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Short answer
Spectral flow cytometry is a flow cytometry approach in which the raw signal is separated into individual fluorophores by unmixing after acquisition, using single-stain controls as references, rather than by conventional compensation. It supports panels with many more parameters than conventional instruments, and its results depend heavily on the quality of the reference controls.
Definition and scope
In conventional flow cytometry, spillover between fluorophores is corrected by compensation. Spectral flow cytometry changes that step. According to a practical workflow paper from researchers at Erasmus University Medical Center in Rotterdam, “a crucial step in spectral flow cytometry is unmixing of the raw data, using single-stain controls as references,” and a large difference from conventional flow cytometry is that unmixing happens after acquisition (den Braanker et al., 2021). The same paper reports that the approach allows panels “with many more parameters than ever deemed possible in conventional flow cytometry.”
The scope of this page is the concept and the quality issues that matter to lab and core-facility staff. It does not cover panel design for a specific instrument or vendor selection.
Spectral, spectra and spectrum flow cytometry
Searches for “spectra flow cytometry” and “spectrum flow cytometry” refer to the same method as spectral flow cytometry. This page uses “spectral” throughout.
How spectral unmixing is used in practice
The workflow in the sources has a consistent shape:
- Acquire reference controls. Single-stained controls and an unstained control are run so the software has a signature for each fluorophore. In a comparison study on a three-laser (405, 488 and 640 nm) Cytek Aurora, samples were unmixed live in SpectroFlo software using single-stained and unstained controls (van der Pan et al., 2023).
- Choose the reference material carefully. “As a rule of thumb, single-stain controls on your cell type of interest give the best unmixing results,” and reference controls can be reused across batches, which saves time (den Braanker et al., 2021).
- Unmix the data. SpectroFlo uses linear unmixing, although other methods exist (den Braanker et al., 2021).
- Check the result. The mass cytometry comparison study used NxN plots to evaluate unmixing accuracy (van der Pan et al., 2023).
Autofluorescence is handled differently too. The den Braanker workflow lists removal of autofluorescence per cell among the characteristics of the approach, and names minimal compensation requirements and reduced autofluorescence effects as advantages.
How it differs from conventional flow cytometry
The rows below summarise statements in den Braanker et al. (2021).
| Aspect | Conventional flow cytometry | Spectral flow cytometry |
|---|---|---|
| Signal separation | Compensation | Unmixing of the raw data using single-stain controls as references |
| When separation happens | Spillover is corrected by compensation | Unmixing is done after acquisition |
| Panel size | Tied to the number of channels of the instrument | Panels with many more parameters |
| Acquisition time | Reference point | Comparable to conventional (den Braanker et al.) |
| Autofluorescence | Not described as a characteristic of the approach | Removal per cell listed as a characteristic |
For the compensation side of this comparison, see CASRAI’s guides on single-stain controls and compensation and on how flow cytometry data is displayed.
Where it matters for labs and core facilities
Performance evidence from a comparison with mass cytometry
A 2023 study compared spectral flow cytometry with mass cytometry for innate myeloid cell populations and reported comparable results between the two technologies. Spectral flow cytometry had better acquisition time and recovery and lower intra-measurement variability than mass cytometry, and the authors state it has higher sensitivity for detecting low-abundance proteins. They also attribute some marker discrepancies to the spread of the signal in spectral flow cytometry and call for studies that measure the same sample on different days (van der Pan et al., 2023).
A checklist of what goes wrong
The den Braanker workflow paper names these failure points:
- Banana-shaped populations. They can appear when unmixing with reference controls on beads, a sign to revisit the reference material.
- Manual compensation. The paper says small adjustments (under 3%) can correct artefacts, but manual compensation should be avoided.
- Truncated data on import. Some import routes can clip extreme positive values, so check that the import step preserves them.
- Transformation artefacts. Artificial extra spikes after transformation should be avoided.
- Batch effects. Differences between batches are described as unavoidable, and the authors report that the CytoNorm method “was able to remove almost all batch effects” in their data.
Control choices carry over from conventional work. See the guides on FMO, isotype and unstained controls and on multicolor instrument setup and QC beads. For a different instrument category, see the comparison of imaging and conventional flow cytometry.
Common misunderstandings
- “Spectral means no controls.” The opposite: unmixing relies on single-stain controls as references, and their quality drives the result.
- “It is slower.” The workflow paper reports acquisition times comparable to conventional flow cytometry.
- “Unmixing is the same as compensation done later.” The sources treat unmixing as a different step, performed after acquisition, and advise against manual compensation.
- “More colours remove all spillover problems.” The comparison study notes that signal spread can still affect marker results.
Frequently asked questions
What is spectral flow cytometry?
It is a flow cytometry approach in which raw data are unmixed after acquisition, using single-stain controls as references, instead of being corrected by compensation (den Braanker et al., 2021).
Is spectra flow cytometry or spectrum flow cytometry something different?
No. Those are alternative spellings of the same method.
Does spectral flow cytometry need single-stain controls?
Yes. Unmixing uses single-stain controls as references, and controls on the cell type of interest give the best unmixing results (den Braanker et al., 2021).
Is it slower than conventional flow cytometry?
The workflow paper reports comparable acquisition times to conventional flow cytometry (den Braanker et al., 2021).
How does it compare with mass cytometry?
One study of innate myeloid cell populations reported comparable results between the two technologies, with spectral flow cytometry showing higher recovery and lower intra-measurement variability (van der Pan et al., 2023).
Sources
- den Braanker H, Bongenaar M, Lubberts E. How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow. Front Immunol 2021;12:768113. https://pmc.ncbi.nlm.nih.gov/articles/PMC8640183/ (checked 2026-10-29).
- van der Pan K, et al. Performance of spectral flow cytometry and mass cytometry for the study of innate myeloid cell populations. Front Immunol 2023;14:1191992. https://pmc.ncbi.nlm.nih.gov/articles/PMC10235610/ (checked 2026-10-29).
