Statistical prediction of nanoparticle delivery: from culture media to cell

Brown, M Rowan; Hondow, Nicole; Brydson, Rik; Rees, Paul; Brown, Andrew P; Summers, Huw; Brown, Rowan

doi:10.1088/0957-4484/26/15/155101

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Statistical prediction of nanoparticle delivery: from culture media to cell

M Rowan Brown, Nicole Hondow, Rik Brydson, Paul Rees

, Andrew P Brown, Huw Summers

, Rowan Brown

Nanotechnology, Volume: 26, Issue: 15, Start page: 155101

Swansea University Authors: Paul Rees , Huw Summers , Rowan Brown

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DOI (Published version): 10.1088/0957-4484/26/15/155101

Abstract

The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatme...

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Published in:	Nanotechnology
ISSN:	0957-4484 1361-6528
Published:	2015
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa20639

first_indexed	2015-04-14T02:06:58Z
last_indexed	2018-02-09T04:57:23Z
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spelling	2017-07-13T10:00:27.7924586 v2 20639 2015-04-13 Statistical prediction of nanoparticle delivery: from culture media to cell 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false d7db8d42c476dfa69c15ce06d29bd863 0000-0003-3628-2524 Rowan Brown Rowan Brown true false 2015-04-13 EAAS The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatment of various diseases, where judicious surface functionalization is exploited to increase efficacy of administered therapeutic dose. However, quantification of heterogeneity associated with absolute dose of a nanotherapeutic (NP number), how this is trafficked across biological barriers has proven difficult to achieve. The main issue being the quantitative assessment of NP number at the spatial scale of the individual NP, data which is essential for the continued growth and development of the next generation of nanotherapeutics. Recent advances in sample preparation and the imaging fidelity of transmission electron microscopy (TEM) platforms provide information at the required spatial scale, where individual NPs can be individually identified. High spatial resolution however reduces the sample frequency and as a result dynamic biological features or processes become opaque. However, the combination of TEM data with appropriate probabilistic models provide a means to extract biophysical information that imaging alone cannot. Previously, we demonstrated that limited cell sampling via TEM can be statistically coupled to large population flow cytometry measurements to quantify exact NP dose. Here we extended this concept to link TEM measurements of NP agglomerates in cell culture media to that encapsulated within vesicles in human osteosarcoma cells. By construction and validation of a data-driven transfer function, we are able to investigate the dynamic properties of NP agglomeration through endocytosis. In particular, we statistically predict how NP agglomerates may traverse a biological barrier, detailing inter-agglomerate merging events providing the basis for predictive modelling of nanopharmacology. Journal Article Nanotechnology 26 15 155101 0957-4484 1361-6528 23 3 2015 2015-03-23 10.1088/0957-4484/26/15/155101 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University RCUK 2017-07-13T10:00:27.7924586 2015-04-13T08:15:41.5488173 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering M Rowan Brown 1 Nicole Hondow 2 Rik Brydson 3 Paul Rees 0000-0002-7715-6914 4 Andrew P Brown 5 Huw Summers 0000-0002-0898-5612 6 Rowan Brown 0000-0003-3628-2524 7 0020639-614201620058PM.pdf nano_26_15_155101.pdf 2016-06-14T14:00:58.4530000 Output 1263175 application/pdf Version of Record true 2016-06-14T14:00:58.4530000 Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. true
title	Statistical prediction of nanoparticle delivery: from culture media to cell
spellingShingle	Statistical prediction of nanoparticle delivery: from culture media to cell Paul Rees Huw Summers Rowan Brown
title_short	Statistical prediction of nanoparticle delivery: from culture media to cell
title_full	Statistical prediction of nanoparticle delivery: from culture media to cell
title_fullStr	Statistical prediction of nanoparticle delivery: from culture media to cell
title_full_unstemmed	Statistical prediction of nanoparticle delivery: from culture media to cell
title_sort	Statistical prediction of nanoparticle delivery: from culture media to cell
author_id_str_mv	537a2fe031a796a3bde99679ee8c24f5 a61c15e220837ebfa52648c143769427 d7db8d42c476dfa69c15ce06d29bd863
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author	Paul Rees Huw Summers Rowan Brown
author2	M Rowan Brown Nicole Hondow Rik Brydson Paul Rees Andrew P Brown Huw Summers Rowan Brown
format	Journal article
container_title	Nanotechnology
container_volume	26
container_issue	15
container_start_page	155101
publishDate	2015
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description	The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatment of various diseases, where judicious surface functionalization is exploited to increase efficacy of administered therapeutic dose. However, quantification of heterogeneity associated with absolute dose of a nanotherapeutic (NP number), how this is trafficked across biological barriers has proven difficult to achieve. The main issue being the quantitative assessment of NP number at the spatial scale of the individual NP, data which is essential for the continued growth and development of the next generation of nanotherapeutics. Recent advances in sample preparation and the imaging fidelity of transmission electron microscopy (TEM) platforms provide information at the required spatial scale, where individual NPs can be individually identified. High spatial resolution however reduces the sample frequency and as a result dynamic biological features or processes become opaque. However, the combination of TEM data with appropriate probabilistic models provide a means to extract biophysical information that imaging alone cannot. Previously, we demonstrated that limited cell sampling via TEM can be statistically coupled to large population flow cytometry measurements to quantify exact NP dose. Here we extended this concept to link TEM measurements of NP agglomerates in cell culture media to that encapsulated within vesicles in human osteosarcoma cells. By construction and validation of a data-driven transfer function, we are able to investigate the dynamic properties of NP agglomeration through endocytosis. In particular, we statistically predict how NP agglomerates may traverse a biological barrier, detailing inter-agglomerate merging events providing the basis for predictive modelling of nanopharmacology.
published_date	2015-03-23T18:39:46Z
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score	11.04748

Statistical prediction of nanoparticle delivery: from culture media to cell

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