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

Phase Contrast and Differential Interference Contrast Microscopy

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...

You might also read

Related Articles

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

Sort by

Same author

Histopathologic Features and Molecular Markers of Encephalocraniocutaneous Lipomatosis (ECCL).

Dermatopathology (Basel, Switzerland)·2025

Same author

T<sub>H</sub>17 cells converted into exT<sub>H</sub>17 cells sustain rheumatoid-like IL-17-independent inflammatory arthritis.

Science immunology·2025

Same author

Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics.

Science (New York, N.Y.)·2025

Same author

A rude awakening.

Nature·2025

Same author

Novel fusion superkine, <i>IL-24S/IL-15</i>, enhances immunotherapy of brain cancer.

Journal for immunotherapy of cancer·2025

Same author

Catalytic Inhibition of KAT6/KAT7 Enhances the Efficacy and Overcomes Primary and Acquired Resistance to Menin Inhibitors in MLL Leukemia.

Cancer discovery·2025

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

Multi-scale and multi-context interpretable mapping of cell states across heterogeneous spatial samples.

Patrick C N Martin¹, Wenqi Wang², Hyobin Kim¹

¹Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA.

Nature Communications

|August 21, 2025

Summary

We developed a new cell mapping strategy using a Linear Assignment Problem (LAP) to compare spatial data, outperforming existing methods for accurate cell analysis across diverse datasets.

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

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

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

Published on: April 30, 2019

Related Experiment Videos

Last Updated: Jun 20, 2026

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

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

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

Published on: April 30, 2019

Area of Science:

Computational Biology
Bioinformatics
Spatial Omics

Background:

Increasing need for methods to align and compare spatial data without visual cues.
Existing methods struggle with capturing complex cellular spatial context.
Spatial omics data presents challenges in cross-sample and cross-technology comparisons.

Purpose of the Study:

To develop an interpretable cell mapping strategy for comparing spatial biological data.
To accurately map cells and their niches across diverse datasets and conditions.
To enable systemic comparison and analysis of heterogeneous spatial data.

Main Methods:

Developed a cell mapping strategy by solving a Linear Assignment Problem (LAP).
Cost computation integrates information from individual cells and their surrounding niches.
Implementation allows for flexible and interpretable spatial data mapping.

Main Results:

The proposed method outperforms existing approaches in capturing cellular spatial context.
Accurate cell mapping achieved across various samples, technologies, and resolutions.
Demonstrated spatiotemporal cell decoupling during development and identified patient-specific subpopulations in In Situ Mass Cytometry (IMC) data.

Conclusions:

The interpretable cell mapping strategy provides a robust framework for spatial data analysis.
Facilitates accurate comparison of heterogeneous spatial omics data.
Offers a flexible tool for researchers to adapt spatial data analysis to specific biological contexts.