Integrative single-cell and cell-free plasma RNA transcriptomics identifies biomarkers for early non-invasive AD screening

Li WuRenxin ZhangYichao WangShaoxing Dai^*Naixue Yang^*

• Yunnan Key Laboratory of Primate Biomedical Research, Kunming University of Science and Technology, Kunming, China

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), as circulating nucleic acids, can traverse the blood-brain barrier and be released from the brain into the blood. Here, we integrated blood-derived cfRNA-seq data from 337 samples (172 AD patients and 165 age-matched control) with brain-derived single cell RNA-seq (scRNA-seq) data from 88 samples (46 AD patients and 42 control) to explore the potential of cfRNA profiling for AD diagnosis. Systematic profiling of cfRNA revealed its capability to facilitate non-invasive detection of pathological features of AD, including cell-type-specific signatures in AD brain. Notably, we identified 34 dysregulated genes with consistent expression changes in both cfRNA and scRNA-seq from AD patients. 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. Futhermore, 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%). These findings suggests that the 34 genes have the potential to serve as biomarkers for early non-invasive screening of AD.