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Normalizing for individual cell population context in the analysis of high-content cellular screens

BACKGROUND: High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell'...

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Autores principales: Knapp, Bettina, Rebhan, Ilka, Kumar, Anil, Matula, Petr, Kiani, Narsis A, Binder, Marco, Erfle, Holger, Rohr, Karl, Eils, Roland, Bartenschlager, Ralf, Kaderali, Lars
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/
https://www.ncbi.nlm.nih.gov/pubmed/22185194
http://dx.doi.org/10.1186/1471-2105-12-485
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author Knapp, Bettina
Rebhan, Ilka
Kumar, Anil
Matula, Petr
Kiani, Narsis A
Binder, Marco
Erfle, Holger
Rohr, Karl
Eils, Roland
Bartenschlager, Ralf
Kaderali, Lars
author_facet Knapp, Bettina
Rebhan, Ilka
Kumar, Anil
Matula, Petr
Kiani, Narsis A
Binder, Marco
Erfle, Holger
Rohr, Karl
Eils, Roland
Bartenschlager, Ralf
Kaderali, Lars
author_sort Knapp, Bettina
collection PubMed
description BACKGROUND: High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. RESULTS: We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. CONCLUSIONS: Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
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spelling pubmed-32591092012-01-18 Normalizing for individual cell population context in the analysis of high-content cellular screens Knapp, Bettina Rebhan, Ilka Kumar, Anil Matula, Petr Kiani, Narsis A Binder, Marco Erfle, Holger Rohr, Karl Eils, Roland Bartenschlager, Ralf Kaderali, Lars BMC Bioinformatics Research Article BACKGROUND: High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. RESULTS: We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. CONCLUSIONS: Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results. BioMed Central 2011-12-20 /pmc/articles/PMC3259109/ /pubmed/22185194 http://dx.doi.org/10.1186/1471-2105-12-485 Text en Copyright ©2011 Knapp 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 Research Article
Knapp, Bettina
Rebhan, Ilka
Kumar, Anil
Matula, Petr
Kiani, Narsis A
Binder, Marco
Erfle, Holger
Rohr, Karl
Eils, Roland
Bartenschlager, Ralf
Kaderali, Lars
Normalizing for individual cell population context in the analysis of high-content cellular screens
title Normalizing for individual cell population context in the analysis of high-content cellular screens
title_full Normalizing for individual cell population context in the analysis of high-content cellular screens
title_fullStr Normalizing for individual cell population context in the analysis of high-content cellular screens
title_full_unstemmed Normalizing for individual cell population context in the analysis of high-content cellular screens
title_short Normalizing for individual cell population context in the analysis of high-content cellular screens
title_sort normalizing for individual cell population context in the analysis of high-content cellular screens
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/
https://www.ncbi.nlm.nih.gov/pubmed/22185194
http://dx.doi.org/10.1186/1471-2105-12-485
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