A Look Back on Flow Cytometry in 2025

January 14, 2026

Guest Authored By: Kelly Lundsten

There aren’t many people left who need to be convinced that spectral cytometry, the pivotal coalescence of engineering and computational advancements that has enabled 401-5 and 506 color flow cytometry, is destined to become a mainstream technology. All significant advancements this year have centered around making 35+ color, what might be also called ultra-high parameter flow cytometry, easier to access whether it be technical advancements in fluorophores with less cross-beam excitation and spectral spillover like the Horizon™ Real family of multimers from BD (discussed in FluoroFinder’s December newsletter), a wider selection of spectral instrumentation like the CytoFLEX mosaic spectral detection module from Beckman Coulter or computational solutions to manage computational errors in unmixing accuracy7-9. It’s also a focus by the technical community to stop reinventing the wheel and focus on what we can do to ease the pain of the typical biologist using single cell technologies in their efforts to validate and implement such complicated but rewarding assays. Besides technical advancements, this also involves efforts in experimental standardization by different collaborative groups and holding OMIP (Optimized Multicolor Immunofluorescence Panel) publications to a higher standard commensurate with the effort required to validate these panels to be fit for their intended purpose. However, this year spectral cytometry, although still in its adolescence has begun to find its groove. When spectral cytometry was introduced into the mainstream, a common saying you’d hear from early adopters was that with this technology, you “see everything you didn’t see before”, both good and bad. Although that saying was intended to pass shade on poor panel design and experimental practice, I’d like to reuse that phrase to describe something more discerning. If I had to look back and define a theme for 2025, I’d say it would be an understanding that traditional cytometry caused us to overlook or not be able to “see” important cellular features and function. For a long while, we’ve been turning a blind eye to cell “events” that we don’t know what to do with!

At 30+ fluorescent parameters, enough markers are included in the assay to make one question how we have historically identified cells as members of a subset. For example in human blood/PBMC, when you can include every major phenotypic marker for all of the known mature cell types, also known as lineage markers, and go through a path of analysis where each major cell subset is excluded individually from the analysis of CD45+ leukocytes, meaning the exclusion of CD34+ progenitors, CD56+ NK, CD123+ basophil and pDC, CD11c/HLA-DR+ cDC, CD15/CD66b+ granulocytes, CD14+ monocytes, CD127+ ILC, CD19+ B and CD3+T cells, and even add in a handful of additional markers like CD117, CD16 etc for good measure, there still remains a substantial population of cells remaining ungated. This should lead us to question a few things. First, are we using the right markers and gate logic to define our cells? Second, are the remaining events an artifact of an improperly designed assay or limitation of our technology? If the answer is no to the above, then what are these cells that remain and what is their function… and how did we overlook them for so long? If we sorted these cells, what would single cell sequencing in parallel with high parameter flow cytometry, inform us about their function and the ever-growing understanding of the heterogeneity of the biological condition?

Along the same lines of what we have historically ignored, we might be encouraged to no longer ignore all the events that are dual positive for major lineage markers. For example, in donors experiencing any type of immune activation, bivariate plots initially gated on traditional scatter and both singlet gates (height vs area) and then gated between combinations of CD15+/CD14+/CD3+/CD19+/CD56+ show significant events that are dual positive. These are cells that by their scatter profiles do not look like doublets, and yet it is a very good possibility they are immunologically synapsed cells, effectors caught in act of doing the job they are programmed to do. I find myself often wondering if immunologists studying infection might want to use imaging cytometry to take a deeper look at 11c+ T, B and NK cells to see if it is true marker expression or an artifact of synapsed cells! Although imaging cytometry has been around for decades, the wider implementation to the repertoire of next generation instrumentation has brought interest in these cells back to the limelight. This year saw the release of the Cytek Aurora Evo and the BD FACSDiscover A8. Imaging cytometry still lacks significant ability to resolve in 3D and in the Z dimension… it is not replacing the spatial confidence of confocal microscopy and no one should make localization claims, but we do have the added benefit of being able to both sort events that are either dual positive for the markers of mature cell subsets or use image-based cell sorting of visually confirmed doublets for further analysis.

