Overview
The mammalian brain is one of the most complex tissues in the body, comprising multiple distinct cell types with high spatial and cellular heterogeneity. While single-cell transcriptomics has revealed a strong link between aging and transcriptomic alterations, most of what is known comes from steady-state RNA abundance measurements using short-read sequencing, which cannot resolve individual RNA isoforms or capture the underlying changes in transcription, splicing, processing, and decay. This leaves the cell-type-specific regulation of RNA metabolism across the lifespan largely uncharacterized.
To bridge this gap, the lab combines long-read direct RNA sequencing, single-cell sequencing, and spatial transcriptomics to establish the most detailed view of the aging brain transcriptome. By profiling full-length RNA molecules at single-cell resolution, the lab can simultaneously measure transcription start-site selection, alternative splicing, polyadenylation, poly(A) tail length, and RNA modifications — all at the isoform level. Statistical modeling and machine learning are used to integrate these multi-dimensional measurements and identify post-transcriptional modulators of aging, with the goal of uncovering novel regulatory mechanisms and potential intervention points.