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

Authors :
Kevin Menden
Margherita Francescatto
Tenzin Nyima
Cornelis Blauwendraat
Ashutosh Dhingra
Melissa Castillo-Lizardo
Noémia Fernandes
Lalit Kaurani
Deborah Kronenberg-Versteeg
Burcu Atasu
Eldem Sadikoglou
Barbara Borroni
Salvador Rodriguez-Nieto
Javier Simon-Sanchez
Andre Fischer
David Wesley Craig
Manuela Neumann
Stefan Bonn
Patrizia Rizzu
Peter Heutink
Source :
Scientific Data, Vol 10, Iss 1, Pp 1-8 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract 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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.88edd2738c9d48e8ab92b285970b6bb0
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-023-02598-x