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

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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.

in: SCI DATA, Jahrgang 10, Nr. 1, 01.12.2023, S. 849.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Menden, K, Francescatto, M, Nyima, T, Blauwendraat, C, Dhingra, A, Castillo-Lizardo, M, Fernandes, N, Kaurani, L, Kronenberg-Versteeg, D, Atasu, B, Sadikoglou, E, Borroni, B, Rodriguez-Nieto, S, Simon-Sanchez, J, Fischer, A, Craig, DW, Neumann, M, Bonn, S, Rizzu, P & Heutink, P 2023, 'A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes', SCI DATA, Jg. 10, Nr. 1, S. 849. https://doi.org/10.1038/s41597-023-02598-x

APA

Menden, K., Francescatto, M., Nyima, T., Blauwendraat, C., Dhingra, A., Castillo-Lizardo, M., Fernandes, N., Kaurani, L., Kronenberg-Versteeg, D., Atasu, B., Sadikoglou, E., Borroni, B., Rodriguez-Nieto, S., Simon-Sanchez, J., Fischer, A., Craig, D. W., Neumann, M., Bonn, S., Rizzu, P., & Heutink, P. (2023). A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes. SCI DATA, 10(1), 849. https://doi.org/10.1038/s41597-023-02598-x

Vancouver

Menden K, Francescatto M, Nyima T, Blauwendraat C, Dhingra A, Castillo-Lizardo M et al. A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes. SCI DATA. 2023 Dez 1;10(1):849. https://doi.org/10.1038/s41597-023-02598-x

Bibtex

@article{bba5c7c7d39d47f08bdb8537c5ce5eb3,
title = "A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes",
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.",
keywords = "Humans, Frontal Lobe, Frontotemporal Dementia/genetics, Multiomics, Mutation",
author = "Kevin Menden and Margherita Francescatto and Tenzin Nyima and Cornelis Blauwendraat and Ashutosh Dhingra and Melissa Castillo-Lizardo and No{\'e}mia Fernandes and Lalit Kaurani and Deborah Kronenberg-Versteeg and Burcu Atasu and Eldem Sadikoglou and Barbara Borroni and Salvador Rodriguez-Nieto and Javier Simon-Sanchez and Andre Fischer and Craig, {David Wesley} and Manuela Neumann and Stefan Bonn and Patrizia Rizzu and Peter Heutink",
note = "{\textcopyright} 2023. The Author(s).",
year = "2023",
month = dec,
day = "1",
doi = "10.1038/s41597-023-02598-x",
language = "English",
volume = "10",
pages = "849",
journal = "SCI DATA",
issn = "2052-4463",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

TY - JOUR

T1 - A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes

AU - Menden, Kevin

AU - Francescatto, Margherita

AU - Nyima, Tenzin

AU - Blauwendraat, Cornelis

AU - Dhingra, Ashutosh

AU - Castillo-Lizardo, Melissa

AU - Fernandes, Noémia

AU - Kaurani, Lalit

AU - Kronenberg-Versteeg, Deborah

AU - Atasu, Burcu

AU - Sadikoglou, Eldem

AU - Borroni, Barbara

AU - Rodriguez-Nieto, Salvador

AU - Simon-Sanchez, Javier

AU - Fischer, Andre

AU - Craig, David Wesley

AU - Neumann, Manuela

AU - Bonn, Stefan

AU - Rizzu, Patrizia

AU - Heutink, Peter

N1 - © 2023. The Author(s).

PY - 2023/12/1

Y1 - 2023/12/1

N2 - 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.

AB - 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.

KW - Humans

KW - Frontal Lobe

KW - Frontotemporal Dementia/genetics

KW - Multiomics

KW - Mutation

U2 - 10.1038/s41597-023-02598-x

DO - 10.1038/s41597-023-02598-x

M3 - SCORING: Journal article

C2 - 38040703

VL - 10

SP - 849

JO - SCI DATA

JF - SCI DATA

SN - 2052-4463

IS - 1

ER -