Automated force volume image processing for biological samples.

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves...

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Autores principales:	Pavel Polyakov, Charles Soussen, Junbo Duan, Jérôme F L Duval, David Brie, Grégory Francius
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7fe4cf4f73a3440fae80b1cb664c3b0d
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spelling	oai:doaj.org-article:7fe4cf4f73a3440fae80b1cb664c3b0d2021-11-18T06:54:40ZAutomated force volume image processing for biological samples.1932-620310.1371/journal.pone.0018887https://doaj.org/article/7fe4cf4f73a3440fae80b1cb664c3b0d2011-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21559483/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.Pavel PolyakovCharles SoussenJunbo DuanJérôme F L DuvalDavid BrieGrégory FranciusPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 4, p e18887 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pavel Polyakov Charles Soussen Junbo Duan Jérôme F L Duval David Brie Grégory Francius Automated force volume image processing for biological samples.
description	Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.
format	article
author	Pavel Polyakov Charles Soussen Junbo Duan Jérôme F L Duval David Brie Grégory Francius
author_facet	Pavel Polyakov Charles Soussen Junbo Duan Jérôme F L Duval David Brie Grégory Francius
author_sort	Pavel Polyakov
title	Automated force volume image processing for biological samples.
title_short	Automated force volume image processing for biological samples.
title_full	Automated force volume image processing for biological samples.
title_fullStr	Automated force volume image processing for biological samples.
title_full_unstemmed	Automated force volume image processing for biological samples.
title_sort	automated force volume image processing for biological samples.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/7fe4cf4f73a3440fae80b1cb664c3b0d
work_keys_str_mv	AT pavelpolyakov automatedforcevolumeimageprocessingforbiologicalsamples AT charlessoussen automatedforcevolumeimageprocessingforbiologicalsamples AT junboduan automatedforcevolumeimageprocessingforbiologicalsamples AT jeromeflduval automatedforcevolumeimageprocessingforbiologicalsamples AT davidbrie automatedforcevolumeimageprocessingforbiologicalsamples AT gregoryfrancius automatedforcevolumeimageprocessingforbiologicalsamples
_version_	1718424238815182848

Automated force volume image processing for biological samples.

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