A deep-learning microglial response score integrates single-cell and spatial transcriptomics for Alzheimer’s disease stratification
Year: 2026
Type: article
Abstract: Background Microglia exhibit diverse responses in Alzheimer’s disease (AD), yet linking these responses to tissue pathology and disease heterogeneity remains challenging. We aimed to develop a deep-learning microglial response score (DL-MRS) integrating single-cell, spatial and bulk transcriptomics to characterize AD-associated transcriptional patterns. Methods We analyzed human single-cell, spatial and bulk transcriptomic data to characterize microglial clearance and inflammatory programs. These programs and tissue-context features formed eight module-level inputs to a multilayer perceptron that generated DL-MRS. Model performance and feature contributions were assessed using five repeats of five-fold cross-validation, cross-cohort testing, feature-set comparisons and SHapley Additive exPlanations (SHAP) analysis. Spatial localization, individual-level prediction using spatial transcriptomic data, and molecular heterogeneity within AD were also examined. Results Single-cell analyses characterized clearance-enriched and inflammation-enriched microglial groups. Descriptive spatial projection visualized regional variation in the weighted microglial and tissue-context programs. Across 697 AD and control bulk samples, DL-MRS achieved a repeated out-of-fold area under the receiver-operating characteristic curve (AUC) of 0.939 and a mean cross-cohort transfer AUC of 0.947. SHAP analysis of the expanded model identified complement as the largest contributor, alongside contributions from microglial and tissue-context modules. Among 129 AD samples, median DL-MRS was higher in the immune-enriched than in the neuronal/synaptic-enriched molecular group (0.970 versus 0.800). These findings support DL-MRS as an interpretable research tool for integrating microglial and tissue-context signals to characterize AD-associated transcriptional patterns and within-disease molecular heterogeneity. Conclusion DL-MRS integrates microglial and tissue-context signals into an interpretable measure of AD-associated transcriptional remodeling. By capturing coordinated variation across immune, glial, vascular, neuronal and synaptic programs, DL-MRS reveals molecular heterogeneity among AD samples and provides a framework for investigating interindividual differences and advancing molecular stratification in AD research.
Source: Frontiers in Neuroscience
Authors: Lei Ren, Wenhao Ouyang, Zijia Lai, Zijin Gong, Li Ling
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225632168