High content, multi-parameter analyses in buccal cells to identify Alzheimer’s disease
Maxime Francois, Edith Cowan University
Ralph Martins, Edith Cowan UniversityFollow
Stephanie Rainey-Smith, Edith Cowan UniversityFollow
Bentham Science Publishers
Centre of Excellence for Alzheimer's Disease Research & Care
Alzheimer’s disease (AD) is a degenerative brain disorder and is the most common form of dementia. Minimally invasive approaches are required that combine biomarkers to identify individuals who are at risk of developing mild cognitive impairment (MCI) and AD, to appropriately target clinical trials for therapeutic discovery as well as lifestyle strategies aimed at prevention. Buccal mucosa cells from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing cohort (n=60) were investigated for cytological markers that could be used to identify both MCI and AD individuals. Visual scoring of the buccal cytome demonstrated a significantly lower frequency of basal and karyorrhectic cells in the MCI group compared with controls. A high content, automated assay was developed using laser scanning cytometry to simultaneously measure cell types, nuclear DNA content and aneuploidy, neutral lipid content, putative Tau and amyloid-β (Aβ) in buccal cells. DNA content, aneuploidy, neutral lipids and Tau were similar in all groups. However, there was significantly lower Tau protein in both basal and karyolytic buccal cell types compared with differentiated buccal cells. Aβ, as measured by frequency of cells containing Aβ signal, as well as area and integral of Aβ signal, was significantly higher in the AD group compared with the control group. Buccal cell Aβ was correlated with mini-mental state examination (MMSE) scores (r = -0.436, P=0.001) and several blood-based biomarkers. Combining newly identified biomarkers from buccal cells with those already established may offer a potential route for more specific biomarker panels which may substantially increase the likelihood of better predictive markers for earlier diagnosis of AD. © 2016 Bentham Science Publishers.