Cell tracking is crucial for understanding the complex patterns of cellular migrations 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 <100 Bq droplets and cells, in phantoms and in vivo. We found that while background count and multiplexing effects impact static source tracking, sensitivity remains critical for dynamic tracking of moving sources. We successfully localized multiple single cells in vivo, 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.