UC Irvine

The advent of single-cell sequencing has allowed us to simultaneously capture transcripts in millions of cells, providing the opportunity to dissect important biological regulatory mechanisms at an unprecedented resolution. Unfortunately, computational modeling of single-cell data has faced several challenges. Specifically, it is sparse with many zeros, sensitive to numerous experimental confounding factors, and complicated with many non-linear biological interactions, making it hard for computational analysis. In the following three studies –– across the tissue-cellular-DNA levels, we utilized biological information and mathematical models to address these computational challenges. Firstly, at the tissue level, we leveraged the resolution of single-cell sequencing to perform a novel cell-to-cell communication analysis to discover dysregulated communicating cell types. Secondly, at the cellular level, we performed a cell-type-specific analysis to identify key driver genes in Alcohol Use Disorder. Thirdly, at the DNA level, we developed a computational pipeline that studies virus infection that pinpoints retroviral integration sites at the genetic base pair resolution within specific cell types. By synergizing single-cell sequencing with tailored computational analyses, we pave the way for a new era in medicine, enabling physicians to practice with unparalleled insight and precision.