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Predicting gene expression using morphological cell responses to nanotopography.

Marie F A Cutiongco¹, Bjørn Sand Jensen², Paul M Reynolds¹

¹Divison of Biomedical Engineering, School of Engineering, University of Glasgow, Glasgow, UK.

Nature Communications

|March 15, 2020

Summary

This summary is machine-generated.

Scientists developed a new platform linking cell shape (morphome) to gene expression changes caused by nanotopography. This tool predicts how materials influence cell behavior and gene activity, advancing regenerative medicine.

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Area of Science:

Biomaterials Science
Cell Biology
Systems Biology

Background:

Predicting cellular responses to environmental cues like nanotopography is difficult due to complex cell behavior.
A lack of informative parameters linking cell morphology to biological function hinders progress.

Purpose of the Study:

To develop a platform relating cell morphology to gene expression changes induced by nanotopography.
To utilize a multivariate dataset of cell morphology parameters (the 'morphome') for predictive modeling.

Main Methods:

Created a Bayesian linear regression model integrating the morphome to predict gene expression.
Applied the model to predict nanotopography-induced gene expression in bone, cartilage, muscle, and fibrous cells.
Validated the model in a complex co-culture microenvironment.

Main Results:

The morphome-based model robustly predicted gene expression changes across multiple cell types.
Successfully predicted nanotopography-induced gene expression in a co-culture system.
Discovered novel effects of nanotopography on single-cell interactions and osteogenic gene expression.

Conclusions:

The morphome provides a powerful predictive tool for understanding cell-material interactions.
This approach reveals new insights into nanotopography's influence on cell behavior and gene expression.
The predictive framework holds potential for designing novel biomaterials and topographies.