Spectral Flow Cytometry Panel Design

After antigens have been tiered appropriately and a gating strategy has been determined (See Flow Cytometry Experimental Process), designing a panel involves selecting and pairing markers with fluorophores to optimally resolve cellular subsets and detect antigens of interest (Figure 1). To simplify the review process, reagents can be organized in a table to quickly reference fluorophore assignment, explore fluorophores emission maxima, and forecast potential cross-excitation and spread (Table 1).

Table 1. Fluorophore table. Common fluorophores listed by primary laser excitation and approximate primary peak emission. Fluorophores which have very similar peak excitation and emission are listed together in the same block (i.e., PE and Alexa Fluor 561). Using a table such as this is helpful when pairing fluorophores with markers.

Reagent availability can be checked online in vendor catalogs or with the assistance of a reagent database or a panel builder tool. Sophisticated panel builder tools are dynamically linked to a fluorescent reagent database and adjust availability based on the choice of targets, in addition to interactive antigen density information and theoretical fluorophore brightness. These tools help guide users through the panel design process by saving time and preventing the selection of incompatible fluorophores. Some instruments offer integrated spectral viewers based on the specific configuration used (Figure 2). To expand reagent options, antibodies can be conjugated to fluorophores using conjugation kits or through a vendor’s custom conjugation program.

A.

Figure 2. Selection of 30 fluorophores. The Invitrogen Bigfoot Spectral Cell Sorter with Sasquatch Software (SQS) provides the ability to spectrally unmix data in real time. The SQS has on-board tools for fluorophore selection, with a complexity index that tallies as each new fluorophore is added to track the quality of the overall panel (A), and a visual representation of the selected panel of fluorophores (B). The complexity value increases when there is more predicted overlap between spectral signatures, fluorophores that minimally increase the complexity are ideal choices. In this example, the 30 fluorophores are spread out over the spectrum giving the panel a 7.70 complexity index. The spectral similarity matrix of SQS allows identification of fluorophore combinations that cause an increase in complexity in an easy visual that shades combination from low to high similarity (C) to inform optimal pairing with markers.

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In addition to antibodies used to identify specific antigens of interest, a viability dye should be selected to exclude dead/dying cells from the analysis (read more about this topic in BioProbes Journal—Checking Vital Signs: Don’t Let Dead Cells Mislead You). Using light scatter alone to gate on live cells is insufficient as antibodies can non-specifically bind to dead cells, causing false positive results, and affecting signal resolution. The most common reagents used to identify dead cells are amine-reactive dyes (i.e., LIVE/DEAD Fixable Dead Cell stains), impermeant nucleic acid-binding dyes (i.e., propidium iodide, DAPI), and enzymatic substrates (i.e., calcein, AM).

Once the available reagents have been identified and their properties defined, fluorophores and antibodies can be matched with the following guidelines [2] (Figure 3).

• Antibodies used to detect tertiary antigens can be assigned to the brightest fluorophores, while working to minimize spread to maximize resolution.
• Antibodies used to detect secondary antigens, if not co-expressed, can be assigned to any bright fluorophores remaining available. If secondary antigens are co-expressed, assign them to bright fluorophores that do not spread to co-expressed antigens or tertiary antigens. When fluorophores without additional spread are unavailable, select a dim fluorophore to reduce spread and loss of resolution.
• Antibodies used to detect primary antigens can be assigned to dim fluorophores with low spread. For more information on antibodies used in spectral flow cytometry please see: Flow Cytometry Experimental Process—Spectral versus Conventional.
• Prioritize reagent selection to limiting reagents such as fluorescent proteins and functional dyes that must be included in the panel.
• Some antibodies may only be conjugated to a few fluorophores, and these can be prioritized irrespective of the antigen tier.
• For panels that may have many co-expressed markers, avoid using highly similar fluorophores. An example of this would be a panel used to study many markers on a single cell type.
• For markers that are co-expressed, pair with fluorophores having minimal spread.
• For fluorophores that receive a lot of spread, assign a marker that has on/off expression

Keep in mind that panels that are larger or have many co-expressed markers will be more challenging to design.

