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

You might also read

Related Articles

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

Sort by
Same author

A titin truncating variant linked to atrial fibrillation increases atrial profibrotic signalling and cholinergic sensitivity.

Cardiovascular research·2026
Same author

The Impact of Short-Format Training on Light Curing Technique in Dental Students and Dental Team Members.

Journal of dental education·2026
Same author

AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.

Cell·2026
Same author

IFNγ blockade in Mycobacterium tuberculosis infected macaques alters the granuloma environment but not bacterial control.

Nature communications·2026
Same author

Interpretable deep generative ensemble learning for single-cell omics with Hydra.

Molecular systems biology·2026
Same author

Orchestrating Spatial Transcriptomics Analysis with Bioconductor.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: May 9, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.5K

Highly Adaptable Analysis Tools for Mapping Spatial Features of Cellular Aggregates in Tissues.

Andrew Sawyer1,2,3, Nick Weingaertner1,3, Ellis Patrick4

  • 1School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.

Current Protocols
|May 5, 2025
PubMed
Summary

New metrics quantify cellular structure in entire tissue lesions, offering simple readouts for spatial analysis of tumors and granulomas. This approach enhances prognostic biomarker discovery and lesion comparison in multiplex imaging studies.

Keywords:
cellular aggregatesgranulomamultiplex imagingspatial analysistumor

More Related Videos

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

9.6K
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
11:00

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

16.9K

Related Experiment Videos

Last Updated: May 9, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.5K
A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

9.6K
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
11:00

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

16.9K

Area of Science:

  • Pathology
  • Computational Biology
  • Biomedical Imaging

Background:

  • Multiplex imaging advances reveal spatial cell organization as key prognostic biomarkers.
  • Current spatial analysis tools often focus on small regions, missing patterns in larger cellular aggregates like tumors.
  • Analyzing entire lesions is crucial for understanding disease progression and cellular distribution.

Purpose of the Study:

  • To develop novel quantitative metrics for analyzing the cellular structure of entire tissue lesions.
  • To provide simple, interpretable readouts for spatial analysis applicable to any lesion size or shape.
  • To enable cross-lesion comparisons and enhance prognostic biomarker discovery.

Main Methods:

  • Development of two novel metrics: Total Cell Preference Index and Immune Cell Preference Index.
  • Quantitative description of cellular structure within entire tissue lesions.
  • Application of open-source QuPath software for image analysis.

Main Results:

  • The Total Cell Preference Index quantifies lesion density changes (central vs. peripheral), indicating necrosis extent.
  • The Immune Cell Preference Index maps immune cell type distribution (central vs. peripheral) across the entire lesion.
  • Both indexes provide single-number readouts for simplified interpretation and visualization.

Conclusions:

  • The novel metrics enable comprehensive spatial analysis of entire tissue lesions, overcoming limitations of traditional methods.
  • This approach simplifies cross-lesion comparisons and is compatible with various multiplex imaging systems.
  • The user-friendly protocol, utilizing open-source software, can be rapidly implemented by researchers.