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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

You might also read

Related Articles

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

Sort by

Same author

Targeting Inflammation by Pioglitazone and its R-Enantiomer Mitigates Pathological Myocardial Remodeling in Murine Hypertrophic Cardiomyopathy.

JACC. Basic to translational science·2026

Same author

IgG4-related disease has a specific intestinal microbiota signature.

EBioMedicine·2026

Same author

Clinical development of tacrolimus-resistant regulatory T cells to enable simultaneous immunosuppression and immune regulation.

Molecular therapy. Advances·2026

Same author

An Assessment of GPT-3.5 and GPT-4.0 Responses to Scoliosis FAQs.

Journal of personalized medicine·2026

Same author

Daratumumab in systemic lupus erythematosus: a single-arm phase 2 trial.

Nature communications·2026

Same author

Stable characteristics of intrapopulation heterogeneity in virus-specific Th1 cells during chronic viral challenge infection.

Frontiers in immunology·2026

Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026

Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026

Same journal

Challenges in predicting chromatin accessibility differences between species.

NAR genomics and bioinformatics·2026

Same journal

Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

NAR genomics and bioinformatics·2026

Same journal

LORA: a polymorphic multi-sample long read assembly pipeline.

NAR genomics and bioinformatics·2026

Same journal

Correction to 'Genome sequence assembly and annotation of <i>MATA</i> and <i>MATB</i> strains of <i>Yarrowia lipolytica'</i>.

NAR genomics and bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

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

Exploring transcription modalities from bimodal, single-cell RNA sequencing data.

Enikő Regényi^1,2, Mir-Farzin Mashreghi¹, Christof Schütte³

¹Systems Rheumatology, German Rheumatism Research Centre Berlin, Virchowweg 12, 10117 Berlin, Germany.

NAR Genomics and Bioinformatics

|December 20, 2024

Summary

This summary is machine-generated.

This study introduces a new elliptical method to analyze bimodal single-cell RNA sequencing data, revealing four distinct gene expression modalities. These modalities help identify genes crucial for distinguishing cell phenotypes beyond traditional methods.

More Related Videos

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Related Experiment Videos

Last Updated: Jun 4, 2025

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

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Bimodal single-cell RNA sequencing (scRNA-seq) data is increasingly used for biological pathway analysis.
Current methods primarily use RNA velocities for phenotypic trajectories, neglecting the shape information in 2D data.

Purpose of the Study:

To develop a novel method for analyzing the shape information in 2D bimodal scRNA-seq data.
To identify new gene expression modalities and their biological interpretations.

Main Methods:

Elliptical parametrization of 2D RNA-seq data.
Derivation of statistics to reveal distinct gene expression modalities.
Application to cell cycle and colorectal cancer datasets.

Main Results:

Identified four distinct gene expression modalities from elliptical parametrization.
Interpreted modalities as indicators of changes in splicing, transcription, or degradation rates.
Discovered genes delineating phenotypes that were missed by differential gene expression analysis (DGEA).

Conclusions:

The new elliptical parametrization method expands the analysis of bimodal scRNA-seq data.
The identified modalities offer a new approach to discover phenotype-defining genes.
Incorporating RNA processing insights enhances regulatory and biomarker discovery analyses.