Spectral vs Conventional Flow Cytometry: Key Differences, Advantages, and Applications

Flow cytometry is advancing rapidly, enabling scientists to interrogate complex cell populations at unprecedented resolution. Spectral flow cytometry, in particular, has transformed the field by allowing the detection of a greater number of cellular markers simultaneously and improving the accuracy of complex multicolor experiments. To explore the differences between conventional and spectral flow cytometry—and to highlight the practical implications of these technologies—we spoke with Maria Jaimes, VP of Scientific Commercialization at Cytek Biosciences. In this Q&A, Maria shares her knowledge on the core technologies, key advantages, panel design, ideal applications, and more, providing insights for both new and experienced users.

In a sidebar at the end of the Q&A, Kanako Lewis, Product Manager, Cell Analysis at Revvity’s BioLegend, talks about how AI is being increasingly applied to streamline spectral flow cytometry workflows.

Biocompare: What are the key differences between spectral and conventional flow cytometry?

Maria Jaimes: The key difference between spectral and conventional flow cytometry is the collection optics.

Fluorophores are excited by the onboard lasers of a flow cytometer. Upon excitation, photons are emitted, and the collection optics ensure that the photons are “captured” by photodetectors, which convert the emitted light into an electrical signal.

Conventional cytometers are designed to exclusively capture the light emitted at specific wavelengths using special optical filters known as long-pass and bandpass filters. The filters are designed to capture the wavelengths corresponding to the emission peak of specific fluorophores. For example, if the conventional cytometer is intended to detect FITC and PE, that cytometer will have filters with a center wavelength of 530 nm and 575 nm, respectively. The filters also limit the number of photons captured by each detector that are not specifically assigned to that detector.

A spectral cytometer is designed to collect as many emitted photons as possible. The optics capture not only the peak emission but also the entire range of emitted wavelengths (spectrum) after a fluorophore is excited. Fluorophores have wide excitation profiles, therefore, they can be excited by more than one of the onboard lasers. For example, PE is excited by the violet laser (405 nm), blue laser (488 nm), and yellow-green laser (561 nm). In a spectral cytometer, photons emitted from excitation by all lasers are captured by an array of detectors coupled to each laser and are used to build the full spectrum signature of emission. This is why spectral cytometers have a larger number of detectors compared to conventional cytometers.

Another key difference is the mathematical calculation used to properly assign the photons that are captured per detector (conventional) or across many detectors (spectral) to a specific fluorophore. In conventional cytometry, the mathematical calculation is called compensation, whereas in spectral cytometry, it is called unmixing.

Biocompare: Can you elaborate on unmixing? Why is it important and how can it be done effectively?

Jaimes: Spectral unmixing is a mathematical process/calculation that identifies the contribution of each fluor to the overall signal from a particle labeled with multiple fluorophores. In a spectral cytometer, the information seen by optical detectors is called raw data. In a 64-detector spectral cytometer, raw data are the signals captured by each of the 64 detectors. Unmixed data is the signal coming from each fluorophore that was used to label the particles of interest. Thus, unmixing converts the raw data into the signal generated by each fluor.

Multiple algorithms perform unmixing, but “ordinary least squares” (OLS) is the most common. For the algorithm to produce accurate results, the user needs to generate proper single-stained controls (equivalent to compensation controls) that are needed to establish the full spectrum of each fluorophore used in the assay. The controls must fully match the spectrum of the dyes in the multicolor sample and follow the same rules that exist to generate optimal compensation controls in conventional flow cytometry.

The particle used for the calculation must have a negative and positive population with identical autofluorescence characteristics; there must be good separation between the positive and negative populations; the positive population should be equally bright or brighter than the signal in the multicolor sample; enough events must be acquired to ensure robust calculation; and finally the spectrum of the fluorophore in the control must be identical to the spectrum of the fluor in the multicolor sample. This last rule is often the cause of compensation errors in conventional cytometry or unmixing errors in spectral cytometry; for example, if FITC is used as a mismatched control for GFP, or the lot of a tandem dye in the control does not match the lot in the multicolor sample.

Biocompare: What advantages does spectral flow cytometry offer?

Jaimes: Spectral flow cytometry offers several advantages, the most important being:

• Flexibility in choice of fluorophores: Conventional cytometers can only detect specific fluorophores based on the optical filters paired with each laser. In contrast, a spectral cytometer can detect any fluorophore that is excited by the onboard lasers of the instrument, providing users with more choices when it comes to fluorophores for assays.
• Multiplexing: Spectral cytometers allow for the use of a high number of fluorophores due to the optics and mathematical approach to treating emitted light. For example, Cytek^® published a panel of 45 unique fluorophores (OMIP-109) using a 5-laser spectral cytometer.
• Higher resolution for multicolor assays using similar dyes: Spectral cytometry can use more fluorophores, but importantly, the quality of the data is also superior when using a high-performance instrument with correct panel design and assay optimization. The OMIP-069 and OMIP-109 publications support this statement and are used as references in flow cytometry because of the quality of the data generated using complex panels.
• Autofluorescence extraction: Spectral cytometry can characterize autofluorescence and “extract” it from the mixture of signals in the multicolor sample to improve data resolution and accuracy of assays using samples that are highly autofluorescent.

Biocompare: Are you saying that autofluorescence (AF) is not as much of an issue with spectral flow cytometry?

Jaimes: Historically, highly autofluorescent samples have been problematic using conventional flow cytometry. Despite various strategies to quench the AF of the sample or reduce its impact by choosing fluorophores emitting in other wavelengths, samples with high AF can introduce noise and artifacts in assays that lead to loss of resolution and inaccurate interpretation of the data.

