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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.
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Related Experiment Video

Updated: Jun 30, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Progress in single-cell multimodal sequencing and multi-omics data integration.

Xuefei Wang¹, Xinchao Wu¹, Ni Hong¹

¹Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.

Biophysical Reviews

|March 18, 2024

Summary

This summary is machine-generated.

Single-cell multimodal omics technologies profile multiple molecular layers from one cell, overcoming information fragmentation. Integrating this data will build a comprehensive cell atlas for understanding gene regulation and disease.

Keywords:

Data integration Multi-omics Multi-omics data integration Single-cell multimodal omics Single-cell sequencing

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Related Experiment Videos

Last Updated: Jun 30, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

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

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Area of Science:

Single-cell biology
Multi-omics data integration
Genomics and epigenomics

Background:

Single-cell sequencing advances allow dissection of cellular heterogeneity.
Mono-omics approaches provide fragmented information, limiting comprehensive cell state analysis.
Need for integrated approaches to capture complete cellular information.

Purpose of the Study:

To review experimental methods for single-cell multimodal omics.
To summarize computational methods for multi-omics data integration.
To discuss future directions in the field of single-cell multi-omics.

Main Methods:

Overview of experimental techniques for joint profiling of genome, transcriptome, epigenome, and proteome from single cells.
Summary of computational strategies for integrating diverse single-cell omics datasets.
Discussion of data integration challenges and opportunities.

Main Results:

Single-cell multimodal omics enables simultaneous investigation of molecular layers.
Integration of multi-omics data reveals links between genomic/epigenetic changes and gene expression/translation.
Potential to uncover disease pathogenesis mechanisms at single-cell resolution.

Conclusions:

Single-cell multimodal omics technologies offer a holistic view of cellular states.
Data integration is crucial for leveraging the full potential of multi-omics data.
Future integration of public multi-omics data will enable a comprehensive cell atlas.