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Isolation and Transcriptome Analysis of Plant Cell Types
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Why "Where" Matters as Much as "How Much": Single-Cell and Spatial Transcriptomics in Plants.

Kinga Moskal1, Marta Puchta-Jasińska1, Paulina Bolc1

  • 1Plant Breeding and Acclimatization Institute-National Research Institute, Radzików, 05-870 Błonie, Poland.

International Journal of Molecular Sciences
|December 30, 2025
PubMed
Summary

Spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) are revolutionizing plant science by revealing gene expression in its anatomical context. This integration offers unprecedented insights into plant development and stress responses, paving the way for crop improvement.

Keywords:
ambient RNAdoublet detectionnuclear isolationplant single-cell atlasprotoplastssingle-cell RNA sequencingsingle-cell multiomics (RNA + ATAC)single-nucleus RNA sequencingspatial transcriptomics

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Area of Science:

  • Plant biology
  • Genomics
  • Bioinformatics

Background:

  • Plant tissues possess a layered structure where spatial context is crucial for understanding gene expression.
  • Traditional gene expression analysis often overlooks the importance of location, focusing solely on magnitude.
  • Advances in single-cell/nucleus RNA sequencing (scRNA-seq/snRNA-seq) and spatial transcriptomics are transforming plant research.

Purpose of the Study:

  • To review and synthesize recent advances in integrating scRNA-seq/snRNA-seq with spatial transcriptomics in plants.
  • To provide practical guidance on platform selection, sample processing, data integration, and computational analysis.
  • To highlight how these integrated approaches reveal plant developmental trajectories and stress responses for crop engineering.

Main Methods:

  • Comparison of protoplast-based scRNA-seq and snRNA-seq for plant tissue profiling, noting dissociation artifacts and lineage representation.
  • Integration of spatial transcriptomics platforms (e.g., Visium HD, Stereo-seq) and targeted imaging techniques (e.g., smFISH, MERFISH) with plant-specific adaptations.
  • Application of computational workflows for label transfer, deconvolution, spatial embedding, and cell-cell communication analysis.

Main Results:

  • snRNA-seq offers improved representation of recalcitrant lineages and reduced stress signatures compared to scRNA-seq.
  • Integrated spatial and single-cell data reveal cell identities, chromatin accessibility, and spatial niches in plant models like Arabidopsis, soybean, and maize.
  • These analyses uncover joint developmental trajectories and stress responses, linking gene expression to anatomical context.

Conclusions:

  • The integration of spatial transcriptomics and scRNA-seq provides a powerful framework for understanding plant biology at cellular and spatial resolutions.
  • Practical guidance is offered for overcoming technical challenges in sample preparation, data processing, and multi-omics integration.
  • This approach facilitates the transition from descriptive atlases to mechanism-informed engineering of agronomic traits in crops.