Shuonan Chen

The paper identifies topographic organization in locus coeruleus norepinephrine neurons, linking their morphology, gene expression, and activity to specific brain regions and learning-related behaviors.

This study develops blind demixing methods and a neural network to recover neuronal morphology from barcode imaging data, showing accurate recovery is feasible with sufficient barcode signal density.

BarDensr demixes spatial transcriptomics data by modeling the imaging process and using convex optimization, achieving state-of-the-art signal recovery in dense or low-resolution data.

The paper uses scRNA-seq to analyze alternative splicing regulation across neuronal cell types, revealing distinct splicing programs and RBPs involved in graded regulation.