Understanding Alzheimer’s disease resilience at the cellular level

Abstract Background Until recently, postmortem observation of amyloid beta (Aβ) and tau neurofibrillary tangles was sufficient for a definitive diagnosis of AD. Observations of resilient subjects exhibiting Alzheimer's‐associated neuropathology but no dementia have challenged this concept. Understanding the molecular mechanisms of resilience can provide a powerful suite of targets for therapeutic interventions. Method Building upon our studies of bulk brain transcriptional signatures of AD resilience, and in order to identify cell‐specific signatures, we have leveraged recently available single nucleus RNA‐seq data (n = 30) from the Religious Order Study and Memory and Aging Project (ROSMAP) to perform differential gene expression analyses. We classified individuals into AD, Resilience (RES), and Control (CTRL) using levels of Aβ plaques and neurofibrillary tangles, and presence/absence of cognitive impairment. Result Resilience signatures are shared between different cell types, but few are broadly distributed. Certain cell types show higher response to early events (e.g., excitatory neurons), while others seem to be particularly important in driving resilience to AD (e.g., inhibitory neurons and oligodendrocytes). Among cell‐specific differences, we noticed patterns where cells from the RES group had an opposing direction compared to AD and CTRL, suggesting protective events, or even resistance to AD neuropathology. For instance, in excitatory neurons, we identified 1,437 differentially expressed genes (DEGs) between RES/CTRL and 179 DEGs between AD/RES. CAMK2N1 (codes for an inhibitor of CaMKII, a synaptic protein pivotal in learning and memory) shows decreased expression between RES/AD and RES/CTRL. In contrast, in inhibitory neurons we identified 759 DEGs between RES/AD, and 46 DEGs in RES/CTRL. CRLF3 (encodes a cytokine receptor‐like factor thought to negatively regulate cell cycle progression, involved in neuronal morphology and synaptic vesicle biogenesis) exhibits increased expression in RES compared to AD and CTRL. For in silico cross‐validation, we determined if features identified as ‘markers of resilience’ were good predictors by comparing to the original diagnostic group. Our results suggest that oligodendrocytes are important drivers of AD resilience. Conclusion We have identified cell‐specific gene expression signatures of AD resilience, suggesting inhibitory neurons and oligodendrocytes as critical drivers of AD resilience.