Cellular senescence profiling reveals distinct sub-types of neurofibrillary tangle bearing neurons.

Background: Neurons with tau-containing neurofibrillary tangles (NFTs) closely correlate with Alzheimer’s disease (AD) progression and severity. Our prior work determined that NFT-bearing neurons undergo a change in cell fate consistent with cellular senescence. The goal of this project was to determine whether senescent NFT-bearing neurons differ from quiescent NFT-bearing neurons. Method: Postmortem human AD brains were analyzed using GeoMx (NanoString, Inc) digital spatial profiling (DSP). Region of interest (ROI) selection included the presence or absence of neuronally expressed AT8 (phosphorylated tau, NFTs) and/or p19 (senescence). NFTs with or without p19 expression were compared to healthy neurons from the same cases. The expression of 86 proteins associated with neuropathology, inflammation, autophagy and cell typing was determined for each ROI and immediate environment (Fig. 1a-b). Result: NFT-bearing neurons with p19 (senescent) were significantly different than NFT-bearing neurons without p19 (quiescent). Senescent NFT-bearing neurons appeared similar to neurons without tau pathology. In contrast quiescent NFT-bearing neurons displayed signs of crisis (i.e. apoptosis). Additionally, we revealed significant differences in tau phosphorylation at multiple epitopes between the two classes of NFT-bearing neurons (Fig. 1c-h). Conclusion: These data indicate the presence of distinct NFT-bearing neuron subtypes in AD. Our ongoing studies are investigating (i) whether senescent or quiescent NFTs are more closely associated with pathogenesis, (ii) the transition between neuronal quiescence to senescence and (iii) which tau species contribute to this cell fate decision (self-sacrificing versus disease propagating). More broadly, these results will provide a better understanding of distinct tau species in neurodegeneration which is critically important to clinical studies that are increasingly relying on tau biomarkers to predict disease onset, progression and target engagement in clinical trials.