mRNA polyadenosine (polyA) tail lengths play a unique and critical role in controlling gene expression in the developmental transition from oocyte to embryos from worms to humans. We have recently generated a large comprehensive profile of polyA tail lengths across the mouse oocyte-to-embryo transition with Nanopore long read sequencing, capturing tail length regulation with isoform-specific resolution (including 3'UTR length, splice isoform, polyadenylation site choice, etc.). We found dynamic changes in polyA tail length across this transition and that these changes in tail length control mRNA translation and stability. But what molecular mechanisms orchestrate these changes in polyA tail length?  In this project, we will apply machine learning approaches to ask which mRNA features (number and position of specific RNA binding protein motifs, mRNA length, mRNA abundance, polyadenylation site choice, 3'UTR length, etc.) are most predictive of (1) polyA tail length at each developmental stage and (2) change in tail length across consecutive developmental stage transitions.  These analyses provide an exciting opportunity to address questions decades-old questions as to the mechanisms driving the earliest stages of mammalian development.

We developed a novel tau propagation model using 3D spheroid model that rapidly develop tau pathology and neurodegeneration in just three weeks. Single cell transcriptomics of the model reveals cell type specific changes that resemble transcriptomic signatures from Alzheimer’s disease postmortem brain.

To understand cell type specific vulnerability of Alzheimer’s disease, we utilize snRNA-seq to characterize human brain tissues from Alzheimer’s disease patients across different brain regions.