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Protein Diffusion in the Membrane

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...

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Related Experiment Video

Updated: Sep 9, 2025

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

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Published on: April 9, 2019

Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.

Xuesong Wang^1,2, Yimin Fan^1,2, Yucheng Guo¹

¹BioMap Research, Palo Alto, CA, USA.

Nature Communications

|September 2, 2025

Summary

This summary is machine-generated.

MorphDiff, a new model, simulates cell morphology changes from genetic or drug perturbations. This tool aids in predicting drug mechanisms and bioactivity, accelerating drug discovery.

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Last Updated: Sep 9, 2025

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Area of Science:

Computational Biology
Drug Discovery
Cellular Imaging

Background:

High-throughput image-based profiling is crucial for phenotypic drug discovery.
Predicting mechanisms of action (MOA) and compound bioactivity requires understanding cell morphology changes.
Exploring all chemical and genetic perturbations is infeasible with current methods.

Purpose of the Study:

To develop a computational model for simulating high-fidelity cell morphological responses to perturbations.
To enhance the prediction of mechanisms of action (MOA) and compound bioactivity.
To accelerate phenotypic screening in drug discovery.

Main Methods:

Proposed MorphDiff, a transcriptome-guided latent diffusion model.
Applied the model to three large-scale datasets (two drug, one genetic perturbation).
Evaluated performance on thousands of perturbations, including unseen ones.

Main Results:

MorphDiff accurately predicts cell morphological changes for novel perturbations.
The model significantly improves MOA retrieval accuracy, comparable to ground-truth morphology.
Outperformed baseline methods in MOA identification by 16.9% and 8.0%.

Conclusions:

MorphDiff is a powerful tool for simulating cell morphology under perturbation.
The model accelerates phenotypic screening and enhances MOA identification.
Demonstrates significant potential for advancing drug discovery pipelines.