Biology subjects

Wagai, F.

Publications and source records attributed to Wagai, F..

2 recordsLinked to original sources

bioRxiv2024

Reconstruction of functional olfactory sensory tissue from embryonic nasal stem cells

During the development of the olfactory epithelium (OE), olfactory sensory neurons (OSNs) express only one member of the odorant receptor (OR) gene family, and OSNs expressing the same OR converge their axons to the same set of glomeruli on the olfactory bulb (OB). The resulting odor maps allow mice to discriminate more than 100,000 different odorants using about 1,000 ORs. It remains elusive how odor maps are formed. Here, we show a means of forming OE organoids with pseudostratified structure from mouse embryonic OE stem cells. Single-cell RNA sequencing revealed that the OE organoids give rise to all the OE cellular lineages and undergo active neurogenesis. We also found that most OSNs in OE organoids exclusively express only one type of ORs and exhibit a unique molecular code of axon guidance-related genes that can discriminate between OR classes. Thus, OE organoids could be a useful model for studying olfactory nervous system development.

developmental biology↗

bioRxiv2023

Inference of gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation (scCRISPR) has enabled the inference of gene regulatory networks (GRNs) with causal relationships. However, a snapshot of scCRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a new computational method that infers GRNs using a time-series scCRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its GRN. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring GRNs. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a GRN consistent with multiple databases and literature. Accurate inference of GRNs by RENGE would enable the identification of key factors for various biological systems.

bioinformatics↗