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Integrative single-cell and cell-free plasma RNA transcriptomics identifies biomarkers for early non-invasive AD
Li Wu1,2, Renxin Zhang1,2, Yichao Wang1,2
1State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, China.
Introduction:
Data-driven omics approaches have rapidly advanced our understanding of the molecular heterogeneity of Alzheimer's disease (AD). However, limited by the unavailability of brain tissue, there is an urgent need for a non-invasive tool to detect alterations in the AD brain. Cell-free RNA (cfRNA), which crosses the blood-brain barrier, could reflect AD brain pathology and serve as a diagnostic biomarker.
Methods:
Here, we integrated plasma-derived cfRNA-seq data from 337 samples (172 AD patients and 165 age-matched controls) with brain-derived single cell RNA-seq (scRNA-seq) data from 88 samples (46 AD patients and 42 controls) to explore the potential of cfRNA profiling for AD diagnosis. A systematic comparative analysis of cfRNA and brain scRNA-seq datasets was conducted to identify dysregulated genes linked to AD pathology. Machine learning models-including support vector machine, random forest, and logistic regression-were trained using cfRNA expression patterns of the identified gene set to predict AD diagnosis and classify disease progression stages. Model performance was rigorously evaluated using area under the receiver operating characteristic curve (AUC), with robustness assessed through cross-validation and independent validation cohorts.
Results:
Notably, we identified 34 dysregulated genes with consistent expression changes in both cfRNA and scRNA-seq. Machine learning models based on the cfRNA expression patterns of these 34 genes can accurately predict AD patients (the highest AUC = 89%) and effectively distinguish patients at early stage of AD. Furthermore, classifiers developed based on the expression of 34 genes in brain transcriptome data demonstrated robust predictive performance for assessing the risk of AD in the population (the highest AUC = 94%).
Discussion:
This multi-omics approach overcomes limitations of invasive brain biomarkers and noisy blood-based signatures. The 34-gene panel provides non-invasive molecular insights into AD pathogenesis and early screening. While cfRNA stability challenges clinical translation, our framework highlights the potential for precision diagnostics and personalized therapeutic monitoring in AD.
