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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10692098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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