# 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.

## Key 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.