👤 Danish Anwer

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and widespread cerebral pathology. Understanding cell-type-specific molecular mechanisms underlying AD is critical for identifying precise therapeutic targets. We applied a supervised machine learning approach to single-nucleus RNA sequencing data from the ROSMAP cohort, aggregating gene expression profiles into pseudobulk representations across six major brain cell types. Systematic evaluation of all possible cell-type combinations identified microglia and astrocytes as the most discriminative cell types for AD classification. A logistic regression model trained on 228 highly variable genes achieved robust classification performance on held-out ROSMAP samples (balanced accuracy 0.87, AUC 0.89) and generalized to an independent cohort from the Seattle Alzheimer's Disease Brain Cell Atlas (balanced accuracy 0.86, AUC 0.92), demonstrating cross-cohort reproducibility that remains uncommon in computational AD research. Among the 72 genes selected by the model, microglial PTPRG exhibited the highest absolute coefficient. Gene Set Enrichment Analysis (GSEA) revealed that microglia-expressed genes were enriched for chronic immune activation and inflammatory signaling, while astrocyte-associated genes implicated protein homeostasis stress and HSF1-mediated chaperone pathways. Weighted Gene Co-expression Network Analysis (WGCNA) further showed that PTPRG operates within fundamentally different gene network contexts in AD and NCI microglia, with AD networks characterized by inflammatory dysregulation and NCI networks reflecting homeostatic immune surveillance. Cell-cell communication analysis identified established AD risk genes including APOE, GRN, PSEN1, and CLU among the top neuronal ligands predicted to regulate microglial PTPRG, positioning it as a convergence point for disease-relevant neuronal signals. Correlation analysis further revealed that excitatory and inhibitory neurons couple to microglial PTPRG through distinct biological processes, implicating divergent mechanisms of AD-associated microglial dysregulation. Collectively, these findings establish microglial PTPRG as a central hub integrating neuronal signaling and inflammatory dysregulation in AD pathology. Show less

Alzheimer's disease contributes to 60-70% of all dementia cases in the general population. Belonging to the BIN1/amphiphysin/RVS167 (BAR) superfamily, the bridging integrator (BIN1) has been identified to impact two major pathological hallmarks in Alzheimer's disease (AD), i.e., amyloid beta (Aβ) and tau accumulation. Aβ accumulation is found to increase by BIN1 knockdown in cortical neurons in late-onset AD, due to BACE1 accumulation at enlarged early endosomes. Two BIN1 mutants, KR and PL, were identified to exhibit Aβ accumulation. Furthermore, BIN1 deficiency by BIN1-related polymorphisms impairs the interaction with tau, thus elevating tau phosphorylation, altering synapse structure and tau function. Even though the precise role of BIN1 in the neuronal tissue needs further investigation, the authors aim to throw light on the potential of BIN1 and unfold its implications on tau and Aβ pathology, to aid AD researchers across the globe to examine BIN1, as an appropriate target gene for disease management. Show less