Aberrant protein aggregation is a pathological cellular hallmark of many neurodegenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), where the tau protein is aggregating, forming neurofibrillary tangles (NFTs), and propagating from neuron to neuron. These processes have been linked to disease progression and a decline in cognitive function. Various therapeutic approaches aim at the prevention or reduction of tau aggregates in neurons. Human induced pluripotent stem cells (hiPSCs) are a very valuable tool in neuroscience discovery, as they offer access to potentially unlimited amounts of cell types that are affected in disease, including cortical neurons, for in vitro studies. We have generated an in vitro model for tau aggregation that uses hiPSC - derived neurons expressing an aggregation prone, fluorescently tagged version of the human tau protein after lentiviral transduction. Upon addition of tau seeds in the form of recombinant sonicated paired helical filaments (sPHFs), the neurons show robust, disease-like aggregation of the tau protein. The model was developed as a plate-based high content screening assay coupled with an image analysis algorithm to evaluate the impact of small molecules or genetic perturbations on tau. We show that the assay can be used to evaluate small molecules or screen targeted compound libraries. Using siRNA-based gene knockdown, genes of interest can be evaluated, and we could show that a targeted gene library can be screened, by screening nearly 100 deubiquitinating enzymes (DUBs) in that assay. The assay uses an imaging-based readout, a relatively short timeline, quantifies the extent of tau aggregation, and also allows the assessment of cell viability. Furthermore, it can be easily adapted to different hiPSC lines or neuronal subtypes. Taken together, this complex and highly relevant approach can be routinely applied on a weekly basis in the screening funnels of several projects and generates data with a turnaround time of approximately five weeks.

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http://dx.doi.org/10.1016/j.slasd.2023.12.009DOI Listing

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