The brain relies on an enormous diversity of local interneurons to form specialized circuits with remarkable processing capacities. Yet how different interneuron types emerge during development and integrate into functional circuits remains unclear.
Using genetic fate mapping strategies combined with statistical and machine learning-based methods, our goal is to elucidate the intrinsic and extrinsic factors that drive cellular decision-making during interneuron development. We are using high-throughput single cell RNA-sequencing methods to reconstruct developmental trajectories and are building an integrated framework to understand how a cell’s spatial localization, epigenomic landscape, parental lineage and neural network activity influence its behavior and fate.
A growing body of evidence suggests that defects in interneuron development are associated with epilepsy, autism, bipolar disorder, schizophrenia and other complex neuropsychiatric diseases. Therefore, elucidating the molecular mechanisms regulating interneuron development is crucial for understanding how the brain operates in both health and disease.