The authors show that tau can be released by neurons and transferred to other neurons via the extracellular space. Moreover, they show that enhancing neuronal activity accelerates transneuronal tau propagation and exacerbates tau pathology. Tau protein can transfer between neurons transneuronally and trans-synaptically, which is thought to explain the progressive spread of tauopathy observed in the brain of patients with Alzheimer's disease. Here we show that physiological tau released from donor cells can transfer to recipient cells via the medium, suggesting that at least one mechanism by which tau can transfer is via the extracellular space. Neuronal activity has been shown to regulate tau secretion, but its effect on tau pathology is unknown. Using optogenetic and chemogenetic approaches, we found that increased neuronal activity stimulates the release of tau in vitro and enhances tau pathology in vivo. These data have implications for disease pathogenesis and therapeutic strategies for Alzheimer's disease and other tauopathies.

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell–cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer’s disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell–cell relationships. It is also a powerful framework that can be applied to general scRNA-Seq analyses.