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Modeling adult skeletal stem cell response to laser-machined topographies through deep learning

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.
Deep Learning, Modelling techniques, Stem cells, Topographic effects
0040-8166
MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

MacKay, Benita Scout, Praeger, Matthew, Grant-Jacob, James, Kanczler, Janos, Eason, Robert, Oreffo, Richard and Mills, Benjamin (2020) Modeling adult skeletal stem cell response to laser-machined topographies through deep learning. Tissue and Cell, 67, [101442]. (doi:10.1016/j.tice.2020.101442).

Record type: Article

Abstract

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

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Submitted date: 17 June 2020
Accepted/In Press date: 11 September 2020
e-pub ahead of print date: 15 September 2020
Published date: December 2020
Additional Information: Funding Information: BM was supported by an EPSRC Early Career Fellowship ( EP/N03368X/1 ) Funding Information: Research funding to RO by the Biotechnology and Biological Sciences Research Council ( BB/P017711/1 ) and the UK Regenerative Medicine Platform Acellular / Smart Materials – 3D Architecture ( MR/R015651/1 ) is gratefully acknowledged. Funding Information: The authors would like to acknowledge Julia Wells and Kate White within the Bone and Joint Research Group, for their constant patience and provision of technical expertise. Additional appreciation to EPSRC for funding and the Faculties of Engineering & Physical Sciences and Medicine at the University of Southampton for permission to use all required equipment and materials to complete this project. Funding Information: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P600 GPU used for this research, donated through NVIDIA GPU Grant Program. Publisher Copyright: © 2020 The Authors
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Keywords: Deep Learning, Modelling techniques, Stem cells, Topographic effects
Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Institute for Life Sciences research

Identifiers

Local EPrints ID: 443950
URI: http://eprints.soton.ac.uk/id/eprint/443950
DOI: doi:10.1016/j.tice.2020.101442
ISSN: 0040-8166
PURE UUID: 514bc39a-6286-470b-bb4f-49b9874a5818
ORCID for Benita Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Janos Kanczler: ORCID iD orcid.org/0000-0001-7249-0414
ORCID for Robert Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Richard Oreffo: ORCID iD orcid.org/0000-0001-5995-6726
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 17 Sep 2020 16:41
Last modified: 12 Jul 2024 01:48

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Contributors

Author: Benita Scout MacKay ORCID iD
Author: Matthew Praeger ORCID iD
Author: James Grant-Jacob ORCID iD
Author: Janos Kanczler ORCID iD
Author: Robert Eason ORCID iD
Author: Richard Oreffo ORCID iD
Author: Benjamin Mills ORCID iD

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