Biology subjects

Wakui, T.

Publications and source records attributed to Wakui, T..

2 recordsLinked to original sources

bioRxiv2025

Human aged astrocytes induce neurotoxicity in response to inflammatory stimuli

Astrocytes play a critical role in neuroinflammation and the pathogenesis of neurodegenerative diseases. Here we found that human induced pluripotent stem cell (iPSC)-derived astrocytes responded differently to inflammatory triggers compared to rodent astrocytes, showing increased neurotoxicity when exposed to TNF- and IFN-{gamma}. Furthermore, astrocytes with senescent features showed even higher levels of neurotoxicity in the presence of TNF- and IFN-{gamma}, suggesting a potential link between aging and neurodegenerative diseases. It was also demonstrated that LPS-activated neuron/astrocyte/microglia tri-culture produced TNF-, leading to neurotoxicity in the tri-culture when IFN-{gamma} was present. Through compound screening, we identified Janus kinase inhibitors capable of preventing neurotoxicity in astrocytes induced by TNF- and IFN-{gamma}, demonstrating the potential use of neurotoxic astrocytes as a platform for drug screening. These results provide insight into the complex relationship between aging, inflammation, and neurodegenerative diseases, emphasizing the potential of targeting astrocytes as a novel therapeutic approach for addressing neurodegenerative diseases.

neuroscience↗

bioRxiv2022

Predicting gene expression from cell morphology in human induced pluripotent stem cells

Purification is essential before differentiating human induced pluripotent stem cells (hiPSCs) into cells that fully express particular differentiation marker genes. High-quality iPSC clones are typically purified through gene expression profiling or visual inspection of the cell morphology; however, the relationship between the two methods remains unclear. We investigated the relationship between gene expression levels and morphology by analyzing live-cell phase-contrast images and mRNA profiles collected during the purification process. We employed this data and an unsupervised image feature extraction method to build a model that predicts gene expression levels from morphology. As a benchmark, we confirmed that the method can predict the gene expression levels from tissue images for cancer genes, performing as well as state-of-the-art methods. We then applied the method to iPSCs and identified two genes that are well-predicted from cell morphology. Although strong batch effects resulting from the reprogramming process preclude the ability to use the same model to predict across batches, prediction within a reprogramming batch is sufficiently robust to provide a practical approach for estimating expression levels of a few genes and monitoring the purification process.

bioengineering↗