The urgent need for safe and effective treatments for COVID-19 has fueled the launch of many parallel complex studies of cellular therapies with small to modest enrolment projections. By pooling data from multiple studies that are similar, we can increase the ability to achieve sufficient power to determine effectiveness more quickly through meta-analysis. A scoping review of registered clinical trials using cell-based interventions for COVID-19 was conducted to identify candidate studies for meta-analysis that could support an accelerated regulatory review. ClinicalTrials.gov and WHO International Clinical Trials Registry Platform were searched April 23, 2020. Trials were included if they utilized cell or cell-derived products to treat or prevent COVID-19. Fifty-four registered cellular therapy trials were identified and included for analysis. Studies of mesenchymal stromal cells (MSCs; 41 studies; 1129 subjects projected to receive cells) and natural killer (NK) cells (5 studies; 135 projected to received cells) were observed most commonly. A subset of studies are controlled (34 studies, or 63%), including 27 studies of MSCs and 3 of NK cells. While heterogeneity in study design exists, the cumulative projected enrolment of patients from similar studies appears sufficient to allow the detection of meaningful differences in clinically important outcomes such as mortality, admission to intensive care and need for mechanical ventilation by September 2020-sooner than any individual study could determine effectiveness. MSCs are the predominant cell type in registered trials for severe or critical COVID-19 and represent the most promising candidates for future meta-analysis. Sufficient pooled sample size to detect clinically important reductions in multiple outcomes, including mortality, is anticipated by September 2020, but may require accessing supplementary data to align outcome reporting. Regulatory approval, funding and implementation by cell manufacturing partners will be accelerated by our framework for rapid meta-analysis.