Biology subjects

Prusiner, S. B.

Publications and source records attributed to Prusiner, S. B..

2 recordsLinked to original sources

bioRxiv2021

Tau aggregates are RNA-protein assemblies that mis-localize multiple nuclear speckle components

Tau aggregates contribute to neurodegenerative diseases including frontotemporal dementia and Alzheimers disease (AD). Although RNA promotes tau aggregation in vitro, whether tau aggregates in cells contain RNA is unknown. We demonstrate in cell culture and mouse brains that both cytosolic and nuclear tau aggregates contain RNA, with enrichment for snRNAs and snoRNAs. Nuclear tau aggregates colocalize with and alter the composition, dynamics, and organization of nuclear speckles, which are membraneless organelles involved in pre-mRNA splicing. Moreover, several nuclear speckle components, including SRRM2, mislocalize to cytosolic tau aggregates in cells, mouse brains, and patient brains with AD, frontotemporal dementia (FTD), and corticobasal degeneration (CBD). Consistent with these alterations we observe the presence of tau aggregates is sufficient to alter pre-mRNA splicing. This work identifies tau alteration of nuclear speckles as a feature of tau aggregation that may contribute to the pathology of tau aggregates.

molecular biology↗

bioRxiv2021

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics

In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations may hinder the use of certain fluorescent markers. Here, we present a deep learning method for overcoming this limitation. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimers disease (AD) from high-content high-throughput screening (HCS). The ML method identified novel compounds that effectively blocked tau aggregation, which would have been missed by traditional screening approaches unguided by ML. The method improved triaging efficiency of compound rankings over conventional rankings by raw image channels. We reproduced this ML pipeline on a biologically independent cancer-based dataset, demonstrating its generalizability. The approach is disease-agnostic and applicable across diverse fluorescence microscopy datasets.

pharmacology and toxicology↗