UC Irvine

Complex traits are intricately intertwined with an organism's genome, a relationship underscored by the dynamic landscape of its transcriptome. Selective gene expression regulates cell type specialization and fluctuation of cell states. The development of RNA sequencing has facilitated the capture of the whole transcriptome of a given sample. However, a bulk approach obscures cell type heterogeneity, impeding the precise dissection of cell-specific effects, including those modulated by genotype, developmental stage, and disease state. In contrast, single-cell and single-nucleus RNA-seq preserves cellular identity, enabling a comprehensive mapping of gene expression across various cell types and states.

Here, I describe my work in single-cell transcriptomics to characterize cell types and cell states in mouse. First, I present our long-read single-cell RNA-seq method, benchmarked in the C2C12 mouse myogenic system, which revealed cell type-specific isoform switching in key genes during myogenesis. Next, I characterize 5 mouse tissues at single-nucleus resolution during postnatal development using the ENCODE4 mouse dataset, where I used topic modeling to reveal cell type- and state-specific cellular programs. Lastly, I investigate the impact of genetic variation on gene expression across 8 diverse tissues from 8 mouse genotypes, pinpointing genotype- driven variation in specific celltypes in both wild-derived and classical lab strains. Together, these projects lay the groundwork for cohesive cell type and cell state annotation and comparative analyses, contributing to future characterization of these tissues in other contexts such as human diseases and hybrid mouse genotypes.