Regenerative therapies based on stem cell transplantation hold great clinical promise, particularly for patients with damaged hearts and end-stage heart failure. Imaging plays an important role in these therapies by tracking the fate of the transplanted cells. However, existing imaging methods can only visualize where large groups of transplanted cells accumulate and not how single cells arrive there. The limited capabilities of these imaging tools prevent us from fully understanding the fate and dynamic behavior of single transplanted cells. Improving the efficiency and functional outcomes of cell-based therapies is a long-term goal of cardiovascular research. Our objective in this proposal is to develop a new imaging approach that can follow single transplanted cells in real time as they home to sites of cardiac injury. Our hypothesis is that, under certain conditions, micro positron emission tomography (microPET) can track single transplanted cells in live mice, with sub-millimeter spatial resolution and sub-second time resolution. These capabilities of microPET have not been explored yet because previous studies have exclusively relied on conventional reconstruction algorithms to track radiolabelled cells. By leveraging the knowledge that only a sparse set of cells contain radioactivity, it is possible to formulate an algorithm for reconstruction single cell trajectories from microPET measurements with much higher sensitivity and spatial resolution. The rationale for the proposed research is that tracking single cells in vivo at the whole-body level will allow for far more detailed insight into the dynamic behavior and fate of transplanted cells and their interaction with injured heart tissue than conventional bulk-tissue techniques. Our preliminary simulations indicate that a single cell labeled with 1-10 Bq can be tracked with microPET in vivo. We will test our hypothesis by pursuing the following two aims: (1) Expand trajectory reconstruction to track multiple single cells in parallel;and (2) Optimize cell labeling and track single cells in vivo. In the first aim, we will implement a new reconstruction algorithm that takes raw list-mode data from a small-animal PET scanner and reconstructs spatiotemporal trajectories for multiple single cells, in parallel. In the second aim, we will optimize radionuclie labeling of bone-marrow mononuclear cells to achieve 10 Bq/cell. We will then track the homing of these single cells to sites of cardiac injury in a mouse model of myocardial infarction. The reconstruction algorithm we plan to develop is innovative because, unlike previous algorithms, it accounts for the fact that radioactivity is only contained within the transplanted cells;thus it ues all available measurements in an optimal way. The proposed research project is significant because reconstructing the spatiotemporal trajectory of transplanted cells at the whole-body level would provide unprecedented information on the dynamics of cell trafficking in living organisms and could be applied to improve cell transplantation protocols for more efficient cell engraftment.

Public Health Relevance

Cell-based therapies have the potential to substantially improve the treatment of heart disease, neurological disorders, cancer, and diabetes. This project is relevant to public health because it seeks to establish a new tool for tracking what happens to single cells once they are transplanted into a patient or a small-animal model of human disease.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-A (50))
Program Officer
Lin, Sara
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code
Kiru, Louise; Kim, Tae Jin; Shen, Bin et al. (2018) Single-Cell Imaging Using Radioluminescence Microscopy Reveals Unexpected Binding Target for [18F]HFB. Mol Imaging Biol 20:378-387
Ouyang, Yu; Kim, Tae Jin; Pratx, Guillem (2016) Evaluation of a BGO-Based PET System for Single-Cell Tracking Performance by Simulation and Phantom Studies. Mol Imaging 15:
Lee, Keum Sil; Kim, Tae Jin; Pratx, Guillem (2015) Single-cell tracking with PET using a novel trajectory reconstruction algorithm. IEEE Trans Med Imaging 34:994-1003