Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ultrastructural changes in cryopreserved tracheal grafts of sprague-dawley rats.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2009

Same author

Facile synthesis of size-tunable micro-octahedra via metal-organic coordination.

Chemical communications (Cambridge, England)·2009

Same author

N-acetyl cysteine and penicillamine induce apoptosis via the ER stress response-signaling pathway.

Molecular carcinogenesis·2009

Same author

Targeting glucosylceramide synthase downregulates expression of the multidrug resistance gene MDR1 and sensitizes breast carcinoma cells to anticancer drugs.

Breast cancer research and treatment·2009

Same author

N-glycosylation of ATF6beta is essential for its proteolytic cleavage and transcriptional repressor function to ATF6alpha.

Journal of cellular biochemistry·2009

Same author

A humanized anti-osteopontin antibody inhibits breast cancer growth and metastasis in vivo.

Cancer immunology, immunotherapy : CII·2009

Search research articles

Related Experiment Video

Updated: Jul 11, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning.

Hao Wang¹, Yu-Nan Lin¹, Shen Yan¹

¹The Innovation Team of Crop Germplasm Resources Preservation and Information, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.

|November 5, 2023

Summary

NRTPredictor accurately predicts rice root cell stages using single-cell RNA sequencing data. This tool identifies key marker genes and aids in understanding stress responses in rice roots.

Keywords:

Cell subpopulations Machine learning Marker genes Rice root tips scRNA-seq

More Related Videos

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

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

Related Experiment Videos

Last Updated: Jul 11, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

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
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) offers insights into rice root cellular heterogeneity.
Accurate cell identity annotation remains a challenge in plant scRNA-seq due to data complexity.

Purpose of the Study:

To develop an accurate and interpretable system for predicting rice root cell stages.
To identify novel biomarker genes associated with cell identity and stress responses.

Main Methods:

Developed NRTPredictor, an ensemble-learning system for cell stage prediction.
Utilized model interpretability to identify 110 marker genes involved in phenylpropanoid biosynthesis.
Integrated scRNA-seq and bulk RNA-seq data to analyze stress responses.

Main Results:

NRTPredictor achieved 98.01% accuracy and 95.45% recall in predicting rice root cell stages.
Identified 110 marker genes, including those in phenylpropanoid biosynthesis, for mapping root expression patterns.
Observed aberrant expression in Epidermis cell subpopulations under flooding, Pi, and salt stresses.

Conclusions:

NRTPredictor is a valuable tool for automated rice root cell stage prediction.
The system aids in deciphering rice root cellular heterogeneity and stress response mechanisms.
A free webserver is available for NRTPredictor: https://www.cgris.net/nrtp.