Improving Precision Cell Analysis with Advances in Spectral Flow Cytometry

The evolution of flow cytometry has been a critical contributor to biopharmaceutical innovation, offering researchers the opportunity to profile complex biological systems with more precision than they could with traditional technologies. Yet science never stops innovating, which is why it’s particularly exciting to see the evolution continue with spectral flow cytometry. Technological advances are making it more accessible to laboratories so they can accelerate answers for many complex questions that otherwise may not have been possible to address. Spectral flow cytometry enables investigators to gain new insights into tumor heterogeneity, immune evasion, and more, thus providing a path to finding novel therapeutic targets.

Unlike traditional flow cytometry, which only measures the peak of fluorescence in optical filters of varying width arranged in a discontinuous fashion, spectral flow cytometry collects the full spectrum of light emitted by each fluorochrome. It achieves this by using iterative filters or prisms that leave few if any gaps in collecting the spectra. Even relatively small differences in the spectral signature between fluorochromes, fluorescent proteins, or dyes can be sufficient to properly separate one from another. The shift to pattern recognition over intensity enhances the flexibility in experimental design and enables researchers to confidently characterize significantly more parameters simultaneously even when challenged by varying intensity.

With the advance to spectral flow cytometry, however, comes significant challenges, not the least of which is that it makes setting up experiments more complex. Technology providers can work hand-in-hand with researchers to overcome these challenges and streamline spectral flow cytometry workflows.

Spectral flow in action

Embedded in spectral flow cytometry are datasets that can reveal more complex relationships than what can be displayed in simple bivariate plots. The analysis technology paired with spectral flow cytometry empowers researchers to display datasets with meaningful relationships in a two-dimensional space using a process called dimensionality reduction. With the appropriate statistical-analysis tools—which are increasingly being paired with machine learning—they can create sophisticated data maps. These maps can be used to compare sample results broadly and quickly without the need for extensive, manual data reviews.

Several examples of how spectral flow cytometry is benefiting researchers across a range of therapeutic areas have emerged in recent years. For example, one team of immunology researchers reported last year that they created a 50-color spectral flow cytometry panel that enabled in-depth analyses of T cells and antigen-presenting cells, while simultaneously measuring B cells, natural killer cells, and innate lymphoid cells. The use of 50 reagents specific for a wide range of protein markers allowed researchers to capture previously unseen details about the differentiation and activation of T cells and antigen-presenting cells.¹

Spectral flow cytometry is benefiting cancer research by providing a practical way to deal with what are typically incredibly limited samples from pediatric patients. By extracting more answers from fewer samples, researchers are able to determine disease state in ways that would have been much more difficult using smaller panels spread across a larger number of samples. The technology also makes it easier to make use of limited samples from preclinical models, allowing researchers to start with broad questions about, say, immunological activity in cancer, and then drill down into detailed investigations based on preliminary data.

Optimizing assays

To get the most out of the capabilities spectral flow cytometry offers, researchers must overcome complex challenges to optimize assays. Errors can come from basic biology or the technology itself, but regardless of the source, the best way to reduce the risk is to make the detection task less challenging.

A major challenge of spectral flow cytometry is that if the selected fluorescence reporters are too highly overlapping, users won’t be able to effectively unmix them, which will interfere with their ability to discern any meaningful quantitative information from the assays. Effective unmixing requires robust single-stain controls that can match the spectral signatures, allowing fluorochromes that have similar emission but different spectral signatures to be distinguished from each other. There are currently four popular unmixing algorithms, each of which offers advantages and disadvantages in terms of accuracy with low data spreading, sensitivity to outliers, ease of implementation and other factors.²

Another challenge is autofluorescence, in which certain cellular components and metabolites in samples fluoresce in response to specific wavelengths of light. Changes in autofluorescence can serve as valuable markers of disease in cells and tissues, including the aging of nerve and skin cells and the evolution of cancer cells. However, autofluorescence can contribute to background, making it difficult to distinguish dim populations of cells of interest.³ One potential solution is to identify the distinct autofluorescent characteristics of the sample and develop new panels that include those spectral signatures as additional parameters for phenotyping, which can help researchers distinguish the spectral contributions of autofluorescence. Then they can use computational tools to remove them from the signals of interest.⁴

The AI revolution

Spectral flow cytometry is rapidly evolving in ways that will improve its efficiency, effectiveness, and breadth of use. We’re on the cusp of an AI revolution that will greatly enhance the sophistication of panel design and the ability to automate the process of gating cells of interest. Reducing subjectivity will be critical as scientists start to use spectral flow cytometry to diagnose more varied disease states.

The technology will continue to improve, offering researchers an expanded toolbox they can use to discern information about cells that was difficult to obtain before, such as cell morphology and organelle activity. They may even be able to combine fluorescent and non-fluorescent technologies to answer complex questions, and AI will enable them to make better decisions with bigger datasets.

For the industry to derive the maximum benefits spectral flow cytometry provides, it’s important to strive for data comparability between instruments and methods. The National Institute of Standards and Technology (NIST) has established a consortium that aims to develop reference standards, methods, and materials. Complementary to this is SOULCAP, a consortium focusing on standardizing the semantic description of cells in hierarchical models, so that researchers are working with a common nomenclature based upon observed phenotypes.

Ultimately, spectral flow cytometry will enable researchers to do much more than discern cellular identity, activity, and health. It will also advance the industry’s ability to probe non-cellular entities like freely circulating DNA. This will be useful for efforts such as rapid microbe identification. No doubt the future is bright for spectral flow cytometry—and it will continue to improve as researchers and technology developers work together to overcome challenges and make the most of what this evolving technology has to offer.

Matthew Goff has nearly two decades’ experience in biosciences, working in research, commercial, and product development roles. As the Senior Manager of the Hardware Portfolio for the Beckman Coulter Life Sciences flow cytometry division, he oversees the evolution and sustainment of the company’s analytical, sorting, and preparatory solutions.

References

1. Konecny, A., et al. (2024). OMIP-102: 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells. Cytometry Part A. Volume 105, Issue 6.

2. Unlocking Insights: The Vital Role of Unmixing Algorithms in Spectral Flow Cytometry. Beckman Coulter Life Sciences. 2024.

3. Unveiling the Hidden Signals: Overcoming Autofluorescence in Spectral Flow Cytometry Analysis. Beckman Coulter Life Sciences. 2024.

4. Jameson, V., et al. (2022) Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects. Cytometry Part A. Volume 101, Issue 11.