- These technologies are applied to study vascular cambium differentiation, abiotic stress responses, and rare cell types in forest trees.
- The integration of scRNA-seq and ST provides unprecedented resolution in woody plant biology.
Conclusions:
- scRNA-seq and ST are revolutionizing forest tree biology by enabling the study of cellular heterogeneity.
- These technologies offer new perspectives for research on woody plants, addressing challenges in gene expression analysis.
- Future research should focus on overcoming current limitations to fully exploit the potential of scRNA-seq and ST in forest tree studies.
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