Adipose tissue secretes cytokines to regulate essential functions, but comprehensive study is prevented by difficult-to-access depots such as visceral epicardial adipose tissue and differences between donor sources and methods to generate cell lines. This project will start with an isogenic population of cultured human preadipocytes and reprogram them to specific types of adipocytes using a combinatorial process analogous to that of induced pluripotent stem cells. This project includes working with single cell (sc)-RNAseq and sc-ATACseq datasets using XGBoost and other models.

Type 2 Diabetes (T2D) complications cause morbidity and mortality, but occur heterogeneously among those at risk and thus are difficult to predict. Previous studies to identify individuals at risk of diabetic complications focus on single timepoint data for a few features and do not examine phenotypic variables over time. This project will analyze multiple longitudinal clinical phenotypes to identify clusters of individuals at risk of diabetic complications.

Adipose tissue secretes cytokines to regulate essential functions, but comprehensive study is prevented by difficult-to-access depots such as visceral epicardial adipose tissue and differences between donor sources and methods to generate cell lines. This project will start with an isogenic population of cultured human preadipocytes and reprogram them to specific types of adipocytes using a combinatorial process analogous to that of induced pluripotent stem cells. This project includes working with single cell (sc)-RNAseq and sc-ATACseq datasets using XGBoost and other models.

Our research focuses on understanding how genetic polymorphisms influence disease by affecting transcriptomic and epigenomic profiles in the immune system. However, most of these genetic polymorphisms are located within the non-coding regions of the genome, which are areas that do not directly code for proteins. This makes their interpretation particularly challenging, as their functional role is not immediately apparent. We use next-generation sequencing technologies to identify genetic polymorphisms associated with molecular traits, like changes in gene expression, and leverage information from epigenomic data (ChIP-seq, ATAC-seq, etc.) to pinpoint disease mechanisms.

Identifying genetic variants associated with transcription factor binding. We have implemented a published linear regression model to analyze the allele frequency within DNA, enriched using chromatin immuno-precipitation techniques for variant discovery in primary human immune cell subsets. The relevance of this research is to narrow down the molecular mechanism by which non-coding genetic variants may influence gene expression, contribute to complex phenotypes and influence an individual's susceptibility to disease.

We have assembled a large cohort of donors with various head&neck cancers, who have kindly provided matched tumorigenic and healthy adjacent tissue samples. We have profiled a set of immune cells from these samples, namely T cells, which have been extensively shown to play an important role in cancer immunotherapy. Analyses of the healthy adjacent tissue samples are undergoing, and we will be reporting on a comprehensive T-cell atlas of the head&neck compartments. Yet, there remain massive data from the tumor-related side of the project to be analyzed. These data include single-cell RNA-seq, surface protein expression and T-cell receptor (TCR)-seq modalities, all spanning multiple anatomical sites as well as different types of head&neck cancers.