Cell autofluorescence is the natural emission of light by biological tissues and cells without the introduction of artificial fluorescent labels. This occurs when certain endogenous molecules within the sample absorb light energy from an external source, such as a microscope laser, and then re-emit that energy as lower-energy light. Autofluorescence is an inherent property of many biological components, present in all living and fixed biological samples. The presence of this natural background signal is a common consideration in fluorescence-based imaging and diagnostic techniques across biology and medicine. Understanding the molecular origin of this natural glow is the first step toward managing its interference in scientific observation.
The Molecular Sources of Natural Light Emission
The source of cellular autofluorescence lies in a diverse collection of molecules known as endogenous fluorophores, which are present throughout the cellular and extracellular environments. The most potent and widely studied of these are the metabolic cofactors nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD).
NADH is the reduced form of a coenzyme involved in energy-producing metabolic pathways, primarily localized in the mitochondria and cytoplasm. Only the reduced form is fluorescent, exhibiting an excitation peak around 355 nanometers (nm) and an emission maximum near 470 nm, placing its signal in the blue range of the spectrum. FAD is the fluorescent component of the flavin family, serving as another electron carrier mainly in the mitochondria. FAD fluoresces most strongly when excited around 450 nm, emitting light with a peak near 525 nm in the green spectrum.
The distinct spectral signatures of these two cofactors allow scientists to non-invasively monitor a cell’s metabolic state, often calculating a “redox ratio” to estimate the balance between oxidized and reduced species. Beyond these metabolic molecules, structural components also contribute significantly to the background signal. The extracellular matrix contains proteins like collagen and elastin, which exhibit strong autofluorescence, particularly when excited by ultraviolet light.
Another major contributor is lipofuscin, often called the “age pigment,” which accumulates in lysosomes, especially in post-mitotic cells like neurons and cardiac muscle cells. Lipofuscin is a complex accumulation of partially degraded proteins, carbohydrates, and lipids that increases with cellular aging and fixation. This pigment has a very broad excitation and emission range, generally fluorescing across the blue-green to red spectrum (460–670 nm emission), making it a persistent challenge in older or fixed tissue samples.
Autofluorescence as a Research Barrier
Endogenous light emission creates a persistent, non-specific background glow that interferes with fluorescence-based assays. When researchers introduce an artificial fluorescent probe, such as a labeled antibody, this background signal directly reduces the signal-to-noise ratio (SNR). A low SNR makes it difficult to confidently distinguish the intended molecular target from the natural cellular background.
In fluorescence microscopy, this manifests as a hazy image where fine cellular structures are masked by overall sample brightness. If the probe emits light in the same spectral region as a major autofluorescent component, the specific signal can be obscured or misinterpreted, potentially leading to false readings. This issue is particularly severe when detecting low-abundance targets, as their inherently weak signal is easily overwhelmed by the pervasive natural background.
The interference also affects quantitative methods, such as flow cytometry. Autofluorescent components in cell types like macrophages or dendritic cells register a higher baseline signal. This can mask specific fluorescent markers used for identification, leading to inaccurate cell counting or classification, especially in experiments where multiple fluorescent labels are used simultaneously.
Strategies for Signal Correction
Scientists employ a combination of physical, chemical, and computational strategies to mitigate the impact of autofluorescence on their experimental results.
Physical Optimization
Physical optimization involves selecting fluorescent probes excited by longer wavelengths, typically in the yellow, red, or far-red regions (above 550 nm). Endogenous fluorophores are most strongly excited by shorter, ultraviolet and blue wavelengths, so using longer wavelengths minimizes the natural signal. Using narrow bandpass filters is another physical method, which helps to exclude the broad emission spectrum characteristic of many autofluorescent molecules, while allowing the specific, narrower emission of the engineered probe to pass through.
Chemical Quenching
Chemical quenching involves treating the sample with reagents designed to reduce or eliminate the endogenous signal directly. Sudan Black B (SBB) is a commonly used lipophilic dye that effectively acts as a dark mask to diminish autofluorescence originating from lipofuscin granules. SBB is typically applied to fixed tissue sections in a diluted ethanol solution, where it significantly suppresses the background without compromising the integrity of the specific fluorescent labels. Commercial quenching kits, such as TrueBlack or TrueVIEW, offer specialized formulations targeting various sources, including lipofuscin and extracellular components like collagen and elastin.
Computational Methods
Computational methods provide a sophisticated means of mathematically separating the signals after image acquisition. Spectral unmixing is a powerful technique available on modern microscopes that capture the full emission spectrum (or “fingerprint”) of every fluorophore present in the sample, including the autofluorescent components. By knowing the unique spectral signature of the autofluorescence, algorithms like linear unmixing or non-negative matrix factorization (NMF) can treat the natural glow as a distinct signal and mathematically subtract it from the image. This allows for the recovery of weak, specific signals that would otherwise be obscured by the background, leading to an improvement in image contrast and the ability to accurately quantify co-localized signals.