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Toponomics method for the automated quantification of membrane protein translocation

BACKGROUND: Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and s...

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Autores principales: Domanova, Olga, Borbe, Stefan, Mühlfeld, Stefanie, Becker, Martin, Kubitz, Ralf, Häussinger, Dieter, Berlage, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230911/
https://www.ncbi.nlm.nih.gov/pubmed/21929784
http://dx.doi.org/10.1186/1471-2105-12-370
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author Domanova, Olga
Borbe, Stefan
Mühlfeld, Stefanie
Becker, Martin
Kubitz, Ralf
Häussinger, Dieter
Berlage, Thomas
author_facet Domanova, Olga
Borbe, Stefan
Mühlfeld, Stefanie
Becker, Martin
Kubitz, Ralf
Häussinger, Dieter
Berlage, Thomas
author_sort Domanova, Olga
collection PubMed
description BACKGROUND: Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and should yield results comparable to manual evaluation. The automated method presented here was developed on rat liver tissue sections to study the translocation of bile salt transport proteins in hepatocytes. For validation, the cholestatic liver state was compared to the normal biological state. RESULTS: An automated quantification method was developed to analyze the translocation of membrane proteins and evaluated in comparison to an established manual method. Firstly, regions of interest (membrane fragments) are identified in confocal microscopy images. Further, densitometric intensity profiles are extracted orthogonally to membrane fragments, following the direction from the plasma membrane to cytoplasm. Finally, several different quantitative descriptors were derived from the densitometric profiles and were compared regarding their statistical significance with respect to the transport protein distribution. Stable performance, robustness and reproducibility were tested using several independent experimental datasets. A fully automated workflow for the information extraction and statistical evaluation has been developed and produces robust results. CONCLUSIONS: New descriptors for the intensity distribution profiles were found to be more discriminative, i.e. more significant, than those used in previous research publications for the translocation quantification. The slow manual calculation can be substituted by the fast and unbiased automated method.
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spelling pubmed-32309112011-12-07 Toponomics method for the automated quantification of membrane protein translocation Domanova, Olga Borbe, Stefan Mühlfeld, Stefanie Becker, Martin Kubitz, Ralf Häussinger, Dieter Berlage, Thomas BMC Bioinformatics Methodology Article BACKGROUND: Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and should yield results comparable to manual evaluation. The automated method presented here was developed on rat liver tissue sections to study the translocation of bile salt transport proteins in hepatocytes. For validation, the cholestatic liver state was compared to the normal biological state. RESULTS: An automated quantification method was developed to analyze the translocation of membrane proteins and evaluated in comparison to an established manual method. Firstly, regions of interest (membrane fragments) are identified in confocal microscopy images. Further, densitometric intensity profiles are extracted orthogonally to membrane fragments, following the direction from the plasma membrane to cytoplasm. Finally, several different quantitative descriptors were derived from the densitometric profiles and were compared regarding their statistical significance with respect to the transport protein distribution. Stable performance, robustness and reproducibility were tested using several independent experimental datasets. A fully automated workflow for the information extraction and statistical evaluation has been developed and produces robust results. CONCLUSIONS: New descriptors for the intensity distribution profiles were found to be more discriminative, i.e. more significant, than those used in previous research publications for the translocation quantification. The slow manual calculation can be substituted by the fast and unbiased automated method. BioMed Central 2011-09-19 /pmc/articles/PMC3230911/ /pubmed/21929784 http://dx.doi.org/10.1186/1471-2105-12-370 Text en Copyright ©2011 Domanova et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Domanova, Olga
Borbe, Stefan
Mühlfeld, Stefanie
Becker, Martin
Kubitz, Ralf
Häussinger, Dieter
Berlage, Thomas
Toponomics method for the automated quantification of membrane protein translocation
title Toponomics method for the automated quantification of membrane protein translocation
title_full Toponomics method for the automated quantification of membrane protein translocation
title_fullStr Toponomics method for the automated quantification of membrane protein translocation
title_full_unstemmed Toponomics method for the automated quantification of membrane protein translocation
title_short Toponomics method for the automated quantification of membrane protein translocation
title_sort toponomics method for the automated quantification of membrane protein translocation
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230911/
https://www.ncbi.nlm.nih.gov/pubmed/21929784
http://dx.doi.org/10.1186/1471-2105-12-370
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