Predicting gene expression using morphological cell responses to nanotopography.
Animals
Bayes Theorem
Biocompatible Materials
Bone and Bones
/ cytology
Cell Communication
/ physiology
Cellular Microenvironment
Coculture Techniques
Computational Biology
Gene Expression
Machine Learning
Mice
Musculoskeletal System
/ diagnostic imaging
NIH 3T3 Cells
Nanoparticles
Nanotechnology
/ methods
Osteogenesis
/ genetics
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
13 03 2020
13 03 2020
Historique:
received:
22
05
2019
accepted:
06
02
2020
entrez:
15
3
2020
pubmed:
15
3
2020
medline:
14
7
2020
Statut:
epublish
Résumé
Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two. A key problem is the lack of informative representations of parameters that translate directly into biological function. Here we present a platform to relate the effects of cell morphology to gene expression induced by nanotopography. This platform utilizes the 'morphome', a multivariate dataset of cell morphology parameters. We create a Bayesian linear regression model that uses the morphome to robustly predict changes in bone, cartilage, muscle and fibrous gene expression induced by nanotopography. Furthermore, through this model we effectively predict nanotopography-induced gene expression from a complex co-culture microenvironment. The information from the morphome uncovers previously unknown effects of nanotopography on altering cell-cell interaction and osteogenic gene expression at the single cell level. The predictive relationship between morphology and gene expression arising from cell-material interaction shows promise for exploration of new topographies.
Identifiants
pubmed: 32170111
doi: 10.1038/s41467-020-15114-1
pii: 10.1038/s41467-020-15114-1
pmc: PMC7070086
doi:
Substances chimiques
Biocompatible Materials
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1384Références
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