In-depth immune investigation of Alzheimer's disease. (SAO)

Being the first cause of dementia, Alzheimer’s disease (AD) has a strong burden in an ageing society in term of

quality of life and cost. Yet, therapeutic tools to prevent disease appearance or progression are lacking. The role for

the immune system through neuroinflammation as playing a part in AD pathophysiology has recently emerged. Indepth

investigation of the immune mechanisms related to AD remains underexplored while this compartment

might bring new therapeutic options to the field.

While a clear role for the innate immune system has been identified in AD pathophysiology, the role of the adaptive

immune system remains to be determined albeit evidence of acquired immunity playing part in AD has been

strongly identified in recent studies in which carrying specific HLA haplotype has shown to be a high-risk factor for

developing AD.

In this proposal, using a multi-layered high-definition immune phenotyping platform based on advanced flow

cytometry, we will thoroughly assess the immune system of patients diagnosed with AD to establish the complete

immune landscape of the disease. This study will investigate 3 different patient cohorts. Firstly, we will compare

the immune status of AD patients to the one of healthy controls (spouses). Secondly, the immune signature

identified will be further evaluated in patients enrolled in a pre-clinical phase of AD. In addition, systematic brain

imaging data are available to further stratify the patients. Finally, the third studied cohort will evaluate the

specificity of the immune AD pattern through comparison with patients affected with other causes of dementia

(Frontotemporal dementia and Dementia with Lewy Bodies).

In this study, using multi-parameter data from a high-throughput source, we will build a systems immunology

analysis specifically studying the underlying immunological mechanisms of neurodegeneration and

neuroinflammation associated with AD development. Additional implementation of a machine learning approach

will evaluate the ability of using immunological parameters as biomarkers for diagnosis tool for Alzheimer’s disease.

The overall goal of the proposal is to discover an AD immune signature using in-depth immunophenotyping in

addition to machine learning analyses to predict AD diagnosis and possibly the evolution of the disease. By

performing a full immunological phenotyping of AD patients, we hope to further increase the knowledge based on

which future diagnostics and therapeutics will be designed.