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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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Updated: Aug 18, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Integration of scATAC-Seq with scRNA-Seq Data.

Ivan Berest^1,2, Andrea Tangherloni³

¹Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|December 10, 2022

Summary

This study integrates single-cell RNA sequencing (scRNA-Seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) to analyze human fetal progenitors. The findings enhance understanding of cell development and disease onset through multi-omics data integration.

Keywords:

Bioinformatics Integrative analysis Multi-omics scATAC-seq scRNA-seq

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single-cell studies reveal molecular processes in cell development and disease.
Single-cell RNA sequencing (scRNA-Seq) captures gene expression heterogeneity.
Single-cell ATAC-Seq (scATAC-Seq) analyzes chromatin accessibility for epigenetic insights.

Purpose of the Study:

To integrate scRNA-Seq and scATAC-Seq data for a comprehensive analysis.
To study the single-cell transcriptome and chromatin accessibility in human fetal progenitors.
To address challenges in multi-omics data integration in bioinformatics.

Main Methods:

Utilizing scRNA-Seq for transcriptome-wide gene expression analysis at single-cell resolution.
Employing scATAC-Seq to assess chromatin accessibility at the single-cell level.
Developing integrative bioinformatics approaches for multi-omics data fusion.

Main Results:

Successfully combined scRNA-Seq and scATAC-Seq data from human fetal progenitors.
Revealed insights into cell heterogeneity and rare cell populations.
Demonstrated the utility of integrated analysis for understanding developmental processes.

Conclusions:

The integration of scRNA-Seq and scATAC-Seq provides a powerful approach to study cellular heterogeneity.
This integrative analysis deepens our understanding of human fetal progenitor development.
Overcoming multi-omics integration challenges is crucial for advancing single-cell biology.