Bridging the Gap in Flow Cytometry Training: A Practical Workflow for Teaching Autofluorescence Management in Immunology 2261098

Abstract 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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Publication Details

Journal
The Journal of Immunology
Published
2026-07-28
DOI
https://doi.org/10.1093/jimmun/vkag141.1030
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Bridging the Gap in Flow Cytometry Training: A Practical Workflow for Teaching Autofluorescence Management in Immunology 2261098

Shelby Ma, Basudha Habisyasi, Elena Galkina, Alina Moriarty et al.
The Journal of Immunology
Single-cell and spatial transcriptomics
article

Bridging the Gap in Flow Cytometry Training: A Practical Workflow for Teaching Autofluorescence Management in Immunology 2261098

Shelby Ma, Basudha Habisyasi, Elena Galkina, Alina Moriarty, Kathleen Coleman
article en

Abstract

Abstract 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)

The Journal of ImmunologyVol. 215(Supplement_1)
Old Dominion University (US)
Openalex Percentile: Top 14%
Single-cell and spatial transcriptomics
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