Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

3.7K
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...
3.7K
RNA-seq03:21

RNA-seq

10.5K
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...
10.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neoadjuvant Danburstotug (IMC-001) therapy in gastric, esophageal, and hepatocellular carcinoma: the NeoChance phase II study.

NPJ precision oncology·2026
Same author

Reply to Hojný et al.: Fusion-driven vs. fusion-negative high-grade endometrial stromal sarcoma.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Dependencies in heterogeneous, lineage plastic patient-derived prostate cancer organoids revealed through integrated single-cell multiomics and CRISPR screening.

bioRxiv : the preprint server for biology·2026
Same author

Not just how much, but how it's done: movement activity bout distributions and everyday cognition in older adults with elevated dementia risk.

European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity·2026
Same author

Multiparametric Classification of Pure-tone Responses Distinguishes Neurons in Inferior Colliculus Subdivisions.

bioRxiv : the preprint server for biology·2026
Same author

Socioeconomic deprivation and reclassification of ischemic stroke risk in patients with atrial fibrillation: A report from UK Biobank.

Heart rhythm·2026

Related Experiment Video

Updated: Oct 3, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

5.1K

Recent advances in spatially resolved transcriptomics: challenges and opportunities.

Jongwon Lee1, Minsu Yoo2, Jungmin Choi3

  • 1Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841; Brain Korea 21 Plus Project for Biomedical Science, Korea University College of Medicine, Seoul 02841, Korea.

BMB Reports
|February 16, 2022
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics technologies map gene expression within tissues. New computational methods integrate these spatial data, overcoming limitations of current technologies for deeper biological insights.

More Related Videos

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
05:45

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

Published on: March 29, 2024

2.7K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

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

Published on: May 3, 2024

3.4K

Related Experiment Videos

Last Updated: Oct 3, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

5.1K
Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
05:45

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

Published on: March 29, 2024

2.7K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

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

Published on: May 3, 2024

3.4K

Area of Science:

  • Biotechnology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides cellular heterogeneity insights but loses spatial context.
  • Loss of spatial information hinders understanding of tissue architecture and intercellular communication.
  • Existing spatial transcriptomic technologies have limitations in resolution or throughput.

Approach:

  • Review of current state-of-the-art spatially resolved transcriptomic technologies.
  • Discussion of imaging-based and capture-based transcriptomic profiling methods.
  • Exploration of computational algorithms and integrative methodologies.

Key Points:

  • Imaging-based methods offer single-molecule resolution but are targeted.
  • Capture-based methods provide genome-wide data at lower resolution (55-100 μm).
  • No single technology captures the complete spatial transcriptome.

Conclusions:

  • Balancing throughput and resolution is crucial for specific biological questions.
  • Computational algorithms are essential to overcome technology-specific limitations.
  • Integrative computational approaches with other data modalities offer a framework for understanding complex tissue organization.