Spectral Flow Cytometry Fundamentals

Flow cytometry is a technology that provides rapid multiparameter analysis of single cells or particles in suspension as they flow past single or multiple lasers. Each cell or particle is analyzed for scattered light and multiple fluorescence signals captured by the detectors of the instrument. The ability to perform discrete measurements on thousands or millions of cells in a single sample makes flow cytometry one of the most powerful platforms available. Single-cell analysis using flow cytometry reveals cellular heterogeneity and the dynamics of single cells and is applicable across research areas, biotech, biopharma, and clinical settings for cell identification and characterization. Common applications include immunology, infectious disease, immunology-oncology and cancer biology, microbiology, drug discovery and biomarker identification, and molecular biology. Increasing adoption of high-parameter cell-based testing is powered by the ever-expanding desire to understand immune system complexity and to manipulate cells within the immune system to improve health. Recent advancements in both instrumentation and fluorophore development have increased the capabilities and the number of parameters that can be analyzed in a single sample using flow cytometry.

Flow cytometry has evolved over many decades after the first commercial flow cytometers capable of measuring a single fluorescence parameter were introduced in the early 1970s [1]. By the late 1970s, instruments configured with two lasers were engineered with improved capabilities that could not only measure and quantify cells, but also were adapted to allow for cell sorting [2]. Continued development of flow cytometry technology provided increasing quantity of lasers and detectors, allowing detection of greater numbers of parameters [3–5]. More recently, flow cytometers with five lasers have allowed for the detection of 28 fluorescent parameters [6].

Despite substantial growth in detection capabilities, considerable interest persisted for measurement across the complete fluorescence spectrum, with efforts to develop flow cytometers that perform similar to a spectrofluorometer. A primary hurdle in these efforts was the short signal integration times needed for single-particle measurements. Advances in optics and detectors have enabled spectral measurements on micro-second time scales typically used for flow cytometric measurements [7].

Spectral approaches utilizing flow cytometry were first described by the Robinson group at Purdue University in 2004 [8–10]. Sony Biotechnology launched the first commercial spectral flow cytometer in 2012 using prisms along with photomultiplier tubes (PMT) to collect and amplify light. Several companies, including Cytek Biosciences, BD Biosciences, and Thermo Fisher Scientific, have also developed spectral flow cytometers and spectral cell sorters using varying systems of signal detection and amplification.

Many of the fundamental concepts of conventional flow cytometry are easily translated to spectral flow cytometry [11–12]. Sample uptake and delivery commonly use either a positive pressure or a vacuum-driven system, and the underlying physical principles of sample interrogation remain unchanged. A single-cell suspension is injected in a turbulence-free sheath fluid stream flowing at constant pressure. The difference in pressure between sheath and sample focuses the cells into a single file, called hydrodynamic focusing. Acoustic-assisted hydrodynamic focusing is a recent development combining hydrodynamic focusing with a piezo device that produces sound waves to more precisely align the cells [13].

Both conventional and spectral instruments may utilize photomultiplier tubes (PMT) or avalanche photodiodes (APD) as photodetectors. APDs possess higher quantum efficiency than PMTs, an attribute that improves their performance at wavelengths greater than 650 nm, enhancing red and near-infrared detection. In biological measurements, PMTs and APDs perform similarly in the visible wavelength region [14].

Flow cytometry panel design relies on understanding the instrument configuration, compatible fluorophores, the expression level of the markers in the biological system, and analysis strategy [15]. In general, panel design for spectral flow cytometry follows the same principles as with conventional flow cytometry, including gating strategy, matching fluorophore brightness to antigen density, characterizing and minimizing spillover spreading error, and systematic use of experimental and technical controls. However, spectral panel development requires additional considerations [12]. Lastly, as with conventional flow cytometry, the digitized information generated in a spectral flow cytometer is stored as a Flow Cytometry Standard (FCS) file and is analyzed statistically to report on cellular characteristics.

Table 1. Comparison of main features of conventional flow cytometry and spectral flow cytometry.

Learn more: Flow cytometry basics

Conventional and spectral flow cytometers differ in:

• The bandwidth of emitted light delivered to the photodetectors
• The number of detectors used per fluorophore
• The algorithms employed to separate one fluorophore from another

In conventional flow cytometry, each fluorophore present is measured in a single target detector with a portion of the full emission collected using band-pass or long-pass optical filters. Spillover from other fluorophores that may have emission in that detector is corrected using compensation (Figure 1).

Figure 1. Comparison of conventional and spectral flow cytometry optical detection. (A) Conventional compensation-based flow cytometers use a single detector to collect fluorescence emission from one primary fluorophore, with only a section of emission collected. (B) Spectral unmixing-based based flow cytometers use multiple detectors to collect full spectrum fluorescence emission for all fluorophores using multi-laser excitation.
 

In contrast, spectral flow cytometry uses multiple detectors to measure the full spectrum emission of every fluorophore across multiple lasers used in the system to create a more detailed signature for each fluorophore. The spectrum detected by each group or array forms a spectral signature (Figure 2). While conventional flow cytometry uses compensation to correct for fluorescence spillover, spectral flow cytometry uses a process called unmixing to identify each fluorophore. Spectral unmixing uses a mathematical algorithm that distinguishes the many fluorophore signatures within a multicolor sample, based on the unique spectral signature of each fluorophore. Through this approach, fluorophores with near-identical peak emissions but different off-peak emissions maybe distinguished and used together in a panel. Finally, cellular autofluorescence may be extracted from the fluorescence signal to improve signal resolution with most spectral systems [1,16].

