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📄 New Paper Published

🌟 Our paper in Nature Communications presents a new framework for decoding temporal transcriptional dynamics from Fluorescent Timer data using an integrative combination of molecular genetics, flow cytometry, and machine learning.

Using a convolutional neural network (ConvNet)-based approach together with Foxp3-Tocky Fluorescent Timer reporter mice and CRISPR gene editing, this study reveals previously unrecognised features of Foxp3 transcriptional dynamics, including roles of CNS2 in regulating transcription frequency and age-dependent differences from neonatal to aged mice.

• Irie N, Takeda N, Satou Y, Araki K, Ono M (2025). Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer. Nature Communications 16:5720. https://doi.org/10.1038/s41467-025-61279-y 🔬🧠