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

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

Study on the Cytotoxic Effects, Apoptosis Induction, Treatment Resistance, and Inflammation Caused by a Gold(III) Complex Containing 3,4-diaminobenzophenone and 2,3-butanedione Monoxime on the Esophageal Cancer Cell Line KYSE-30.

Current molecular medicine·2026

Same author

Adaptive Cavity-Enabled Crystalline Chirality in Nanocarbon Cages.

Angewandte Chemie (International ed. in English)·2026

Same author

Three-Year Visual Quality Evaluation in Children Using Orthokeratology Lenses: A Retrospective Study With OPD-Scan III Measurements.

Journal of ophthalmology·2026

Same author

Double-Plane Midface Lifting: An Improved and Efficient Approach Through the Lower Blepharoplasty Incision.

The Journal of craniofacial surgery·2026

Same author

Synthesis and guest inclusion for molecular catcher-based structure determination.

Nature protocols·2026

Same author

Dietary calcium and phosphorus levels in lactating Shenxian sows regulate growth performance and bone metabolism in suckling piglets.

Frontiers in veterinary science·2026

Same journal

Glycoform engineering of a mammalian platform to sculpt a humanized recombinant bioscavenger.

Cell systems·2026

Same journal

Targeted genomic editing of human gut Bacteroides species based on CRISPR-associated transposases.

Cell systems·2026

Same journal

Scalable enumeration and sampling of minimal metabolic pathways for organisms and communities.

Cell systems·2026

Same journal

Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens.

Cell systems·2026

Same journal

High-throughput machine learning-aided antibody discovery for cell surface antigens.

Cell systems·2026

Same journal

Quantitative cytokine profiling of primary human macrophages reveals distinct single-cell modes of trained immunity.

Cell systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 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

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Shang Li¹, Qunlun Shen¹, Shihua Zhang²

¹NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

|February 4, 2025

Summary

This summary is machine-generated.

STALocator maps single cells to spatial transcriptomics data, improving spatial organization reconstruction and gene expression analysis for enhanced biological insights.

Keywords:

data integration single-cell RNA sequencing spatial localization spatial transcriptomics supervised auto-encoder

More Related Videos

Use of Single Molecule Fluorescent In Situ Hybridization SM-FISH to Quantify and Localize mRNAs in Murine Oocytes

Use of Single Molecule Fluorescent In Situ Hybridization SM-FISH to Quantify and Localize mRNAs in Murine Oocytes

Published on: April 24, 2019

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

Related Experiment Videos

Last Updated: May 29, 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

Use of Single Molecule Fluorescent In Situ Hybridization SM-FISH to Quantify and Localize mRNAs in Murine Oocytes

Use of Single Molecule Fluorescent In Situ Hybridization SM-FISH to Quantify and Localize mRNAs in Murine Oocytes

Published on: April 24, 2019

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA-sequencing (scRNA-seq) provides high-resolution gene expression but lacks spatial context.
Spatial transcriptomics (ST) offers spatial information but with lower data quality compared to scRNA-seq.
Integrating these techniques is crucial for understanding cellular organization and function.

Purpose of the Study:

To develop STALocator, a novel method for accurately localizing single cells within spatial transcriptomics data.
To enhance the analysis of spatial gene expression patterns and reconstruct cellular organization.
To improve the prediction of genome-wide gene expression from various spatial data types.

Main Methods:

Development of STALocator algorithm for cell-to-ST data localization.
Application and validation using simulated datasets.
Testing on human brain and squamous cell carcinoma datasets (e.g., Slide-seqV2, FISH, Xenium).

Main Results:

STALocator demonstrated superior performance compared to existing localization methods on simulated data.
Successfully reconstructed the spatial organization of key cell populations in human brain and cancer tissues.
Enhanced gene expression patterns and predicted genome-wide expression for FISH and Xenium data.
Identified more spatially variable genes and biologically relevant Gene Ontology (GO) terms.

Conclusions:

STALocator effectively integrates scRNA-seq and ST data, overcoming data quality limitations.
The method enables robust reconstruction of cellular spatial architecture and enhances gene expression analysis.
STALocator facilitates deeper biological discoveries by identifying novel spatial patterns and relationships.