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

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

Mapping the Cell-Cell Interactions in Cardiovascular Diseases: A Single-Cell Atlas Perspective.

Korean circulation journal·2026

Same author

Identification of distinct clinical phenotypes and their neurobiological signatures in stress-exposed individuals: A multimodal machine learning approach.

European psychiatry : the journal of the Association of European Psychiatrists·2026

Same author

Universal conditional knockout approach to multiple species, from fish to human induced pluripotent stem cells.

Nucleic acids research·2026

Same author

Analysis of unmapped RNA-seq data from cancer spatial transcriptome toward characterizing cancer microbiome.

Scientific reports·2026

Same author

Dynamic recovery of piriform cortex function and its impact on cognitive processing speed following mild COVID-19.

BMC neurology·2026

Same author

Network-level structural alterations distinguish persistent from remitted post-traumatic stress disorder: a morphometric covariance approach.

European journal of psychotraumatology·2026

Same journal

Cancer immunotherapy with oncolytic myxoma virus.

Advances in cancer research·2026

Same journal

Cancer terminator viruses: Unique conditionally replication competent oncolytic viruses producing therapeutic anti-cancer immunomodulating agents.

Advances in cancer research·2026

Same journal

Advancing clinical translation of oncolytic adenoviruses.

Advances in cancer research·2026

Same journal

Operation oncolysis: Reoviruses in the theater of cancer war.

Advances in cancer research·2026

Same journal

AAV vectors for cancer gene therapy.

Advances in cancer research·2026

Same journal

Vesicular stomatitis virus (VSV) as an oncolytic therapeutic agent for cancer.

Advances in cancer research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 21, 2026

Adapting 3' Rapid Amplification of CDNA Ends to Map Transcripts in Cancer

Adapting 3' Rapid Amplification of CDNA Ends to Map Transcripts in Cancer

Published on: March 28, 2018

Current computational methods for spatial transcriptomics in cancer biology.

Jaewoo Mo¹, Junseong Bae², Jahanzeb Saqib¹

¹School of Systems Biomedical Science, Soongsil University, Dongjak-Gu, Seoul, Republic of Korea.

Advances in Cancer Research

|September 13, 2024

Summary

This summary is machine-generated.

This review covers computational methods for spatial transcriptomics in cancer biology. It highlights how these techniques help understand tumor cell behavior within their tissue microenvironment, crucial for cancer research.

Keywords:

Cell segmentation Deconvolution Niche-gene analysis Spatial transcriptomics Tissue domain analysis Tumor microenvironment

More Related Videos

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

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: Jun 21, 2026

Adapting 3' Rapid Amplification of CDNA Ends to Map Transcripts in Cancer

Adapting 3' Rapid Amplification of CDNA Ends to Map Transcripts in Cancer

Published on: March 28, 2018

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

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:

Computational biology
Cancer research
Genomics

Background:

Multicellular organisms rely on cell-cell interactions within a self-organizing system.
Cancer arises from cellular malfunctions within this complex microenvironment.
Single-cell RNA sequencing provides transcriptome data but loses spatial context.

Purpose of the Study:

To review computational methods for analyzing spatial transcriptomics data in cancer biology.
To bridge the gap between gene expression data and cellular behavior in the tumor microenvironment.
To emphasize the importance of spatial information for understanding cancer development.

Main Methods:

Review of existing computational and statistical methodologies.
Focus on techniques applicable to spatial transcriptomics data.
Integration of gene expression with spatial location information.

Main Results:

Spatial transcriptomics preserves crucial cellular address and neighbor information lost in single-cell dissociation.
Computational methods are essential for interpreting complex spatial transcriptomic data.
These methods enable a deeper understanding of tumor cell interactions and microenvironment.

Conclusions:

Computational approaches are vital for unlocking the potential of spatial transcriptomics in cancer research.
Understanding spatial relationships is key to deciphering cancer's self-organizing principles.
This review provides a foundation for applying advanced computational tools to cancer spatial biology.