Citizen science gamers enable automated flow cytometry gating through machine learning

Montante, S.; Yokosawa, D.; Li, L.; Butyaev, A.; Malek, M.; Movassaghi, R.; Michalchuk, Q.; Chieh-Ting, H. J.; Shmil, D.; Rahim, A.; Cossarizza, A.; Esteban, J. B.; Erhart, K.; Finnbogason, B.; Kelion, G.; Leifsson, H.; Rivers, J.; Ecker, D.; Szantner, A.; Waldispühl, J.; Brinkman, R. R.

doi:10.1101/2025.10.07.679685

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bioRxiv · 10.1101/2025.10.07.679685

Citizen science gamers enable automated flow cytometry gating through machine learning

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Abstract

Manual flow cytometry gating requires up to one hour per sample with 32% inter-expert variability, creating critical bottlenecks in immunological research reproducibility. To address this, we developed flowMagic, a machine learning algorithm for automated gating that is trained on both expert-curated data (template data) and crowdsourced annotations from citizen science gaming. Through EVE Online, 839,199 players analyzed 52,178 bivariate plots from 37 studies, generating 31,703 quality-controlled training plots. Evaluated against 92,203 expert-validated files spanning 79 immune populations (i.e., a biologically defined cell subset within each bivariate plot), flowMagic achieved 90% accuracy for abundant populations and 65% for rare populations, outperforming existing methods. The algorithm reproduced biological patterns including neutrophil dynamics in COVID-19 patients and immune development in newborns. This gaming-based approach demonstrates that crowd-sourced pattern recognition generates robust training data for complex biomedical applications, offering transformative potential for standardizing flow cytometry analysis and accelerating immunological discovery.

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Montante, S., Yokosawa, D., Li, L., Butyaev, A., Malek, M., Movassaghi, R., Michalchuk, Q., Chieh-Ting, H. J., Shmil, D., Rahim, A., Cossarizza, A., Esteban, J. B., Erhart, K., Finnbogason, B., Kelion, G., Leifsson, H., Rivers, J., Ecker, D., Szantner, A., Waldispühl, J., Brinkman, R. R.. 2025-10-08. Citizen science gamers enable automated flow cytometry gating through machine learning. https://doi.org/10.1101/2025.10.07.679685

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