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...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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

Extracellular Vesicles: Key Mediators of Bioactive Molecule Transport in Plant-Microbe Interactions.

Molecular plant pathology·2026

Same author

Zwitterionic Gel Electrolyte Stabilized Multivalent Tellurium Redox for High-Energy Lithium Batteries.

Angewandte Chemie (International ed. in English)·2026

Same author

Publisher Correction: Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model.

Nature communications·2026

Same author

Erratum to PGK1-coupled HSP90 stabilizes GSK3β expression to regulate the stemness of breast cancer stem cells.

Cancer biology & medicine·2026

Same author

Interfacial microenvironment engineering in CO<sub>2</sub> electroreduction: mechanisms, advances, and perspectives.

Chemical communications (Cambridge, England)·2026

Same author

Balanced electrochemical reaction kinetics and mass transfer for stable zinc negative electrode.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Unsupervised spatially embedded deep representation of spatial transcriptomics.

Hang Xu¹, Huazhu Fu², Yahui Long¹

¹Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, 138648, Singapore.

Genome Medicine

|January 12, 2024

Summary

SEDR integrates gene expression and spatial data for better tissue analysis. This method improves cell communication mapping and data denoising in spatial transcriptomics research.

Keywords:

Batch integration Gene imputation Spatial clustering Spatial transcriptomics Trajectory inference Variational graph auto-encoder

More Related Videos

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Related Experiment Videos

Last Updated: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics enables gene expression analysis within tissue context.
Understanding tissue heterogeneity and cell communication requires integrating gene expression with spatial data.
Existing methods face challenges in scalability and performance with high-resolution spatial transcriptomics.

Purpose of the Study:

To develop a novel computational framework, SEDR, for optimal integration of transcriptomics and spatial data.
To enhance the dissection of tissue heterogeneity and mapping of inter-cellular communications.
To improve the scalability and performance of spatial transcriptomics data analysis.

Main Methods:

SEDR utilizes a deep autoencoder with masked self-supervised learning for gene expression representation.
A variational graph autoencoder embeds gene expression latent space with spatial information.
The method is evaluated on Visium datasets and high-resolution spatial transcriptomics data.

Main Results:

SEDR demonstrates superior clustering performance on manually annotated datasets compared to existing methods.
The framework exhibits enhanced scalability for high-resolution spatial transcriptomics datasets.
SEDR effectively imputes and denoises gene expression data.

Conclusions:

SEDR provides an effective approach for integrating spatial and transcriptomics data.
The method advances the analysis of tissue heterogeneity and cell communication.
SEDR offers improved performance and scalability for spatial transcriptomics applications.