Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics.

Area of Science:

• Plant molecular biology
• Developmental biology
• Genomics

Background:

• Single-cell and single-nucleus RNA sequencing (scRNA-seq) provide high-resolution gene expression data in plants.
• These technologies are increasingly used to study transcriptional dynamics in various biological contexts.
• Standardized protocols for plant scRNA-seq are currently lacking.

Purpose of the Study:

• To discuss common challenges in plant single-cell transcriptomics.
• To propose guidelines for improving data quality and reproducibility.
• To facilitate data sharing and interpretation in the plant science community.

Main Methods:

• Review of current practices and challenges in plant scRNA-seq.
• Identification of key areas for standardization.
• Development of general guidelines for experimental design and data analysis.

Main Results:

• Identified common technical and analytical hurdles in plant scRNA-seq.
• Outlined strategies to enhance data quality, reproducibility, and comparability.
• Emphasized the need for community-driven standards and data accessibility.

Conclusions:

• Standardized approaches are crucial for advancing plant single-cell transcriptomics.
• Adherence to proposed guidelines will improve the reliability and impact of scRNA-seq studies in plants.
• Enhanced data sharing and interpretation will accelerate discoveries in plant biology.