Atherosclerosis—a chronic inflammatory disease of the artery wall—is the underlying cause of human coronary heart diseases. Cells within atherosclerotic lesions are heterogeneous and dynamic. Their pathological features have been characterized by histology and flow cytometry and more recently, by bulk-tissue omics profiling. Despite this progress, our knowledge of cell types and their roles in atherogenesis remains incomplete because of masking of differences across cells when using genomic measurement at bulk level. Single-cell RNA sequencing (scRNA-seq) has catalyzed a revolution in understanding of cellular heterogeneity in organ systems and diseases. This project applies scRNA-seq to define the genetic influences on cell subpopulations and functions in atherosclerotic lesion of transgenic mice for candidate risk genes of human coronary heart diseases as inspired by human genomic discoveries. The students involved in this project are expected to work on: (1) analysis of scRNA-seq data using R/Bioconductor packages; (2) Interpretation of the data using pathway and network analysis. Some relevant workflows are available through the “Resources” page of our lab website at https://hanruizhang.github.io/zhanglab/.

One selected candidate will receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

  • Professor: Hanrui Zhang
  • Department/School: Medicine
  • Location: P&S 10-401
  • The Zhang laboratory is interested in studying the role of macrophages in cardiometabolic diseases using human iPS cells, CRISPR gene editing and screening, and functional genomic approaches including next-generation sequencing.

Project Timeline

  • Earliest starting date: 10/15/2019
  • End date: 5/31/2020
  • Number of hours per week of research expected during Fall 2019: ~4

Candidate requirements

  • Skill sets: R and Bioconductor; Python
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Additional comments: Interested in bioinformatics in a biomedical research setting.