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

Forced Transdifferentiation

Forced Transdifferentiation

Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...

You might also read

Related Articles

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

Sort by

Same author

Flagellar Motor Dynamics of <i>E. coli</i> Persisters under High-Load Reveal Impaired Performance and a Divergent Nonequilibrium State.

The journal of physical chemistry. B·2026

Same author

Global Quantitative Dynamics and Early Warning Signals of Hepatocellular Carcinoma: Integrating Theoretical Modeling with Experimental Validation.

The journal of physical chemistry. B·2026

Same author

Driven-Dissipative Quantum Battery with Non-equilibrium Reservoirs.

The journal of physical chemistry letters·2025

Same author

Spatial landscape and flux for exploring protein pattern formation in rod-shaped bacteria.

The Journal of chemical physics·2025

Same author

Engineering Aggregation-Induced Emission Photosensitizers through a Counterion-Modulation Strategy for Enhanced Photodynamic Immunotherapy.

ACS nano·2025

Same author

Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.

JACS Au·2025

Search research articles

Related Experiment Video

Updated: Jan 12, 2026

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

Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field

Chudan Yu^1,2, Chuanbo Liu², Erkang Wang^1,2

¹College of Chemistry, Jilin University, Changchun, Jilin, 130012, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|November 7, 2025

Summary

Researchers developed a new machine learning method to reconstruct cell force fields from imaging data, enabling the study of cell fate transitions and dynamics without direct velocity measurements.

Keywords:

force field reconstruction morphological change transdifferentiation

More Related Videos

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

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

Published on: March 1, 2017

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: Jan 12, 2026

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

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

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

Published on: March 1, 2017

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:

Single-cell biology
Computational biology
Biophysics

Background:

Transcriptomics advanced cell behavior understanding, but quantitative multi-omic models, especially for cell morphology, are lacking.
Cell-specific velocity information is crucial for analyzing cellular dynamics and thermodynamics but is often unavailable.
Analyzing cell fate transitions requires understanding the underlying forces driving morphological changes.

Purpose of the Study:

To develop a novel machine learning approach for reconstructing cellular force fields from sparse single-cell imaging data.
To quantitatively model cell morphology changes during transdifferentiation.
To extend the landscape and flux framework to non-steady-state conditions for dynamic cellular processes.

Main Methods:

Captured snapshots of fibroblast-to-neuron transdifferentiation.
Developed a machine learning method to reconstruct the force field driving morphological changes.
Decomposed the force field into flow flux and potential gradient components, adapting to non-steady-state conditions.

Main Results:

The reconstructed force field accurately captured the cell morphological landscape during fate switching.
The study revealed the influence of noise on cell state transitions.
The developed method provides a quantitative framework for analyzing cell morphology dynamics.

Conclusions:

The novel machine learning approach successfully reconstructs cellular force fields from sparse imaging data.
This method enables the analysis of cell fate transitions and dynamics without direct velocity measurements.
The framework is applicable to various single-cell multi-omic datasets lacking inherent velocity information.