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P2‐108: USING COMPUTED TOMOGRAPHY TO ASSESS BRAIN VOLUMETRICS IN AGING
Author(s) -
Irimia Andrei,
Kaplan Hillard,
Trumble Ben C.,
Copajira Adrian Juan J.,
Maher Alexander S.,
Rostowsky Kenneth A.,
Chowdhury Nahian F.,
Sutherland M. Linda,
Sutherland James D.,
Allam Adel H.,
Rodriguez Daniel Eid,
Cummings Daniel K.,
Garcia Angela R.,
Rowan Chris J.,
Miyamoto Michael I.,
Alami Sarah,
Seabright Edmond,
Barisano Giuseppe,
Mack Wendy J.,
Chang Chui Helena,
Stieglitz Jonathan,
Law Meng,
Gurven Michael,
Finch Caleb E.
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.06.2515
Subject(s) - medicine , magnetic resonance imaging , neuroimaging , gold standard (test) , nuclear medicine , atrophy , white matter , radiology , cerebrospinal fluid , brain size , pathology , psychiatry
Background: Late-onset Alzheimer disease (LOAD) is the most common cause of dementia worldwide. Despite recent efforts, there is a lack of predictive animals models that can be used to study the progressive neurodegenerative disorder. This is partly due to the cross-species gap presenting a major challenge for translating molecular data between human and mouse. Methods: Here, we developed a novel systems biology approach to align human post mortem brain transcriptome data with key mouse transcripts to evaluate novel LOAD disease mouse models in a highly reproducible manner. A total of 30 harmonized co-expression modules derived from 2,114 human samples across seven brain regions and three research studies was used to create a molecular catalog of LOAD associated processes. A novel NanoString gene expression panel composed of 770 mouse gene probes was designed to rapidly and effectively correlate mouse samples with key human disease processes and pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-seq was performed to assess platform specific effects. Results: Analysis of two LOAD mouse models, APOE4 and Trem2*R47H (141 samples) at different ages (2-14 months) showed an overlap with distinct human co-expression modules linked to specific disease associated pathways, including immune related and DNA-repair pathways. Cross-platform comparison between the novel nCounter Mouse AD Panel with RNA-seq data shows a robust and strong correlation between mouse gene expression changes independent of platform related effects. Conclusions: Taken together, we show that the novel Mouse AD expression panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of novel LOAD mouse models.