ArticleAlzheimer's & dementia (New York, N. Y.)
A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A pilot study.
Article in Alzheimer's & dementia (New York, N. Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe current biomarker framework for the diagnosis and staging of Alzheimer's disease (AD) relies mainly on neuropathological features; thus, its performance for diagnosis is limited prior to the initiation of neurodegeneration. Here, we leveraged transcriptomic data to develop a new framework for omic-informed blood-based diagnostic biomarkers for AD from an early stage.
methodsMicroglial gene expression from single nucleus RNA sequencing (snRNA-seq) data was analyzed via six statistical methods to identify candidate panels of genes predictive of AD. A total of 78 gene panels, 30 to 2000 genes in size, were selected and evaluated for their ability to distinguish AD patients from controls. Three top-ranked panels of 300, 50, and 30 genes were transferred to blood (monocyte) transcriptomic data obtained from living subjects via a graph-based mapping approach based on optimal transport statistics.
resultsThe 300-panel method resulted in an area under the curve (AUC) of 0.7 and moderate accuracy (75%) in classifying AD; however, the accuracy in predicting cognitively normal patients was lower (53%). While the 300 genes provided high accuracy, inspection of the distribution of
conclusionsIntegrating multiomics datasets into the AD biomarker discovery pipeline offers a powerful modality to increase precision and comprehensiveness in AD research and clinical applications.
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