Alzheimer’s Functional Phenotypic Disease Model
Alzheimer’s disease (AD) is caused by soluble beta amyloid, ABeta, aggregates/oligomers leading to neuro-degeneration and cognitive impairment which is one of the major hallmarks in AD.
Yet, the NeuroProof platform is able to go beyond these neurotoxic, i.e. cytotoxic events which of course affect the neuronal network activity dramatically induced by loss of neurons. We investigate the functional Abeta effects before cellular toxicity is affecting neuronal activity. Thus, we concentrate on the early functional physiological effects of Abeta42 and therefore the early pathophysiology of AD. In this context, we have shown that acutely applied Abeta42 peptides significantly affect the functional network activity at a wide range of our 200 parameters especially describing the network synchronicity and connectivity.
Image: Top: Westernblot of native and HFIP-monomeric Abeta42 solved in DSMO. HFIP-monomerized Abeta is stable over 24 hours. Right/bottom: Acute functional effects of different Abeta42 preparations (native oligomeric, monomeric, recombinant) are similar in concentration-response experiments, yet, significant.
In our Abeta-rescue assay we describe
- spontaneous network activity of primary mouse hippocampal neurons,
- acute effects of synthetic or recombinant amyloid beta peptides
- time-dependent efficacy of test compounds to revert the challenged network activity based on selected assay parameters.
Image: Acute Abeta42 effects are rescued by donepezil. Neuronal activity is significantly affected by Abeta1-42 already at nanomolar concentrations shown at 16 parameters from four categories; subsequent co-application of donepezil reverts activity to native control levels; Abeta effect sizes continuously increase under DMSO control conditions.
"Effect Score" calculation: Projection of up to 204 parameters into a single parameter allows ranking of compound effects and rescue efficacies at different concentrations or compound combinations (e.g. with donepezil) based on the complete functional finger print. DMSO control is set to “0”. Abeta is set to “1”. The calculated Z-factor (describing the effect size) is optimized to find the best multi-parametric discrimination between control and Abeta. *p≤0.05, **p≤0.01, ***p≤0.001 vs. Abeta+DMSO; + vs. baseline.