Dr. Christoph A. Thaiss is an Assistant Professor of Pathology at Stanford University. His lab studies how interactions between environment, body, and brain impact physiology and disease over the lifespan. Christoph received his undergraduate training from the University of Bonn, Yale University, ETH Zurich, and the Broad Institute of MIT and Harvard. Following his Ph.D. studies at the Weizmann Institute of Science, he joined the faculty at the University of Pennsylvania.
Cellular replication is a defining feature of life. But how do cells reproduce themselves? Dr. Christine Jacobs-Wagner's laboratory addresses this fundamental question by probing the governing principles and the spatiotemporal mechanisms that underlie cellular replication, with an emphasis of cell morphogenesis, cell growth, chromosome dynamics and cell cycle regulation. They use bacteria as model systems for two main reasons. First, bacteria lack the complex control systems of eukaryotes (e.g., cyclin/Cdk machinery); yet their multiplication process is remarkably efficient and faithful.
The Kuo lab studies a group of airway epithelial cells called pulmonary neuroendocrine (NE) cells. They are normally a rare cell type in the lung with sensory, neurosecretory, and stem cell functions. Some NE cells can be located at branchpoints in highly innervated clusters called neuroepithelial bodies (NEBs), which are abnormal in several pediatric and adult respiratory diseases. The lab uses single cell genetic approaches to study NE cell development and function.
Dr. Charles Gawad's lab works at the interface of biotechnology, computational biology, cellular biology, and clinical medicine to develop and apply new tools for characterizing genetic variation across single cells within a tissue with unparalleled sensitivity and accuracy. They are focused on applying these technologies to study cancer clonal evolution while patients are undergoing treatment with the aim of identifying cancer clonotypes that are associated with resistance to specific drugs so as to better understand and predict treatment response.
