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Compensation for Spectral Overlap in Flow Cytometry


Single-colour flow cytometry methods have progressed to ultra high-parameter flow cytometry, aided by the development of sophisticated fluorescent proteins with a wide range of excitation and emission spectra.1,2 While the amount of data that can be collected per cell has grown vastly, high-parameter flow cytometry is still limited by spectral overlap.1,2
 

 

Spectral overlap
 

In flow cytometry, as a labelled cell travels through the laser interrogation point, each detector should receive photons from one specific fluorochrome.3

However, the fluorescence spectra of most label fluorophores used in flow cytometry cover a wide range of wavelengths, meaning that a fluorophore may give a large fluorescence signal in its assigned fluorescence channel, but will also give smaller signals in channels assigned to other fluorophores.4
 

For example, in a panel of ten markers with different fluorophores, each of the markers fluoresces at different intensities along the light spectrum when excited. While each marker has a different spectrum, the tails of the spectrums can overlap and cause noise within a parameter’s measurements.5
 

When fluorophores emit light into multiple detectors, this is known as this is known as signal spillover, which is caused by the spectral overlap that many fluorochromes have.6
 

Why is spectral overlap problematic?
 

Spectral overlap may result in a reduction in sensitivity, defined as the lowest signal that can be detected over the accumulation of signals coming from all other background sources.3,6 The more signals from fluorescent dyes that spill over into a detector, the less the sensitivity and false positives or negatives can be detected in the presence or absence of a marker.5,6
 

As spectral overlap is an unwanted signal, it needs to be corrected.4
 

How can spectral overlap be prevented?
 

Compensation is the process of removing or correcting the unwanted spillover from all detectors except the primary detector for a specific fluorochrome and is necessary for accurate data analysis of multi-colour flow cytometry.1,3
 

Running parallel samples with one marker removed from the overall panel guarantees that all cells are truly negative for that specific marker, by forcing it to be identical to that of unstained cells.5  
 

Tip: Compensation beads can be used to provide distinct positive- and negative-stained populations by providing control particles that have no binding capacity. This allows compensation levels to be set manually or automatically, without having to use valuable tissue samples.
 

Compensation facilitates the identification of a threshold for the maximum parameter values possible for true negative-marker signals on cells, enabling the determination of marker presence in fully stained samples.5
 

Fluorescence minus one (FMO) is used in manual gating, where clusters of homogenous cells that share common characteristics are identified and regions are placed around them during data analysis.5,7 FMO is helpful for gauging the sensitivity of particular detectors in the context of other reagents, reducing the subjectivity and bias associated with manual gating.5,8


Learn more about spectral flow cytometry.

References
  1. Roca CP, Burton OT, Gergelits V, et al. AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data. Nat Commun. 2021;12(1):2890
  2. Kleeman B, Olsson A, Newkold T, et al. A guide to choosing fluorescent protein combinations for flow cytometric analysis based on spectral overlap. Cytometry A. 2018;93(5):556-562
  3. Bhowmick D, van Diepen F, Pfauth A, Tissier R, Ratliff ML. A gain and dynamic range independent index to quantify spillover spread to aid panel design in flow cytometry. Sci Rep. 2021;11(1):20553
  4. Wang L, Gaigalas AK, Wood J. Quantitative Fluorescence Measurements with Multicolor Flow Cytometry. Methods Mol Biol. 2018;1678:93-110
  5. Fox A, Dutt TS, Karger B, et al. Cyto-Feature Engineering: A Pipeline for Flow Cytometry Analysis to Uncover Immune Populations and Associations with Disease. Sci Rep. 2020;10(1):7651
  6. Nguyen R, Perfetto S, Mahnke YD, Chattopadhyay P, Roederer M. Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A. 2013;83(3):306-315
  7. Malek M, Taghiyar MJ, Chong L, Finak G, Gottardo R, Brinkman RR. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics. 2015;31(4):606-7
  8. Majumdar D, Pietras EM, Pawar SA. Analysis of Radiation-Induced Changes in Cell Cycle and DNA Damage of Murine Hematopoietic Stem Cells by Multi-Color Flow Cytometry. Curr Protoc. 2021;1(8):e216
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