Research
Research in our group uses statistical analysis and machine learning approaches to understand asthma at both mechanistic and population health levels. Asthma is a chronic disease of the lungs that affects an estimated 1 in 20 people worldwide, and in the USA it is also amongst the diseases with the largest disparities driven by race and socioeconomic class. Our research therefore pursues two directions: applying mucosal immunology to understand its molecular mechanisms to work towards improved diagnosis and treatment, and understanding its environmental causes to prevent disease-causing exposures which perpetuate disparities.
Molecular mechanisms: Single-cell analysis of airway mucosa.
Our research aims to improve understanding of inflammatory signaling in the lung that causes or exacerbates asthma. To investigate this, the group applies and develops novel computational methods to analyze ‘multi-omics’ data, particularly single-cell RNA-sequencing. We are currently focused on the role of airway epithelial cells in responding to environmental exposures to triggers such as allergens and air pollution.
This work has so far uncovered two previously unknown subtypes of ‘tuft’ cells present in both mouse airway and small intestine and led to the first extensive single-cell characterization of the small intestinal epithelium in mice. In the airway epithelium we also identified the pulmonary ionocyte, a new cell type that will have implications for understanding the regulation of mucus secretion and its role in asthma and cystic fibrosis. There are many open questions about the role of these rare types of cells, and we can use single-cell and other genomics approaches to map the network of intercellular signaling that allows rare cells to regulate the entire airway in the healthy lung and in asthma.
Environmental causes: housing-related exposures can trigger asthma.
Poor quality housing has been repeatedly identified as a critical source of exposures that can trigger or exacerbate asthma. Rates of respiratory infections and asthma are markedly higher in poor neighborhoods and in communities of color. A 2012 report from the National Center for Health Statistics described a 75% higher death rate from asthma (per 1000 people) among black people compared to white, and a 53% higher prevalence of asthma among people with incomes below the poverty level than those with incomes at least 200% of the poverty level. To map the role of housing in these disparities, our research applies machine learning approaches to population-level observations of indoor environmental exposures in order to identify poor-quality housing that contributes to disparities in exposure and disease burden.