Ovarian cancer is the primary cause of mortality resulting from gynecological cancer, and frequently presents at a late stage when it has metastasized throughout the peritoneal cavity. There has been significant progress in the development of new chemotherapeutic strategies to target these metastatic tumors, however pre-clinical studies suffer from an inability to non-invasively and accurately measure the tumor burden. We propose to develop a new imaging technique that combines two emergent and complementary technologies, Magnetic Particle Imaging (MPI) and Magneto- Endosymbionts (MEs). The former is a new type of imaging scanner that enables deep- tissue imaging of iron with zero background, making it valuable in cases where MRI would struggle due to an inability to differentiate iron from normal anatomic features. The latter is a new class of contrast agent for labeling and tracking cells, based on iron-rich magnetotactic bacteria, which has the potential to solve a long-standing issue of contrast agent dilution through cell division. The combination will allow preclinical tumor models based on injection of exogenous tumor cells to be tracked in vivo for several cell generations throughout the body, irrespective of local physiology. First we plan to optimize the MPI hardware to adapt it to the specific properties of magnetotactic bacteria (Aim 1), by changing the excitation frequency (to take into account the longer rotational times of magnetosomes) and detection electronics (to improve sensitivity). Concurrently, we will genetically engineer MEs to produce magnetosomes (iron containing organelles) which are at the optimal size and shape required for efficient MPI (Aim 2). Preliminary results suggest that with these modifications it should be possible to quantitatively monitor cancer implantation, growth, metastases, and pharmaceutical response in a mouse animal model of ovarian cancer, and we plan to evaluate this in Aim 3. We expect that a combination of MRI (for an anatomic reference) and MPI (for high sensitivity detection of the iron) will produce information-rich in vio image data that can be used to quantify metastatic ovarian tumor burden, and which will also be applicable for other preclinical cell tracking applications involving stem or tumor cells.