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Related Experiment Video

Updated: Jan 7, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

Integrated experimental and computational workflows for single-cell transcriptomics in plants.

Jing Wang¹, Shanqiao Zheng², Bojie Lu²

¹BGI Tech Solutions, BGI Genomics, Wuhan, 430075, China.

|January 4, 2026

Summary

This summary is machine-generated.

This study optimizes single-cell transcriptomics workflows for plants, improving data quality and cell type identification. The developed framework provides practical guidance for maize and other plant species.

Keywords:

Plant transcriptomics Single-cell RNA sequencing Workflow optimization

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Last Updated: Jan 7, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

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Published on: April 7, 2023

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Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Plant biology
Genomics
Molecular biology

Background:

Single-cell transcriptomics (scRNA-seq) faces challenges in plant research, including tissue preparation and data quality.
Optimized workflows are crucial for reliable scRNA-seq in plant systems.

Purpose of the Study:

To systematically benchmark and optimize bulk and single-cell transcriptomic workflows in maize.
To establish an integrated, optimized framework for plant scRNA-seq.
To provide practical guidance for future plant scRNA-seq studies.

Main Methods:

Developed an improved bulk RNA-sequencing (RNA-seq) protocol for enhanced consistency.
Compared transcriptomic profiles from protoplasts, fresh nuclei, and frozen nuclei across different tissues.
Leveraged bulk RNA-seq data as a reference for validating scRNA-seq findings.

Main Results:

Achieved high transcriptome integrity and clear clustering resolution for robust plant cell type identification.
Demonstrated comparability of transcriptomic profiles across different input types (protoplasts, nuclei).
Provided complementary biological context using bulk RNA-seq to aid scRNA-seq data interpretation.

Conclusions:

Established optimized experimental and computational workflows for plant single-cell transcriptomics.
Validated input type comparability and addressed limitations of nuclear data for scRNA-seq.
Provided methodological guidance applicable to diverse plant species beyond maize.