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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...

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Related Experiment Video

Updated: Jun 5, 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

A statistical approach for systematic identification of transition cells from scRNA-seq data.

Yuanxin Wang¹, Merve Dede¹, Vakul Mohanty¹

¹Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Cell Reports Methods

|December 7, 2024

Summary

We developed CellTran, a new method using gene expression correlations to find cells transitioning between states. This approach helps understand development and disease by identifying key transition cells and their molecular drivers.

Keywords:

CP: developmental biology CP: systems biology carcinogenesis cell development cell differentiation cell transitions differential equations dynamic systems gene expression correlation gene regulatory network single-cell RNA sequencing statistical analysis

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Identification of Circular RNAs using RNA Sequencing

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Last Updated: Jun 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Molecular Biology
Genomics
Computational Biology

Background:

Understanding cellular state transitions is key for biology and disease research.
Single-cell RNA sequencing (scRNA-seq) provides insights, but current tools focus on expression, not regulatory shifts.

Purpose of the Study:

To present CellTran, a novel statistical method for detecting transition cells from scRNA-seq data.
To identify cells undergoing state changes without needing to resolve gene regulatory networks.

Main Methods:

CellTran utilizes paired-gene expression correlations to identify transition cells.
The method was applied to diverse biological contexts: tissue regeneration, embryonic development, preinvasive lesions, and immune responses.

Main Results:

CellTran successfully identified transition cells across various biological scenarios.
Distinct gene expression profiles of these transition cells were revealed.

Conclusions:

CellTran offers a powerful approach to study cellular state transitions using scRNA-seq.
The findings enhance understanding of molecular mechanisms driving these transitions and aid in identifying therapeutic targets.