A. Invitrogen Brilliant Ultra Violet 737 dye on Cytek Aurora

B. Invitrogen Brilliant Ultra Violet 737 dye on Invitrogen Bigfoot

Figure 2. Spectral signature. The spectral signature of a fluorophore is a result of multi-laser excitation. In these examples each detector set associated with individual lasers is contributing to a unique signature. (A) Spectral signature of Invitrogen Brilliant Ultra Violet 737 dye is shown using a Cytek Aurora spectral flow cytometer equipped with five lasers. (B) Spectral signature of Invitrogen Brilliant Ultra Violet 737 dye is shown using the Invitrogen Bigfoot Spectral Cell Sorter equipped with seven lasers.
 

Explore: NovaFluor dyes for immunophenotyping
Explore: Brilliant ultra violet polymer dyes

The desire of researchers to maximize the information a single sample can provide has led to advances in instrumentation and an increase in fluorophore availability. With well-designed panels, both spectral and conventional flow cytometers can generate high-resolution data. However, as researchers want to evaluate more parameters, spectral flow cytometry can resolve more individual fluorophores by collecting and processing the data as full spectra. This allows the use of more existing fluorophores that would otherwise be incompatible on a conventional flow cytometer and the expansion of immunophenotyping panels beyond 40 fluorescent parameters [17,18].

In addition, spectral flow cytometers allow the measurement of cellular autofluorescence as a separate parameter as if it were another fluorophore in the panel. Accounting for the contribution of background, due to autofluorescence, can improve the resolution of target-specific fluorescent signals. Spectral flow cytometry provides more information for each fluorophore which allows for increased resolution and sensitivity, and fluorophores having similar emission maxima but differing off-peak emissions can be differentiated using unmixing. This provides greater flexibility and capability in panel design.

Using the power of spectral flow cytometry and cell sorting, researchers can now identify and sort cells based on new combinations of markers. However, the increased complexity of panel design and complex subset characterization requires increased expertise for panel design, accurate analysis of results, and standardized protocols [19]. Increasing the number of parameters has the potential to provide deeper characterization of immune cells and subsets.

1. Mitra-Kaushik, Shibani, et al. "The Evolution of Single-Cell Analysis and Utility in Drug Development." The AAPS Journal 23.5 (2021): 1–15.
2. Herzenberg, Leonard A., et al. "The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford." Clinical chemistry 48.10 (2002): 1819–1827.
3. Roederer, Mario, et al. "8 color, 10‐parameter flow cytometry to elucidate complex leukocyte heterogeneity." Cytometry: The Journal of the International Society for Analytical Cytology 29.4 (1997): 328–339.
4. De Rosa, S. C., and Mario Roederer. "Eleven-color flow cytometry. A powerful tool for elucidation of the complex immune system." Clinics in laboratory medicine 21.4 (2001): 697–712.
5. Perfetto, Stephen P., Pratip K. Chattopadhyay, and Mario Roederer. "Seventeen-colour flow cytometry: unravelling the immune system." Nature Reviews Immunology 4.8 (2004): 648–655.
6. Brummelman, Jolanda, et al. "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry." Nature protocols 14.7 (2019): 1946–1969.
7. Nolan, John P., and Danilo Condello. "Spectral flow cytometry." Current protocols in cytometry 63.1 (2013): 1-27.Jan; Chapter 1: Unit 1.27.
8. Robinson J, Rajwa B, Gregori G, Jones J, Patsekine V. “Collection hardware for high speed multispectral single particle analysis.” In: ISAC. 2004.
9. Robinson, J. Paul. "Multispectral cytometry: the next generation." Biophotonics international 11 (2004): 36–41.
10. Robinson, Joseph Paul, et al. "Multi-spectral detector and analysis system." U.S. Patent No. 7,280,204.9 Oct 2007.
11. Ashhurst, Thomas Myles, Adrian Lloyd Smith, and Nicholas Jonathan Cole King. "High‐dimensional fluorescence cytometry." Current protocols in immunology 119.1 (2017): 5–8.
12. Ferrer‐Font, Laura, et al. "Panel design and optimization for high‐dimensional immunophenotyping assays using spectral flow cytometry." Current protocols in cytometry 92.1 (2020): e70.
13. Ward, Michael D., and Gregory Kaduchak. "Fundamentals of acoustic cytometry." Current protocols in cytometry 84.1 (2018): e36.
14. Lawrence, William G., et al. "Enhanced red and near infrared detection in flow cytometry using avalanche photodiodes." Cytometry Part A: The Journal of the International Society for Analytical Cytology 73.8 (2008): 767–776.
15. Maciorowski, Zofia, Pratip K. Chattopadhyay, and Paresh Jain. "Basic multicolor flow cytometry." Current protocols in immunology 117.1 (2017): 5–4.
16. Niewold, Paula, et al. "Evaluating spectral cytometry for immune profiling in viral disease." Cytometry Part A 97.11 (2020): 1165–1179.
17. Park, Lily M., Joanne Lannigan, and Maria C. Jaimes. "OMIP‐069: forty‐color full Spectrum flow cytometry panel for deep Immunophenotyping of major cell subsets in human peripheral blood." Cytometry Part A 97.10 (2020): 1044–1051.
18. Sahir, Fairooz, et al. "Development of a 43-color panel for the characterization of conventional and unconventional T‐cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry." Cytometry Part A (2020).
19. McCausland, Megan, et al. "With great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials." Bioanalysis 13.21 (2021): 1597–1616.