An integrative multi-omics framework for decoding microglial ecosystems in Alzheimer's disease

TL;DR

This paper introduces a new multi-omics framework to study microglial changes in Alzheimer's disease, revealing key cellular and genetic patterns linked to disease progression.

Contribution

The novel contribution is an integrative framework combining multiple single-cell analysis tools to decode microglial ecosystems in Alzheimer's disease.

Findings

Microglia transition from homeostatic to proliferative and senescent states during Alzheimer's progression.

A filopedia dynamics module (MYO10/PARVG) was identified as a novel microglial state.

RTN4-LINGO1 signaling is implicated in impaired neuronal repair in Alzheimer's disease.

Abstract

Alzheimer’s disease involves complex cellular alterations, yet current methods analyze cell states, signaling, and genetic risk in isolation, preventing systems-level understanding.

We developed an integrative framework combining quasi-binomial compositional analysis, scDemon [1], LIANA [2], scFates [3], and scDRS [4], applied to 12 integrated snRNA-seq datasets from human entorhinal and prefrontal cortex.

Analysis revealed coordinated cellular alterations with inhibitory neuron depletion and microglia expansion. scDemon identified novel microglial states including a filopedia dynamics module (MYO10/PARVG). Trajectory analysis showed progression from homeostatic (P2RY12-high) to proliferative (APOE, AXL-high) and senescent (CDKN1A-high) states. LIANA implicated RTN4-LINGO1 signaling in impaired neuronal repair, while scDRS mapped disease genetic risk to microglial cells.

Our framework links cellular pathophysiology to genetic etiology, providing a blueprint for identifying therapeutic targets in neurodegenerative disease.

1. Mathys H, Boix CA, Akay LA et al. ‘Single-cell multiregion dissection of Alzheimer’s disease.’ Nature 2024;632:858–868.

2. Dimitrov D, Schäfer PSL, Farr E et al. ‘LIANA+ provides an all-in-one framework for cell–cell communication inference.’ Nature Cell Biology 2024;26:1613–1622.

3. Faure L, Soldatov R, Kharchenko PV et al. ‘scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.’ Bioinformatics 2022;39.

4. Zhang MJ, Hou K, Dey KK et al. ‘Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data.’ Nature Genetics 2022;54:1572–1580.