Multi-omics integration in Alzheimer’s disease: linking genotype to phenotype through glial activation, synaptopathy and cognitive decline
File(s)
Author(s)
Schneegans, Eléonore
Type
Thesis
Abstract
Alzheimer's disease (AD) is not a linear process driven solely by amyloid-beta and tau deposition but rather a dynamic, multifaceted disease involving interconnected biochemical, cellular, and clinical stages that evolve over many years and are preceded by a long prodromal phase. In this thesis, I explored how glial activation, specifically of microglia and astrocytes, contributes to AD pathogenesis by adopting a bottom-up approach from genotype to phenotype. I hypothesised that glial activation contributes to cognitive decline through its effect on synaptopathy, forming a glia-cognition link that underlies clinical heterogeneity in AD.
I integrated bulk transcriptomics and proteomics with snRNA-seq, synaptic, and spatial omics data across multiple AD cohorts. I developed and applied an R package, Omix, to integrate transcriptomic and proteomic datasets, enabling the identification of glial-specific disease modules. These modules were mapped to genotype information and validated against snRNA-seq subpopulations and imaging mass cytometry data.
Results highlighted a role for early microglial activation in the genesis of AD and later astrocyte reactivity as a determinant of clinical progression. Microglial activation preceded severe neuropathology, orchestrating local inflammation and complement-driven synaptic pruning. A neurotoxic astrocyte response was associated with accelerated cognitive decline. Astrocyte-associated synaptopathy, involving peri-synaptic astrocyte processes (PAPs), emerged as a potential driver of synapse loss. Imaging mass cytometry localised neurotoxic astrocytes preferentially around Aβ plaques.
This thesis provides evidence that glial activation actively shapes AD pathogenesis and suggests that targeting glial cells, particularly astrocytes, may slow cognitive decline. I nominated fluid-based candidate biomarkers for detecting individuals at risk of rapid decline, offering potential for patient stratification in clinical trials. Ultimately, this work highlights how integrative multi-omics can reveal key mechanisms, therapeutic targets, and biomarkers in complex neurodegenerative diseases.
I integrated bulk transcriptomics and proteomics with snRNA-seq, synaptic, and spatial omics data across multiple AD cohorts. I developed and applied an R package, Omix, to integrate transcriptomic and proteomic datasets, enabling the identification of glial-specific disease modules. These modules were mapped to genotype information and validated against snRNA-seq subpopulations and imaging mass cytometry data.
Results highlighted a role for early microglial activation in the genesis of AD and later astrocyte reactivity as a determinant of clinical progression. Microglial activation preceded severe neuropathology, orchestrating local inflammation and complement-driven synaptic pruning. A neurotoxic astrocyte response was associated with accelerated cognitive decline. Astrocyte-associated synaptopathy, involving peri-synaptic astrocyte processes (PAPs), emerged as a potential driver of synapse loss. Imaging mass cytometry localised neurotoxic astrocytes preferentially around Aβ plaques.
This thesis provides evidence that glial activation actively shapes AD pathogenesis and suggests that targeting glial cells, particularly astrocytes, may slow cognitive decline. I nominated fluid-based candidate biomarkers for detecting individuals at risk of rapid decline, offering potential for patient stratification in clinical trials. Ultimately, this work highlights how integrative multi-omics can reveal key mechanisms, therapeutic targets, and biomarkers in complex neurodegenerative diseases.
Version
Open Access
Date Issued
2025-01-24
Date Awarded
01/06/2025
License URL
Advisor
Jackson, Johanna
Matthews, Paul
Fancy, Nurun
Publisher Department
Department of Brain Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)