Predicting gene expression using morphological cell responses to nanotopography
| Autor: | Marie F.A. Cutiongco, Bjørn Sand Jensen, Nikolaj Gadegaard, Paul M. Reynolds |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Cell General Physics and Astronomy Gene Expression Biocompatible Materials Cell Communication 02 engineering and technology Cell morphology Machine Learning Mice Osteogenesis Gene expression Musculoskeletal System/diagnostic imaging Nanotechnology skin and connective tissue diseases lcsh:Science Musculoskeletal System Multidisciplinary 021001 nanoscience & nanotechnology medicine.anatomical_structure Cellular Microenvironment Cell Communication/physiology Osteogenesis/genetics 0210 nano-technology Bioinformatics Science Computational biology Cellular level Biology Nanotechnology/methods Bone and Bones Article General Biochemistry Genetics and Molecular Biology Morphome 03 medical and health sciences medicine Animals Nanotopography Bone and Bones/cytology Computational Biology Bayes Theorem General Chemistry Biocompatible material Coculture Techniques 030104 developmental biology NIH 3T3 Cells Nanoparticles lcsh:Q sense organs Biomaterials - cells |
| Zdroj: | Cutiongco, M 2020, ' Predicting gene expression using morphological cell responses to nanotopography ', Nature Communications, vol. 11, no. 1, 1384, pp. 1384 . https://doi.org/10.1038/s41467-020-15114-1 Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-020-15114-1 |
| Popis: | 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. The surface nanotopography of biomaterials direct cell behavior, but screening for desired effects is inefficient. Here, the authors introduce a platform that enables prediction of nanotopography-induced gene expression changes from changes in cell morphology, including in co-culture environments. |
| Databáze: | OpenAIRE |
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