Project description

Cystic fibrosis (CF) is a genetic disorder caused by mutations in a gene called CFTR. This gene produces a protein (also called CFTR) that is an ion channel in the epithelial cells that line all the tubes in the body other than blood vessels. While lots of organs are affected, it is the airway disease that causes poor quality of life and early death. The CFTR ion channel defect results in dehydration of the airway surface liquid (ASL) and the inability to clear mucus via the normal clearance mechanism called mucociliary clearance (MCC). This creates the perfect environment for inhaled bacteria to grow in, causing a cycle of infection and inflammation that destroys lung tissue. Our group aims to develop a gene therapy for CF lung disease, and we have developed novel approaches for delivering a correct copy of the CFTR gene into airway cells. For many of our experiments we use a reporter gene such as LacZ, rather than the therapeutic CFTR gene. After the lung tissue is collected it can be processed so that cells expressing the LacZ gene turn blue. This allows us to easily see which cells have been altered. An example of a rat trachea containing blue/green LacZ positive cells is shown here. The ability to quantify the blue LacZ stained area, and to assess the patterns of transduction, is essential. The aim of this project is to apply image processing techniques and machine learning algorithms to develop better ways of quantifying LacZ transduction. This may include the generation of a graphical user interface to allow researchers to interact with the data in a quick and easy-to-use manner.

Assumed knowledge

Matlab and basic image processing knowledge

Supervisors research focus

The Cystic Fibrosis Airway Research Group (CFARG) and Respiratory X-ray Imaging Laboratory (ReXIL) are located in Respiratory Medicine in the Gilbert Building at the Women’s and Children’s Hospital. We are dedicated to developing gene-based therapies for CF lung disease, as well as novel imaging-based methods for assessing the effectiveness of therapies.

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