Figure 3. Pairing antigen density with fluorophore brightness. The x-axis in each of these data plots shows optimal pairing of antigen density and fluorophore brightness.
 

Lastly, to prevent a spectral signature disparity, a dump channel or dump gate is not recommended in spectral flow cytometry. A dump channel refers to the process of intentionally excluding cells that are positive for multiple markers (often lineage-restricted), including viability stains. Traditionally, this approach was used to overcome limitations in the number of fluorescence detectors by pooling several markers in one detector. However, spectral flow cytometry does not have this same restriction and using multiple near-identical fluorophores may cause issues with unmixing. Separating out these markers for individual detection will enable even deeper interrogation of cell heterogeneity, rather than ignoring their contribution [3].

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Light scatter properties are often the first step in distinguishing cells from debris and electronic noise and may be used to identify basic cellular subpopulations. When a cell passes through a laser, it scatters light in all directions. Using light scatter alone, cells may be separated into basic sub-populations in a heterogenous sample, such as lymphocyte, monocyte, and granulocyte populations in lysed whole blood. Even when best practices to limit cellular aggregation are followed in preparing a single-cell suspension, small clusters of cells may remain, and cells may aggregate. These small cellular aggregates may be distinguished from single cells using combinations of scatter signal (i.e., Forward Scatter (FSC)-Area vs. FSC-Height), such that single cells will form a diagonal population and any clusters of cells will fall outside the diagonal. It is also beneficial to observe a confirmatory image of the cells. Using the time parameter to observe a plot of the time vs. scatter or fluorescence will help identify fluidic inconsistencies, laser issues, or problems with the sample. Dead or dying cells can be distinguished from living cells using a viability marker, as dead/dying cells may non-specifically bind to antibodies and confound the true expression on live cells. Gating on viable, single cells is critical for high quality flow cytometry data (Figure 4).

Figure 4. Hierarchical gating example. A representative sequential gating strategy shows the main lymphocyte population (A), single cells (B), viable cells (C), CD3 vs surface CTLA-4 populations (D).

Explore: Flow cytometry panel builder

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Before obtaining the reagents and piloting the initial panel, the panel design should be reviewed in silico using various tools to confirm the selection and combinations of reagents. Once a panel of fluorophores is selected, a complexity index can be used to evaluate the overall similarity for all spectra within a proposed panel. The complexity measurement takes the similarity measurements for each pair of fluorophores for the full panel into account. A lower complexity measurement indicates the likelihood of having lower spread resulting in higher-resolution data. Complexity increases as panels grow, but it can be mitigated through the careful selection of fluorophores sharing low similarity scores. To further corroborate that the best combinations of fluorophores have been chosen, the results of the spread matrix can be checked, as well as the similarity measurements. The panel design process is theoretical, and after initial panel design, testing the panel on cells is imperative. Panel design is an iterative process requiring empirical evidence to assess performance and multiple attempts may be necessary to ensure optimal target population identification.

1. 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.
2. Mahnke, Yolanda D., and Mario Roederer. "Optimizing a multicolor immunophenotyping assay." Clinics in laboratory medicine 27.3 (2007): 469–485.
3. Boesch, Maximilian, Antonio Cosma, and Sieghart Sopper. "Flow Cytometry: To Dump or Not To Dump." Journal of Immunology (Baltimore, Md.: 1950) 201.7 (2018): 1813–1815.
4. Stewart, Judith C., Michelle L. Villasmil, and Mark W. Frampton. "Changes in fluorescence intensity of selected leukocyte surface markers following fixation." Cytometry Part A: the journal of the International Society for Analytical Cytology 71.6 (2007): 379–385.
5. Cossarizza, Andrea, et al. "Guidelines for the use of flow cytometry and cell sorting in immunological studies." European journal of immunology 15.12 (2021): 2708–3145.
6. Ferrer‐Font, Laura, et al. "Panel Optimization for High‐Dimensional Immunophenotyping Assays Using Full‐Spectrum Flow Cytometry." Current Protocols 1.9 (2021): e222.
7. Novo, David, Gérald Grégori, and Bartek Rajwa. "Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices." Cytometry Part A 83.5 (2013): 508–520.