Dataset Information

AD Study - A multiomic approach to characterize the temporal sequence in Alzheimer's disease-related pathology

ABSTRACT: This section will be updated by the appropriate individual.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Brain

SUBMITTER: Vikram Mitra

LAB HEAD: Dr Ian Pike

PROVIDER: PXD008016 | Pride | 2018-12-17

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	30-058_All_MultiConsensus_Grouped_psms.txt	Txt
	30_058-ppr160126-1a_Fr1_01.raw	Raw
	30_058-ppr160126-1a_Fr1_02.raw	Raw
	30_058-ppr160126-1a_Fr2_01.raw	Raw
	30_058-ppr160126-1a_Fr2_02.raw	Raw

Items per page:

1 - 5 of 97

Publications

A multiomic approach to characterize the temporal sequence in Alzheimer's disease-related pathology.

Marttinen Mikael M Paananen Jussi J Neme Antonio A Mitra Vikram V Takalo Mari M Natunen Teemu T Paldanius Kaisa M A KMA Mäkinen Petra P Bremang Michael M Kurki Mitja I MI Rauramaa Tuomas T Leinonen Ville V Soininen Hilkka H Haapasalo Annakaisa A Pike Ian I Hiltunen Mikko M

Neurobiology of disease 20181215

No single-omic approach completely elucidates the multitude of alterations taking place in Alzheimer's disease (AD). Here, we coupled transcriptomic and phosphoproteomic approaches to determine the temporal sequence of changes in mRNA, protein, and phosphopeptide expression levels from human temporal cortical samples, with varying degree of AD-related pathology. This approach highlighted fluctuation in synaptic and mitochondrial function as the earliest pathological events in brain samples with ...[more]

PMID: 30557660

Similar Datasets

Project description:Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer's disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient's longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer's Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject's degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.