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A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotempor...

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Autores principales: Menden, Kevin, Francescatto, Margherita, Nyima, Tenzin, Blauwendraat, Cornelis, Dhingra, Ashutosh, Castillo-Lizardo, Melissa, Fernandes, Noémia, Kaurani, Lalit, Kronenberg-Versteeg, Deborah, Atasu, Burcu, Sadikoglou, Eldem, Borroni, Barbara, Rodriguez-Nieto, Salvador, Simon-Sanchez, Javier, Fischer, Andre, Craig, David Wesley, Neumann, Manuela, Bonn, Stefan, Rizzu, Patrizia, Heutink, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692098/
https://www.ncbi.nlm.nih.gov/pubmed/38040703
http://dx.doi.org/10.1038/s41597-023-02598-x
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author Menden, Kevin
Francescatto, Margherita
Nyima, Tenzin
Blauwendraat, Cornelis
Dhingra, Ashutosh
Castillo-Lizardo, Melissa
Fernandes, Noémia
Kaurani, Lalit
Kronenberg-Versteeg, Deborah
Atasu, Burcu
Sadikoglou, Eldem
Borroni, Barbara
Rodriguez-Nieto, Salvador
Simon-Sanchez, Javier
Fischer, Andre
Craig, David Wesley
Neumann, Manuela
Bonn, Stefan
Rizzu, Patrizia
Heutink, Peter
author_facet Menden, Kevin
Francescatto, Margherita
Nyima, Tenzin
Blauwendraat, Cornelis
Dhingra, Ashutosh
Castillo-Lizardo, Melissa
Fernandes, Noémia
Kaurani, Lalit
Kronenberg-Versteeg, Deborah
Atasu, Burcu
Sadikoglou, Eldem
Borroni, Barbara
Rodriguez-Nieto, Salvador
Simon-Sanchez, Javier
Fischer, Andre
Craig, David Wesley
Neumann, Manuela
Bonn, Stefan
Rizzu, Patrizia
Heutink, Peter
author_sort Menden, Kevin
collection PubMed
description Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.
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spelling pubmed-106920982023-12-03 A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes Menden, Kevin Francescatto, Margherita Nyima, Tenzin Blauwendraat, Cornelis Dhingra, Ashutosh Castillo-Lizardo, Melissa Fernandes, Noémia Kaurani, Lalit Kronenberg-Versteeg, Deborah Atasu, Burcu Sadikoglou, Eldem Borroni, Barbara Rodriguez-Nieto, Salvador Simon-Sanchez, Javier Fischer, Andre Craig, David Wesley Neumann, Manuela Bonn, Stefan Rizzu, Patrizia Heutink, Peter Sci Data Data Descriptor Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease. Nature Publishing Group UK 2023-12-01 /pmc/articles/PMC10692098/ /pubmed/38040703 http://dx.doi.org/10.1038/s41597-023-02598-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Menden, Kevin
Francescatto, Margherita
Nyima, Tenzin
Blauwendraat, Cornelis
Dhingra, Ashutosh
Castillo-Lizardo, Melissa
Fernandes, Noémia
Kaurani, Lalit
Kronenberg-Versteeg, Deborah
Atasu, Burcu
Sadikoglou, Eldem
Borroni, Barbara
Rodriguez-Nieto, Salvador
Simon-Sanchez, Javier
Fischer, Andre
Craig, David Wesley
Neumann, Manuela
Bonn, Stefan
Rizzu, Patrizia
Heutink, Peter
A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title_full A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title_fullStr A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title_full_unstemmed A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title_short A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
title_sort multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692098/
https://www.ncbi.nlm.nih.gov/pubmed/38040703
http://dx.doi.org/10.1038/s41597-023-02598-x
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