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LAMA: automated image analysis for the developmental phenotyping of mouse embryos

Advanced 3D imaging modalities, such as micro-computed tomography (micro-CT), have been incorporated into the high-throughput embryo pipeline of the International Mouse Phenotyping Consortium (IMPC). This project generates large volumes of raw data that cannot be immediately exploited without signif...

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Autores principales: Horner, Neil R., Venkataraman, Shanmugasundaram, Armit, Chris, Casero, Ramón, Brown, James M., Wong, Michael D., van Eede, Matthijs C., Henkelman, R. Mark, Johnson, Sara, Teboul, Lydia, Wells, Sara, Brown, Steve D., Westerberg, Henrik, Mallon, Ann-Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Company of Biologists Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015254/
https://www.ncbi.nlm.nih.gov/pubmed/33574040
http://dx.doi.org/10.1242/dev.192955
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author Horner, Neil R.
Venkataraman, Shanmugasundaram
Armit, Chris
Casero, Ramón
Brown, James M.
Wong, Michael D.
van Eede, Matthijs C.
Henkelman, R. Mark
Johnson, Sara
Teboul, Lydia
Wells, Sara
Brown, Steve D.
Westerberg, Henrik
Mallon, Ann-Marie
author_facet Horner, Neil R.
Venkataraman, Shanmugasundaram
Armit, Chris
Casero, Ramón
Brown, James M.
Wong, Michael D.
van Eede, Matthijs C.
Henkelman, R. Mark
Johnson, Sara
Teboul, Lydia
Wells, Sara
Brown, Steve D.
Westerberg, Henrik
Mallon, Ann-Marie
author_sort Horner, Neil R.
collection PubMed
description Advanced 3D imaging modalities, such as micro-computed tomography (micro-CT), have been incorporated into the high-throughput embryo pipeline of the International Mouse Phenotyping Consortium (IMPC). This project generates large volumes of raw data that cannot be immediately exploited without significant resources of personnel and expertise. Thus, rapid automated annotation is crucial to ensure that 3D imaging data can be integrated with other multi-dimensional phenotyping data. We present an automated computational mouse embryo phenotyping pipeline that harnesses the large amount of wild-type control data available in the IMPC embryo pipeline in order to address issues of low mutant sample number as well as incomplete penetrance and variable expressivity. We also investigate the effect of developmental substage on automated phenotyping results. Designed primarily for developmental biologists, our software performs image pre-processing, registration, statistical analysis and segmentation of embryo images. We also present a novel anatomical E14.5 embryo atlas average and, using it with LAMA, show that we can uncover known and novel dysmorphology from two IMPC knockout lines.
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spelling pubmed-80152542021-04-21 LAMA: automated image analysis for the developmental phenotyping of mouse embryos Horner, Neil R. Venkataraman, Shanmugasundaram Armit, Chris Casero, Ramón Brown, James M. Wong, Michael D. van Eede, Matthijs C. Henkelman, R. Mark Johnson, Sara Teboul, Lydia Wells, Sara Brown, Steve D. Westerberg, Henrik Mallon, Ann-Marie Development Techniques and Resources Advanced 3D imaging modalities, such as micro-computed tomography (micro-CT), have been incorporated into the high-throughput embryo pipeline of the International Mouse Phenotyping Consortium (IMPC). This project generates large volumes of raw data that cannot be immediately exploited without significant resources of personnel and expertise. Thus, rapid automated annotation is crucial to ensure that 3D imaging data can be integrated with other multi-dimensional phenotyping data. We present an automated computational mouse embryo phenotyping pipeline that harnesses the large amount of wild-type control data available in the IMPC embryo pipeline in order to address issues of low mutant sample number as well as incomplete penetrance and variable expressivity. We also investigate the effect of developmental substage on automated phenotyping results. Designed primarily for developmental biologists, our software performs image pre-processing, registration, statistical analysis and segmentation of embryo images. We also present a novel anatomical E14.5 embryo atlas average and, using it with LAMA, show that we can uncover known and novel dysmorphology from two IMPC knockout lines. The Company of Biologists Ltd 2021-03-24 /pmc/articles/PMC8015254/ /pubmed/33574040 http://dx.doi.org/10.1242/dev.192955 Text en © 2021. Published by The Company of Biologists Ltd http://creativecommons.org/licenses/by/4.0This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Techniques and Resources
Horner, Neil R.
Venkataraman, Shanmugasundaram
Armit, Chris
Casero, Ramón
Brown, James M.
Wong, Michael D.
van Eede, Matthijs C.
Henkelman, R. Mark
Johnson, Sara
Teboul, Lydia
Wells, Sara
Brown, Steve D.
Westerberg, Henrik
Mallon, Ann-Marie
LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title_full LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title_fullStr LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title_full_unstemmed LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title_short LAMA: automated image analysis for the developmental phenotyping of mouse embryos
title_sort lama: automated image analysis for the developmental phenotyping of mouse embryos
topic Techniques and Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015254/
https://www.ncbi.nlm.nih.gov/pubmed/33574040
http://dx.doi.org/10.1242/dev.192955
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