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

In-situ Hybridization02:31

In-situ Hybridization

9.7K
In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
Types of probes and labels
A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...
9.7K
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

Protocol for generating and culturing high-grade serous ovarian carcinoma organoids from fresh or cryopreserved patient samples.

STAR protocols·2026
Same author

Multi-modal data integration reveals functionally credible predictive biomarkers in ovarian cancer.

Genome medicine·2026
Same author

Updated patient-reported outcomes and the effect of disease progression on health-related quality of life in the PRIMA/ENGOT-OV26/GOG-3012 trial of niraparib first-line maintenance therapy in patients with newly diagnosed advanced ovarian cancer.

Gynecologic oncology·2026
Same author

Identification of monotonically classifying pairs of genes for ordinal disease outcomes.

Bioinformatics advances·2026
Same author

Cross-Platform Comparative Spatial Transcriptomics in High-Grade Serous Carcinoma.

Laboratory investigation; a journal of technical methods and pathology·2026
Same author

FUSE: data-driven functional segmentation of DNA methylation data.

Bioinformatics (Oxford, England)·2026

Related Experiment Video

Updated: Oct 3, 2025

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
10:57

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Published on: August 14, 2018

10.8K

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression

Sanaz Jamalzadeh1, Antti Häkkinen1, Noora Andersson1

  • 1Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|February 16, 2022
PubMed
Summary

QuantISH is an open-source pipeline for automated RNA in situ hybridization (RNA-ISH) image analysis. It accurately quantifies RNA expression in single cells, aiding tumor heterogeneity studies and biomarker discovery.

More Related Videos

RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling
07:01

RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling

Published on: April 11, 2015

12.6K
Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH
06:26

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Published on: September 20, 2020

3.3K

Related Experiment Videos

Last Updated: Oct 3, 2025

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
10:57

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Published on: August 14, 2018

10.8K
RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling
07:01

RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling

Published on: April 11, 2015

12.6K
Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH
06:26

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Published on: September 20, 2020

3.3K

Area of Science:

  • Biotechnology
  • Computational Biology
  • Oncology

Background:

  • RNA in situ hybridization (RNA-ISH) is crucial for spatial transcriptomics, revealing RNA abundance and localization in single cells.
  • Analyzing RNA-ISH data is challenging due to large volumes, necessitating automated methods for tumor heterogeneity and expression localization studies.

Purpose of the Study:

  • To introduce QuantISH, a comprehensive, open-source pipeline for automated RNA-ISH image analysis.
  • To enable precise quantification of marker expression in individual carcinoma, immune, and stromal cells from both chromogenic and fluorescent in situ hybridization images.

Main Methods:

  • Development of a modular RNA-ISH image analysis pipeline (QuantISH).
  • Adaptation of the pipeline for various image types, sample characteristics, and staining protocols.
  • Validation of QuantISH performance on high-grade serous carcinoma (HGSC) images for cell classification and expression quantification.

Main Results:

  • QuantISH demonstrated high precision in cancer cell classification on chromogenic RNA-ISH images.
  • Signal expression quantification by QuantISH aligned with visual assessments.
  • CCNE1 average expression and DDIT3 expression variability were identified as potential biomarkers in HGSC using QuantISH.

Conclusions:

  • QuantISH provides a robust method for quantifying RNA expression levels and variability in carcinoma cells.
  • The pipeline facilitates the broader utilization of RNA-ISH technology for biomarker discovery and understanding tumor biology.