Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis

Neuron. 2020 Sep 9;107(5):821-835.e12. doi: 10.1016/j.neuron.2020.06.010. Epub 2020 Jun 29.


A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at Overall, our study provides a molecular framework for understanding the complex interplay between Aβ, aging, and neurodegeneration within the most vulnerable neurons in AD.

Keywords: Alzheimer's disease; PTBP1; bacTRAP; machine learning; network; selective neuronal vulnerability.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aging / genetics
  • Aging / pathology
  • Alzheimer Disease / genetics
  • Alzheimer Disease / pathology*
  • Animals
  • Gene Expression Profiling / methods*
  • Gene Regulatory Networks / physiology
  • Humans
  • Machine Learning*
  • Mice
  • Neurons / pathology*
  • Transcriptome*