In spectral cytometry, AF can be accurately characterized, as the spectrum of the unstained sample can be fully dissected. If a spectrum is well defined and homogeneous in the cells of interest, it can simply be extracted from the signal in the sample to accurately identify the signal coming from the fluorophores. If the spectrum is heterogeneous with a combination of different AF from various cell types present in the sample, there are strategies that can be employed to treat each AF signal as a fluorophore and incorporate them in the unmixing calculation. When done properly, this produces clean samples, characterized by double negative populations that are normally distributed and centered around 0.

Correct characterization and extraction of AF relies on experienced users and can be time-consuming. Automating this process would be helpful, especially if users need to sort samples with complex AF characteristics and sorting time is limited.

Biocompare: Can you describe performance differences in sensitivity and ease of panel design between the two technologies?

Jaimes: The sensitivity or resolution of a spectral or conventional flow cytometer depends on multiple performance characteristics of the instrument. It is important to note that a generic spectral cytometer does not guarantee superior sensitivity. In terms of resolution as a signal-to-noise ratio, an instrument with the highest signal and lowest noise exhibits the highest resolution. High signal in a flow cytometer is a result of the performance of multiple optical components, such as the power and wavelength of the lasers and the quantum efficiency of the photodetectors. Low noise is dependent on the electronics in the system.

There are many differences in performance across conventional and spectral flow cytometers in the market due to the type of lasers or photodetectors used. Users should be aware of these differences and seek information to determine the sensitivity of a specific platform. We explained previously that a spectral cytometer collects light across the entire range of emission from shorter to longer wavelengths, therefore, it is critical that the sensitivity of detection is optimal across all wavelengths. Historically, in flow cytometry, sensitivity has been higher for shorter wavelengths, but specific photodetectors such as Avalanche Photodiodes have a very high quantum efficiency, and have made it possible to improve sensitivity at longer wavelengths.

The overall approach for robust panel design is identical between a conventional and a spectral cytometer. The goal of panel design is to ensure the optimal resolution of each marker in the panel. To achieve this, the decision of which fluorophore to assign to each marker needs to account for the biology, such as the level of expression of the antigen and the co-expression with other antigens in the panel, and the performance characteristics of fluorophores, such as brightness and tendency to spread into the other fluors used. The main difference in the panel design process between spectral and conventional is that spectral users have increased flexibility in the choice of fluorophores. This selection process is facilitated by the development of metrics that evaluate the uniqueness of the spectrum of a dye called cosine similarity, and the likelihood of a fluor combination to yield optimal results, which is the condition number of the unmixing matrix.

Biocompare: What are the ideal applications for spectral flow cytometry?

Jaimes: Spectral cytometry can perform the same applications as conventional cytometry. However, there are applications that are only possible with a spectral cytometer:

• Immunophenotyping with high parameter panels: 24-color panels with a 3-laser configuration and 50-colors for 5-laser configurations are possible based on published data. Large panels are especially important when using specimens that have a limited number of cells, such as tumor biopsies, pediatric samples, mouse brain samples, etc.
• Applications using cells/samples with high autofluorescence: High autofluorescence can be extracted from the fluorophore signals to improve resolution. For example, no-wash assays using whole blood with lysed red blood cells are used to ensure accurate absolute counts of different cell populations, however, these are noisy and exhibit low resolution in conventional cytometry. This limitation can be overcome in spectral cytometry as noise can be characterized and “extracted” from the actual signal of the fluorophores used in the assay.
• Label-free applications: Autofluorescence alone can be characterized to identify different cells or particles, such as in the field of marine biology, where different species of algae can be identified based on their AF characteristics.

AI in spectral flow cytometry workflows

According to Kanako Lewis, Product Manager, Cell Analysis at Revvity’s BioLegend, AI is being increasingly applied to streamline spectral flow cytometry workflows, helping researchers manage the complexity of high-parameter experiments from panel design to data acquisition and analysis. Many commercial panel design tools now incorporate AI-based algorithms that can suggest compatible dye combinations, flag conflicts, and balance marker expression with fluorophore brightness. They can also analyze fluorophore emission spectra and instrument configurations to accelerate the development of successful panels.

"AI-assisted acquisition features are also being incorporated into spectral instrument software. For example, AI can be used to optimize instrument settings in real time. It can suggest voltage or gain settings to maximize signal-to-noise ratios for each detector channel, automatically compensate for spectral overlap, and adapt to variations between samples. AI can also be used to monitor instrument performance, detect anomalies in laser output, fluidics, or detector sensitivity, and alert users to potential issues before they impact experimental results.

"AI is improving spectral unmixing by modeling instrument-specific spectral signatures and detecting anomalies in controls or compensation. AI algorithms analyze single-stain or fluorescence-minus-one (FMO) controls to verify the accuracy of spectral compensation and flag problematic channels. This is especially important for large panels where minor errors in unmixing can lead to misinterpretation of rare events.

"High-dimensional data analysis benefits significantly from AI as well. Machine learning algorithms can cluster and classify cell populations, identify rare subsets, and detect subtle phenotypic changes that may be missed with traditional gating methods."

Overall, Lewis explains “AI is enhancing spectral flow cytometry by simplifying panel design, improving data quality, increasing reproducibility, and enabling more sophisticated analysis, ultimately allowing researchers to extract deeper biological insights from complex experiments.”

BioLegend offers a wide variety of fluorophores, antibodies, and reagents designed specifically for spectral flow cytometry. Its signature Spark Dyes™, Spark Plus™, and Fire™ dyes provide exceptionally bright signals with minimal spillover, enabling easy panel expansion and clear spectral separation. The newer Spark Plus dyes build on the original Spark chemistry with even stronger emission and improved stability, making them especially useful for detecting low-expressed markers.