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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: May 11, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Benchmarking plant single cell RNA-sequencing sample processing strategies.

Thomas Eekhout^1,2,3, Lindsy De Veirman^1,2, Jolien De Block^1,2

¹Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.

The EMBO Journal

|May 9, 2026

Summary

This summary is machine-generated.

Accurate plant cell profiling requires optimizing cell enrichment and single-cell transcriptomics (scRNA-seq). This study compared methods, revealing biases in cell purification and scRNA-seq workflows for improved plant single-cell data quality.

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Last Updated: May 11, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Published on: November 11, 2014

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Published on: September 16, 2019

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Plant Biology
Genomics
Cell Biology

Background:

Isolating single plant cells from tissues presents challenges due to selective biases.
Accurate profiling of diverse plant cell types necessitates optimized enrichment and single-cell transcriptomics (scRNA-seq) methodologies.
Understanding plant cell heterogeneity is crucial for advancing plant science.

Purpose of the Study:

To systematically compare protoplast enrichment technologies and scRNA-seq platforms for plant root studies.
To identify and address biases in current cell purification and scRNA-seq workflows.
To provide practical guidance for enhancing data quality in plant single-cell research.

Main Methods:

Comparative analysis of protoplast enrichment: conventional flow cytometry, image-based flow cytometry, and magnetic cell sorting.
Evaluation of scRNA-seq platforms: 10X Genomics Chromium and BD Rhapsody.
Application to Arabidopsis root samples, including single-nucleotide polymorphism analysis for doublet detection.

Main Results:

Image-based flow cytometry enhanced precision with customizable gating.
Magnetic sorting improved processing speed and captured cell size heterogeneity.
Both scRNA-seq platforms captured root cell heterogeneity, but exhibited platform-specific cell type composition differences.
Computational doublet detection misclassified a significant proportion of true single cells.

Conclusions:

Key biases exist in plant cell purification and scRNA-seq workflows.
Image-based flow cytometry and magnetic sorting offer distinct advantages for cell enrichment.
Platform choice influences scRNA-seq results, highlighting the need for careful consideration.
Methodological improvements are essential for accurate plant single-cell studies.