What is spectral flow cytometry?

Spectral flow cytometry is based on many of the fundamental aspects of conventional flow cytometry but has unique optical collection and analytical capabilities. With spectral flow cytometry, the emission spectrum of every fluorescence molecule is captured by a set of detectors across a defined wavelength range. Every molecule’s fluorescent spectrum can be recognized, recorded as a spectral signature, and used as reference in multicolor applications.

Discover how acoustic-assisted focusing, broad detector arrays, and dual-mode workflows (spectral unmixing and compensation) on Xenith enhance spectral panel performance, speed, and resolution—especially for rare populations and challenging samples like PBMCs or tumor digests.

Why are researchers excited about spectral flow cytometry?

Introduction to spectral flow cytometry

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.


History of spectral flow cytometry development

Flow cytometry emerged in the early 1970s with instruments capable of measuring a single fluorescent parameter [1]. By the late 1970s, dual-laser systems enabled simultaneous multi-wavelength analysis and introduced cell sorting capabilities [2]. Over subsequent decades, innovations in optics and detector technologies expanded instrument capacity to support panels with increasing numbers of fluorescent markers, reaching more than 25 detectable parameters [3–5] with the addition of multiple lasers [6].

Despite progress in multi-parameter detection, traditional cytometers remained limited by discrete bandpass filters. Spectral cytometry emerged to address this by capturing full emission spectra per cell—an approach conceptually similar to spectrofluorometry. Implementing this in flow systems required rapid, high-resolution detection capable of operating within microsecond measurement times. Advances in sensor sensitivity and optical dispersion technologies rendered this feasible [7].

Pioneering descriptions of spectral flow gating were published in the early 2000s, leveraging unmixing algorithms to resolve overlapping fluorophores. 

In recent years, instruments such as the Attune Xenith Flow Cytometer have embodied these capabilities, integrating acoustic-assisted hydrodynamic focusing for exceptional sample alignment, broad laser coverage—including UV and NIR excitation—and a high-density detector array comprising 6 lasers and over 50 fluorescent channels. This approach enables both spectral unmixing and conventional compensation workflows, supports high-speed acquisition (up to 1,000 µL/min), and delivers stable rare-event detection while maintaining data quality and reproducibility.

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Comparing spectral and conventional flow cytometry

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. The Attune Xenith Flow Cytometer has introduced acoustic-assisted hydrodynamic focusing, enhancing precision in cell alignment through piezo-generated sound waves [13].

Detection technologies like photomultiplier tubes (PMTs) and avalanche photodiodes (APDs) are used across both conventional and spectral flow cytometry. The Attune Xenith Flow Cytometer is engineered with photomultiplier tubes (PMTs)—a deliberate choice to optimize spectral performance. PMTs offer low electronic noise, a broad dynamic range, and fast signal response, all of which are critical for accurate spectral unmixing and rare event detection. These characteristics make PMTs excellent for resolving overlapping emission spectra in high-parameter panels while maintaining linearity across a wide range of signal intensities. By integrating PMTs, Attune Xenith cytometer delivers reliable sensitivity, speed, and reproducibility for complex immunophenotyping workflows.

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.

 Conventional flow cytometrySpectral flow cytometry
Wavelength range of detection for a given fluorophoreNear emission maxima~350–900 nm
Number of detectors/fluorophoresOneMultiple
Spillover correction methodCompensationUnmixing
Fluorophore selectionLimited by optical configurationLimited by fluorophore spectral signature uniqueness
Autofluorescence extractionNoYes

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

Spectral and conventional flow cytometry differences

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.
 

Advantages of spectral flow cytometry

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.

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References and suggested reading

  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.
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