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Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease
Patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing T...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5848788/ https://www.ncbi.nlm.nih.gov/pubmed/29533394 http://dx.doi.org/10.1038/sdata.2018.36 |
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author | Ping, Lingyan Duong, Duc M. Yin, Luming Gearing, Marla Lah, James J. Levey, Allan I. Seyfried, Nicholas T. |
author_facet | Ping, Lingyan Duong, Duc M. Yin, Luming Gearing, Marla Lah, James J. Levey, Allan I. Seyfried, Nicholas T. |
author_sort | Ping, Lingyan |
collection | PubMed |
description | Patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD. |
format | Online Article Text |
id | pubmed-5848788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-58487882018-03-24 Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease Ping, Lingyan Duong, Duc M. Yin, Luming Gearing, Marla Lah, James J. Levey, Allan I. Seyfried, Nicholas T. Sci Data Data Descriptor Patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD. Nature Publishing Group 2018-03-13 /pmc/articles/PMC5848788/ /pubmed/29533394 http://dx.doi.org/10.1038/sdata.2018.36 Text en Copyright © 2018, The Author(s) http://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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Ping, Lingyan Duong, Duc M. Yin, Luming Gearing, Marla Lah, James J. Levey, Allan I. Seyfried, Nicholas T. Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title | Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title_full | Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title_fullStr | Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title_full_unstemmed | Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title_short | Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease |
title_sort | global quantitative analysis of the human brain proteome in alzheimer’s and parkinson’s disease |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5848788/ https://www.ncbi.nlm.nih.gov/pubmed/29533394 http://dx.doi.org/10.1038/sdata.2018.36 |
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