MassIVE MSV000092241

Partial Public PXD043216

DIA analysis from a large scale patient cohort for Cerebrospinal Fluid Biomarker Discovery in Alzheimer Disease

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Description

Diagnosis of Alzheimer disease (AD) primarily relies on cognitive assessments combined with imaging and cerebrospinal fluid (CSF) biomarkers of amyloid and tau proteins. These biomarkers correlate well with levels of neuropathology, but may be inadequate predictors of cognitive trajectory or treatment response to novel therapeutics. Analysis of larger sets of biomarkers together could improve the diagnosis and prognosis of AD, examine the interaction with and differentiate between comorbid pathophysiologies, and measure treatment efficacy. Here, we report data from a Data-independent Acquisition (DIA) analysis used to simultaneously quantify hundreds of proteins in a large-scale patient cohort from a neurology clinic, all of whom had clinical indication for diagnostic lumbar puncture. We aim to define markers that can stratify and diagnose AD in this clinically-complex cohort. Samples from 408 patients were collected from the clinic according to standardized collection and processing protocols. Samples were prepared in 21 different batches of 24 (23 samples plus one common pool) and analyzed by LC-MS/MS on an Orbitrap Fusion using a DIA method with non-overlapping 25m/z windows from 400-1000 m/z, with a resolution of 120K for MS1 and 60K for MS2. Each sample was injected in duplicate with 4 column washes between each sample. All samples from each batch were run sequentially, with multiples days separating each batch. [doi:10.25345/C56Q1SS95] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Alzheimer disease ; Biomarker discovery ; CSF ; Data Independent acquisition ; Large scale patient cohort ; Batch effect removal

Contact

Principal Investigators:
(in alphabetical order) 		Becky Carlyle, Massachusetts General Hospital and Harvard Medical School, United States
Steven Arnold, Massachusetts General Hospital, USA
Submitting User: QC_MGH
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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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