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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: Jul 9, 2025

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.

Xiaomeng Wan¹, Jiashun Xiao², Sindy Sing Ting Tam³

¹Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

Nature Communications

|November 29, 2023

Summary

This summary is machine-generated.

SpatialScope enhances spatial transcriptomics (ST) data to achieve single-cell resolution and transcriptome-wide profiling. This unified approach integrates sequencing-based and image-based ST data for deeper biological insights.

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

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Last Updated: Jul 9, 2025

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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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

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) technologies are advancing tissue biology understanding.
Current ST methods have limitations in cellular resolution or transcriptome-wide profiling.

Purpose of the Study:

To present SpatialScope, a unified deep generative model approach for ST data analysis.
To enhance seq-based ST data to single-cell resolution and infer transcriptome-wide expression for image-based ST data.

Main Methods:

Integration of single-cell RNA sequencing (scRNA-seq) reference data with ST data.
Application of deep generative models for data enhancement and inference.
Validation through simulation studies and real-world seq-based and image-based ST data.

Main Results:

SpatialScope successfully enhances seq-based ST data to single-cell resolution.
SpatialScope accurately infers transcriptome-wide expression levels for image-based ST data.
Demonstrated utility in spatial characterization, cellular communication analysis, and identification of spatially distinct gene expression.

Conclusions:

SpatialScope offers a unified solution to overcome limitations in current ST technologies.
Provides transcriptome-wide, single-cell resolution spatial characterization of tissues.
Facilitates advanced downstream analyses for biological discovery.