A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease

Gennifer E. Merrihew; Jea Park; Deanna Plubell; Brian C. Searle; C. Dirk Keene; Eric B. Larson; Randall Bateman; Richard J. Perrin; Jasmeer P. Chhatwal; Martin R. Farlow; Catriona A. McLean; Bernardino Ghetti; Kathy L. Newell; Matthew P. Frosch; Thomas J. Montine; Michael J. MacCoss

doi:10.1038/s41597-023-02057-7

A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease

Gennifer E. Merrihew, Jea Park, Deanna Plubell, Brian C. Searle, C. Dirk Keene, Eric B. Larson, Randall Bateman, Richard J. Perrin, Jasmeer P. Chhatwal, Martin R. Farlow, Catriona A. McLean, Bernardino Ghetti, Kathy L. Newell, Matthew P. Frosch, Thomas J. Montine, Michael J. MacCoss

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Abstract

Alzheimer’s disease (AD) is a looming public health disaster with limited interventions. Alzheimer’s is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.

Original language	English
Article number	206
Journal	Scientific Data
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41597-023-02057-7
State	Published - Dec 2023

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Merrihew, G. E., Park, J., Plubell, D., Searle, B. C., Keene, C. D., Larson, E. B., Bateman, R., Perrin, R. J., Chhatwal, J. P., Farlow, M. R., McLean, C. A., Ghetti, B., Newell, K. L., Frosch, M. P., Montine, T. J., & MacCoss, M. J. (2023). A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease. Scientific Data, 10(1), Article 206. https://doi.org/10.1038/s41597-023-02057-7

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title = "A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer{\textquoteright}s disease",

abstract = "Alzheimer{\textquoteright}s disease (AD) is a looming public health disaster with limited interventions. Alzheimer{\textquoteright}s is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.",

author = "Merrihew, {Gennifer E.} and Jea Park and Deanna Plubell and Searle, {Brian C.} and Keene, {C. Dirk} and Larson, {Eric B.} and Randall Bateman and Perrin, {Richard J.} and Chhatwal, {Jasmeer P.} and Farlow, {Martin R.} and McLean, {Catriona A.} and Bernardino Ghetti and Newell, {Kathy L.} and Frosch, {Matthew P.} and Montine, {Thomas J.} and MacCoss, {Michael J.}",

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year = "2023",

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doi = "10.1038/s41597-023-02057-7",

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Merrihew, GE, Park, J, Plubell, D, Searle, BC, Keene, CD, Larson, EB, Bateman, R, Perrin, RJ, Chhatwal, JP, Farlow, MR, McLean, CA, Ghetti, B, Newell, KL, Frosch, MP, Montine, TJ & MacCoss, MJ 2023, 'A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease', Scientific Data, vol. 10, no. 1, 206. https://doi.org/10.1038/s41597-023-02057-7

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AU - Plubell, Deanna

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AU - Keene, C. Dirk

AU - Larson, Eric B.

AU - Bateman, Randall

AU - Perrin, Richard J.

AU - Chhatwal, Jasmeer P.

AU - Farlow, Martin R.

AU - McLean, Catriona A.

AU - Ghetti, Bernardino

AU - Newell, Kathy L.

AU - Frosch, Matthew P.

AU - Montine, Thomas J.

AU - MacCoss, Michael J.

PY - 2023/12

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AB - Alzheimer’s disease (AD) is a looming public health disaster with limited interventions. Alzheimer’s is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.

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A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease

Abstract

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