Autofluorescence Subtraction with the Agilent NovoCyte Opteon Spectral Flow Cytometer

Significance of the topic

Autofluorescence is an intrinsic fluorescence signal originating from endogenous biomolecules (aromatic amino acids, flavins, lipofuscins, pyridine nucleotides) that can obscure or confound detection of fluorescent labels in flow cytometry. In high-parameter immunophenotyping and functional assays, unresolved autofluorescence reduces sensitivity, produces false positives, and complicates quantitative interpretation. Spectral flow cytometry, which records full emission spectra across many detectors, enables treatment of autofluorescence as a separable spectral component. Accurate identification and subtraction of autofluorescence spectra therefore directly improves unmixing fidelity, marker resolution, and downstream biological interpretation.

Objectives and study overview

This application note demonstrates practical workflows for identifying and subtracting autofluorescence using the Agilent NovoCyte Opteon spectral flow cytometer and NovoExpress software. Key goals were:

• Compare unmixing outcomes with and without explicit autofluorescence extraction across sample types.
• Define strategies for simple (homogeneous) versus complex (heterogeneous) autofluorescence scenarios.
• Show how proper autofluorescence characterization prevents artifacts and improves immunophenotyping accuracy for fresh and stabilized human blood, mouse spleen, and mouse lung samples.

Used instrumentation

• Agilent NovoCyte Opteon UVBYR spectral flow cytometer: up to five lasers (349 nm, 405 nm, 488 nm, 561 nm, 637 nm), forward scatter (FSC), blue and violet laser side scatter (BSSC, VSSC), and ~70–73 fluorescence detectors to capture broad spectral information.
• NovoExpress software: tools for spectral density plotting, autofluorescence gating, reference spectrum assignment, and spectral similarity matrices to evaluate distinct autofluorescence signatures.
• Common sample preparation reagents and consumables referenced: AceaLyse (RBC lysis), 1x RBC lysis buffer, HBSS/PBS, PFA fixation, digestive enzymes (collagenase D, DNase I) for tissues, viability dyes (PI, Live/Dead Blue), and compensation beads as a recommended control for complex single-stain spectra.

Methodology

Samples evaluated: freshly collected human peripheral blood (EDTA), stabilized human peripheral blood (CD-Chex Plus), mouse splenocytes, and enzymatically digested mouse lung cells. Common workflow elements:

• Antibody staining of single- and full-stain panels, RBC lysis, washes, fixation (1% PFA), and viability labeling.
• Acquisition on the NovoCyte Opteon to collect full spectral signatures for unstained, single-stained, and fully stained specimens.
• Identification of autofluorescent populations from unstained samples using spectral density plots and hierarchical gating on channels showing strong autofluorescence.
• Designation of identified autofluorescent populations as reference autofluorescence spectra in NovoExpress and inclusion of these spectra in spectral unmixing.
• Use of a spectral similarity matrix (threshold similarity ≤ 0.95 indicates distinct spectra) to reduce redundant AF spectra and select representative signatures.
• Special handling for single-stained spectrum calculations in heterogeneous samples: ensure negative and positive populations used to compute the fluorochrome reference have matching autofluorescence intensity, or use beads when no appropriate negative population exists.

Main results and discussion

Key findings across sample types:

• Human blood (fresh vs stabilized): Autofluorescence signatures across cell populations in blood were spectrally similar. Fresh blood exhibited low-intensity autofluorescence so including AF in unmixing was optional; stabilized blood showed substantially stronger AF and benefited from including a single AF spectrum in unmixing to remove background and improve population separation.
• Mouse spleen: Mostly consistent autofluorescence across the main population with small subpopulations showing distinct signatures. Two distinct AF spectra (AF-A1 and AF-B) were identified using spectral density plots and validated by a low similarity score (~0.42). Inclusion of both AF spectra removed false-positive artifacts in unstained, single-stained, and full-stained unmixing and restored accurate immunophenotyping.
• Mouse lung: Highly heterogeneous and strong autofluorescence with multiple distinct spectral profiles. Four AF spectra were required (after filtering redundant spectra using similarity metrics) to correctly unmix single-stain and full-stain data. Including these AF spectra eliminated spurious populations and enabled correct identification of diverse lung cell types (endothelial cells, type II epithelial cells, alveolar macrophages, neutrophils, eosinophils, multiple dendritic and monocyte/macrophage subsets).
• Spectrum calculation caveats: In heterogeneous samples, naive subtraction of negative from positive populations in single-stained controls can produce inaccurate reference spectra if the negative and positive subsets have differing autofluorescence. The remedy is to gate subpopulations with uniform autofluorescence intensity (using channels dominated by AF) before computing the reference spectrum or to use compensation beads when appropriate.

Benefits and practical applications

Practical advantages demonstrated:

• Improved signal resolution and quantitative accuracy: Explicit AF subtraction reduces background and clarifies fluorochrome-positive populations, particularly when AF intensity overlaps fluorochrome emission.
• Reduced false positives and spreading error: Removing AF components prevents misclassification of cells as marker-positive due to underlying AF variance.
• Flexible strategies matching sample complexity: Simple single-spectrum AF subtraction suffices for homogeneous, low-AF samples (e.g., fresh blood); multi-spectrum strategies and careful gating are required for heterogeneous tissues (e.g., lung).
• Guidance for panel development and QC: Evaluate AF in unstained samples during panel design, employ spectral similarity tools to avoid redundant references, and validate single-stain references in regions of uniform AF intensity.

Future trends and potential applications

Anticipated developments and expanded uses:

• Software automation and AI: Machine-learning approaches to automatically identify and classify distinct AF spectra from complex datasets will streamline workflows and reduce operator dependency.
• Standardized autofluorescence libraries: Shared AF reference libraries for common tissues and fixatives could accelerate analysis and harmonize unmixing across labs.
• Integration with high-parameter cytometry: As panel complexity increases, robust AF handling will become essential for reliable high-dimensional phenotyping and single-cell multiomic workflows.
• Clinical translation: Improved AF subtraction may enhance diagnostic flow cytometry in samples with pathologic AF changes (disease states, activated cells, storage effects), improving sensitivity and specificity.

Conclusion

Autofluorescence is a variable but addressable source of background in spectral flow cytometry. Proper identification of distinct AF signatures from unstained controls, use of spectral similarity filtering, and inclusion of representative AF spectra in spectral unmixing markedly improve data quality. The level of complexity in AF subtraction should be matched to sample heterogeneity: a single AF spectrum is generally sufficient for homogeneous blood samples, while complex tissues with multiple AF populations require multiple AF references and careful gating or bead controls. The Agilent NovoCyte Opteon combined with NovoExpress provides practical tools to implement these approaches and enhance high-resolution immunophenotyping.

References

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