{
  "abstract": "BackgroundMicroglia 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.MethodsWe 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.ResultsSingle-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.ConclusionDL-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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Lei Ren"
    },
    {
      "name": "Wenhao Ouyang"
    },
    {
      "name": "Zijia Lai"
    },
    {
      "name": "Zijin Gong"
    },
    {
      "name": "Li Ling"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8851amfcpf5dc.bib",
  "cited_by": 0,
  "doi": "10.3389/fnins.2026.1935678",
  "fulltext_url": null,
  "id": "8851amfcpf5dc",
  "issns": [
    "1662-453X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1935678",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "biology/neuroscience"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-09",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/8851amfcpf5dc.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:30Z",
      "native_id": "10.3389/fnins.2026.1935678",
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      "url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1935678"
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      "url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1935678/full"
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  "subjects": [
    {
      "field": "biology",
      "origin": "feed-journal:biology/neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "A deep-learning microglial response score integrates single-cell and spatial transcriptomics for Alzheimer’s disease stratification",
  "title_text": "A deep-learning microglial response score integrates single-cell and spatial transcriptomics for Alzheimer’s disease stratification",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T18:20:16Z",
  "url": "https://research.draconitic.com/paper/8851amfcpf5dc",
  "venue": "Frontiers in Neuroscience",
  "versions": []
}