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

You might also read

Related Articles

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

Sort by

Same author

Metabolomics across scales: from single cells to population studies.

Nature·2026

Same author

In vivo autofluorescence lifetime imaging of spatial metabolic heterogeneities and learning-induced changes in the <i>Drosophila</i> mushroom body.

eLife·2026

Same author

Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.

Communications biology·2026

Same author

Systematic analyses of lipid mobilization by human lipid transfer proteins.

Nature·2026

Same author

Deep Imputation for Skeleton data (DISK) for behavioral science.

Nature methods·2025

Same author

Spatial metabolomics and multiomics integration for breakthroughs in precision medicine for kidney disease.

Nature reviews. Nephrology·2025

Same journal

The 1st Mediterranean Meeting on Flow Cytometry: Forging New Collaborations Across the Mediterranean and Beyond.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Publication Guidelines for Optimized Multiparameter Immunolabeling Panels (OMIPs).

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

Same journal

Refractive Index-Correlated Pseudocoloring for Adaptive Color Fusion in Holotomographic Cytology.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 1, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

PySpacell: A Python Package for Spatial Analysis of Cell Images.

France Rose¹, Luca Rappez^2,3, Sergio H Triana²

¹Computational Bioimaging and Bioinformatics, Institut de biologie de l'Ecolenormalesupérieure (IBENS), Ecolenormalesupérieure, CNRS, INSERM, PSL Université Paris, 75005, Paris, France.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|December 25, 2019

Summary

This summary is machine-generated.

This study introduces a Python toolbox to analyze spatial effects in single-cell microscopy images. It quantifies spatial influences on cell phenotypes and molecular data, crucial for understanding cell interactions.

Keywords:

cell graph imaging microscopy neighborhood matrix single cell spatial analysis spatial autocorrelation spatial dependence spatial heterogeneity statistics

More Related Videos

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Related Experiment Videos

Last Updated: Jan 1, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

Area of Science:

Cell Biology
Bioinformatics
Image Analysis

Background:

Single-cell technologies enable detailed phenotypic and molecular measurements in situ.
Understanding spatial effects of cell-cell interactions and microenvironments is a key challenge.
Spatial information is often lost in downstream data analysis, overlooking cell locations.

Purpose of the Study:

To develop a computational tool for analyzing spatial effects in single-cell data.
To enable testing for and estimating the spatial scale of effects in microscopy images.
To integrate spatial analysis into standard single-cell data workflows.

Main Methods:

Development of a Python module for spatial effect analysis.
Application to light microscopy images of adherent cells.
Compatibility with standard single-cell data formats from image analysis tools.

Main Results:

The toolbox can detect the presence of spatial effects in cell imaging data.
It provides an estimation of the spatial scale of these effects.
The module offers a statistically robust approach for diverse applications.

Conclusions:

The developed toolbox addresses the gap in analyzing spatial information in single-cell studies.
It facilitates a deeper understanding of cell-cell interactions and microenvironmental influences.
The tool is versatile for various microscopy and single-cell omics data.