Another important change in perspective is the slow realization that autofluorescence is not simply a nuisance signature to be spectrally “extracted” from unmixing. Instead that it is an important reflection of the metabolic state of a cell as measured by the individual spectra of different vitamins and metabolic cofactors. It also opens up greater potential applications in marine biology and microbiology. I’ve already written a previous essay on the potential beneficial impact of autofluorescence in spectral flow cytometry and won’t belabor that point here. However, I would leave you with the thought that if scatter/morphology, the basis of the early flow cytometer, is the first dimension and fluorescent reagents providing “-omic” level information is the second dimension, imaging flow cytometry and autofluorescence characterization are the third and fourth dimensions with yet to be tapped potential. To finish off this thought, although Miftek has yet to release a beta unit, they have introduced the potential of the fifth dimension being single photon counting and integration of fluorescence lifetime. I suspect, that by the end of my career, someone will have figured out how to unify all 5 dimensions into a single instrument and it blows my mind the breadth and depth of the phenotypic and functional information we’ll be able to unify in a single cell at a rate of thousands of cells per minute.

I may be biased due to my own involvement in the SOULCAP initiative  an effort born in 2024 with a focus on harmonizing marker combinations and cellular ontology, standardizing gate logic and, eventually, automating aspects of analysis in single cell technologies like CyTOF and flow cytometry. However, standardizing cellular nomenclature has long been a need and ultra-high parameter single cell analysis has laid bare how additive the discrepancies are in harmonizing conclusions across data sets and its culpability in the reproducibility crisis. With AI, the need for consistent nomenclature and data curation for large dataset learning has arrived with urgency. Recently, a working group of the world’s leading T cell biologists addressed this issue within their niche through a Nature publication10. Other efforts have been led by the EuroFlow, HIPC and HCDM consortia, each with their own purpose and focus on increasing harmony and reproducibility. It is time to unify across different biological niches on the elements of cellular nomenclature, ontology and analysis where agreement can be found, to build the foundation for the future.

At the same time that single-cell sequencing was transitioning from proof of concept to mainstream, the spectral detection technology that had already been common in confocal and in vivo microscopy was combined with APD detection arrays in flow cytometry to launch detection capabilities that was once limited to 18-25 colors, to 40+ colors in a matter of a few years. Cytek, Beckman Coulter, Agilent, BD Biosciences and Sony Biotechnology all have very competitive instrumentation platforms capable of 40+ color flow cytometry. But the chemistry of the fluorophore repertoire needed to match the pace of the advances in engineering to fulfill the promise of spectral flow cytometry. These assays required bright, photostable fluorophores with discrete excitation from individual lasers and stable fluorometric spectral ribbons.

On the flip side, spectral flow also shows us ALL the frailties of our methods and our technologies. As the years pass and more people become proficient on these platforms, the squabble over whether post-unmixing compensation is an acceptable practice and necessary evil or the equivalent of photoshopping data continues unabated. By monitoring the emission intensity of so many channels, we can watch tandem fluorophores degrade by light, solvent and simply time before our eyes as the spectrum of the fluorophore shifts to an increasing emission intensity of the donor fluorophore versus its intended emission channel. Even next generation fluorophores still rely upon the transfer of energy between a donor and an acceptor. From a hardware perspective we see instruments assembled with components like lasers and APD arrays that need to be held to a higher ISO-defined material performance specification by the manufacturer to increase the reproducibility between instruments of the same brand and configuration, an issue shared across all manufacturers. The performance variance impacted by these specifications did not impact the capability or reliability of traditional cytometers like it does with spectral platforms. We also need to take seriously the efforts to standardize data output scaling, per John Nolan’s State of the Art lecture at CYTO 2025, to make it easier to translate and compare results run on the same instrument or results from panels using the same markers but different instruments and assay components. This will lay the groundwork for better automated analysis efforts by established companies like Dotmatics and BD/FlowJo, or open up the field for newcomers like Ozette to make bespoke analysis solutions accessible to the average researcher. Although it’d be fair to say that 2025 was not a year filled with novel technology, it was a year for cytometry to grow into its shoes and start to fulfill even more of its promise. I’m excited to see what 2026 holds!

