Our fun, collaborative, and interdisciplinary group uses advanced single-molecule fluorescence microscopy to study the molecular machinery of living cells. Watching the motion of individual protein molecules within cell nuclei lets us probe how they interact with DNA and other proteins. We can use the resulting single-molecule tracking datasets to gain insight into how regulatory proteins interact with each other to turn genes on and off. By pursuing side-by-side advances in genetic engineering, microscopy, and data analysis, we are seeking to explore more deeply the inner molecular world of the cell.
Single-molecule fluorescence microscopy allows us to watch tens of thousands of molecules and track their motions in hundreds of cells per experiment. A challenge is obtaining useful information from these large datasets to learn how these molecules function. This project will build upon and streamline our existing software tools for analysis and visualization of single-molecule tracking (SMT) data. We would like to develop a graphical data browser and/or set of Jupyter notebooks to allow users to interact more easily with SMT data, perform manual or automated quality control on large high-throughput datasets, and discern trends in data (e.g., changes in response to drug treatment or biologically meaningful differences between cells).
