Shows improved flow cytometry skills in trainees, suggesting enhanced data reliability.
Introduction Cellular autofluorescence, caused by endogenous biomolecules, can obscure low-intensity fluorophore emissions, leading to compromised data interpretation in flow cytometry. This issue is particularly challenging for trainees and early-career researchers, who may lack experience in spectral unmixing and tissue-specific background management. Methods We, therefore, developed a practical workflow that introduces researchers to autofluorescence as a consistent, tissue-dependent variable and provides structured guidance for improving panel design and data analysis. This workflow integrates unstained controls and autofluorescence extraction during unmixing to refine gating and improve dim-marker resolution (Cytek Aurora (16V—14B—10YG—8R), SpectroFlo software). Results Integrating this training into Core workflows improved researchers’ ability to distinguish true fluorescence from background signals and enhance their understanding of tissue-specific autofluorescence. The approach reduces troubleshooting time, increases reproducibility, and empowers users to apply autofluorescence management strategies to their own experiments independently. Conclusion This workflow addresses a critical blind spot in flow cytometry education by transforming autofluorescence from a confounding variable into a teachable and manageable feature of experimental design. It enhances data quality, supports reproducibility, and builds core competencies in flow cytometry for immunology research. Funding Source n/a Topic Categories Immunology Education and Communication (EDU)
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Moriarty et al. (2026) studied this question.
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