Supporting Your Research

FluoroFinder has developed a suite of tools to streamline your research. Use our Antibody Search function to find antibodies that are validated for flow cytometry, then leverage the Spectral version of our Spectra Viewer to select the best dyes for your spectral instrument. And, if you need a close look at the spectral profiles of different dyes, our Fluorescent Dye Database contains information on more than a thousand different fluorochromes.

 

References:

  1. Park LM, Lannigan J, Jaimes MC. OMIP-069: Forty-Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood. Cytometry A. 2020;97(10):1044-1051. doi:10.1002/cyto.a.24213
  2. Brandi J, Wiethe C, Riehn M, Jacobs T. OMIP-93: A 41-color high parameter panel to characterize various co-inhibitory molecules and their ligands in the lymphoid and myeloid compartment in mice. Cytometry A. 2023;103(8):624-630. doi:10.1002/cyto.a.24740
  3. Kare AJ, Nichols L, Zermeno R, Raie MN, Tumbale SK, Ferrara KW. OMIP-095: 40-Color spectral flow cytometry delineates all major leukocyte populations in murine lymphoid tissues. Cytometry A. 2023;103(11):839-850. doi:10.1002/cyto.a.24788
  4. Park LM, Lannigan J, Low Q, Jaimes MC, Bonilla DL. OMIP-109: 45-color full spectrum flow cytometry panel for deep immunophenotyping of the major lineages present in human peripheral blood mononuclear cells with emphasis on the T cell memory compartment. Cytometry A. 2024;105(11):807-815. doi:10.1002/cyto.a.24900
  5. Waaijer LA, van Cranenbroek B, Koenen HJPM. OMIP-112: 42-Parameter (40-Color) Spectral Flow Cytometry Panel for Comprehensive Immunophenotyping of Human Peripheral Blood Leukocytes. Cytometry A. 2025;107(4):226-232. doi:10.1002/cyto.a.24927
  6. Konecny AJ, Mage PL, Tyznik AJ, Prlic M, Mair F. OMIP-102: 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells. Cytometry A. 2024;105(6):430-436. doi:10.1002/cyto.a.24841Kmet R, Novo D. Reducing Spreading: Removing the Impact of Irrelevant Dyes Improves Unmixed Flow Cytometry Data. Cytometry A. 2025;107(9):573-586. doi:10.1002/cyto.a.24957
  7. Mage PL, Konecny AJ, Mair F. Measurement and prediction of unmixing-dependent spreading in spectral flow cytometry panels. Preprint. bioRxiv. 2025;2025.04.17.649396. Published 2025 May 21. doi:10.1101/2025.04.17.649396
  8. Kmet R, Novo D. Reducing Spreading: Removing the Impact of Irrelevant Dyes Improves Unmixed Flow Cytometry Data. Cytometry A. 2025;107(9):573-586. doi:10.1002/cyto.a.24957
  9. Bhowmick D, Bushnell TP. Impact of Panel Size, Fluorochrome Selection, and Unmixing Algorithms on Ultra-High Parameter Flow Cytometry Analysis. Cytometry A. 2025;107(10):683-694. doi:10.1002/cyto.a.24960
  10. Masopust D, Awasthi A, Bosselut R, et al. Guidelines for T cell nomenclature. Nat Rev Immunol. Published online November 18, 2025. doi:10.1038/s41577-025-01238-2