In Vivo Positron Emission Particle Tracking (PEPT) of Single Cells Using an Expectation Maximization Algorithm

Cell tracking is crucial for understanding the complex patterns of cellular migration that underlie many physiological, pathological, and therapeutic processes. Positron emission particle tracking (PEPT) is a method that uses list-mode positron emission tomography (PET) data to localize moving particles non-invasively inside opaque systems. However, while the application of this method to in vivo cell tracking has previously been evoked, its implementation has been limited to tracking one cell at a time. This study investigates the feasibility of tracking multiple cells simultaneously using a recently developed expectation maximization (EM) algorithm called PEPT-EM. The primary challenge to the translation of this algorithm towards biomedical applications is the low radioactivity of the cells being tracked. We experimentally demonstrated the performance of the PEPT-EM algorithm using a preclinical PET scanner for tracking droplets and cells with activities ranging from tens to hundreds of Bq, in phantoms and in a murine model. We found that while background and multiplexing effects impact static source tracking, sensitivity is critical for dynamic tracking of moving sources. We successfully localized multiple single cells in a murine model, moving at speeds up to 25 mm/s, marking the first use of PEPT-EM for such applications. Our findings highlight the exciting potential of PEPT for real-time, high throughput tracking of multiple single cells in vivo, paving the way for studying cell tracking in biological systems.