Post Doctoral Researcher Macon & Joan Brock Virginia Health Sciences at Old Dominion University, Virginia, United States
Disclosure(s):
Alina Moriarty: No financial relationships to disclose
Introduction/Rationale: 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.
