Our Research

Our lab is focused on harnessing the power of single cell and computational biology to study and model how human immune cells sense and respond to their environment.

We start by innovating new experimental and computational methods that enable the profiling and analysis of immune cells at unprecedented resolution, precision, and scale. We use these tools to systematically dissect immune circuits in health and disease. In healthy donors, we measure the natural variability in immune response and model how genetics and biological rhythms tune immune circuits to affect a person’s risk for disease or response to treatment.
In patients with infectious disease, autoimmunity, neurodegeneration and cancer, we search for common failure modes in immune circuits and identify novel targets for therapeutic intervention. Building on this work, the lab is initiating new efforts to translate our insights into design specifications that can aid the development of personalized medicines and engineered immune circuits for future therapeutic strategies.

Multiplexed Multimodal Single-Cell and Spatial Sequencing

Increasing dimensions and throughputs of single-cell genomics by multiplexing and combinatorial indexing design

Immune Cell Census of Health and Disease

Understanding genetic and environmental effects on human immunity in health and disease.

Functionalization of Immune Disease-Associated Variants

Understanding causal variants in autoimmune disease.

Genetic Architecture of Cell Autonomous and Multicellular Circuits

Mapping gene expression regulatory networks in circulating immune cells.

Interested in working at Ye Lab?

We’re seeking motivated undergraduate students, graduate students, and postdoc fellows with strong backgrounds in genetics, genomics, experimental and computational immunology to join our team. We want to hear from you, reach out to us!


Our People

We are a dynamic and multidisciplinary team with expertise in computational biology, single-cell genomics, genetics, immunology, and molecular biology.