Decoding and Engineering Cellular States in Human Disease

How do molecular programs within individual cells give rise to the complex behaviors of human tissues—and how can we intervene when these programs go awry?

The Yang Lab combines single-cell and spatial multi-omics, functional genomics, quantitative modeling, and machine learning to investigate cellular states and interactions in human disease. We are particularly interested in moving beyond descriptive molecular atlases to uncover the regulatory mechanisms, spatial organization, and dynamic interactions that shape disease progression and therapeutic response.

Our research spans cancer immunology and neurodegenerative disease, with three interconnected directions: decoding and engineering immune cell states, understanding spatial tissue ecosystems, and developing computational frameworks for high-resolution and dynamic tissue biology.

1. Decoding and Engineering Immune Cell States

Immune cells continuously adapt their molecular programs in response to signals from their tissue environment. We seek to understand the regulatory circuits that establish these states, determine their functional consequences, and ultimately learn how to manipulate them.

A major focus of our lab is dendritic-cell biology in cancer immunity. Using human tumor multi-omics, functional perturbations, and experimental models, we investigate immune states associated with response or resistance to cancer immunotherapy and identify the transcription factors, regulatory elements, and signaling pathways that control them. We are extending these discoveries toward functional perturbation and synthetic regulatory circuits designed to reprogram therapeutically important immune-cell states.

Selected Publications

2. Mapping Spatial Tissue Ecosystems

Cells do not function in isolation. Their behavior depends on where they reside, which cells surround them, and the molecular signals exchanged within local tissue niches.

We use single-cell and spatial transcriptomics, epigenomics, imaging, and multimodal profiling to reconstruct these tissue ecosystems at cellular resolution. In cancer, we study how immune, endothelial, and tumor cells organize into spatial niches associated with therapeutic response and disease progression. In neurodegenerative disease, we investigate how cell-type-specific regulatory programs and tissue organization are altered across brain regions and disease states. These studies aim to identify spatial cellular interactions that cannot be understood from dissociated cells alone.

Selected Publications

3. Building Quantitative Models of Tissue Biology

Modern spatial and single-cell technologies provide increasingly detailed snapshots of human tissues, but transforming these measurements into mechanistic understanding requires new computational frameworks.

We develop statistical, machine-learning, and mathematical approaches to extract cellular organization and dynamics from high-dimensional multimodal data. Our work includes methods for reconstructing cells from sequencing-based spatial measurements, integrating complementary spatial technologies, modeling cell-state transitions and interactions, and connecting molecular observations to tissue-scale behavior.

A long-term goal is to move from static molecular snapshots toward predictive models of tissue dynamics—models that can generate testable hypotheses about how cellular states arise, interact, and respond to perturbation.

Selected Publications