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Chapter 11.19 : Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay

Editor: Amy L. Leber1
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Affiliations: 1: Editor in chief, Department of Laboratory Medicine, Nationwide Children’s Hospital, Columbus, Ohio
Content Type: Reference
Publication Year: 2016

Category: Clinical Microbiology

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Citation: Leber A. 2016. Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay, p 11.19.1.1-11.19.2.9. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.19
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Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay
Citation: Leber A. 2016. Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay, p 11.19.1.1-11.19.2.9. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.19
/content/10.1128/9781555818814.chap11.19.fig11.19.2-1-a
Figure 11.19.2–1 (A)
Results from a polychromatic ICS assay where cells from a normal donor were stimulated with one specific antigen, CMV pp65 peptide mix (middle row) or SEB (bottom row), as a positive control or were left unstimulated (top row) as a negative control. Each respective gate is shown for all three stimulations. The first gate is time versus FSC-H. Time gates are used as an in-process control. Typically, cytometer performance is measured prior to acquiring a specific sample. By far, the most common run time error that occurs during acquisition of a flow cytometry specimen is either an air bubble or a clog. Air bubbles and clogs may be either small or large and may or may not be noticed by the operator during acquisition. As a means of assessing the quality of the raw data files acquired on a flow cytometer, time may be viewed and used as a gating tool to exclude those events whereby either scatter or fluorescence appears to be non-uniform during a specific window of time over which the sample was acquired. In this figure, time gates are set to exclude the period when the sample flow rate was decreasing due to being near the end of sample acquisition and little sample remained in the tube. No other obvious run time errors are noted using time for this example set of data. Singlet gates are drawn as the second gate, to exclude aggregate or coincidence events. Singlet gates are drawn using FSC-W versus FSC-H to maximize any differences for the FSC signal, helping to identify aggregates or coincidental events more clearly. The third gates are viable T cells gated using CD3 versus an exclusion channel that includes a LIVE/DEAD fixable aqua dead cell stain multiplexed with CD14 and CD19 Pacific blue into one single channel used for exclusion gating. The fourth gates are CD4 versus CD8; note that in this instance, the CD4 CD8 (double positives [DPs]) are included with the CD8 gate. For some samples, the DP cells may be highly antigen specific and the polyfunctional subset may be comprised mostly of DP cells, making it an important subset of cells to evaluate in an ICS assay. DP cells may be gated alone or in combination with either CD4 or CD8 T cell gates. The next series of gates are the functional gates, TNF-α, CD107a, IL-2, and IFN-γ, for both CD4 and CD8 gates, respectively. Functional gates may be paired with either CD3 and CD4 for CD4 gates or CD8 for CD8 gates. When pairing functional markers with CD3, the CD3 dim antigen-specific population is better visualized, making it easier to see whether or not all of the CD3 dim antigen-specific cells have been included in the CD3 gate. When some of the CD3 dim antigen-specific cells are missing from the CD3 gate, the functional population will appear truncated on the CD3 dim side. Functional gates were set for this example using the unstimulated control, setting the positivity threshold to exclude the negative or nonfunctional population and just beyond the halo of cells that cluster near the nonfunctional or negative population that are not positive events. For each functional marker, uniform gates were applied across all stimulations for this donor. The common markers for this panel are CD3, CD4, and CD8. Since these markers are repeated, they may be used as part of the acceptability criteria as potential assessments for repetitive values. For CD3, the maximum value is 70.5 and the minimum value is 66.3, for a spread of 4.2 (maximum minus minimum value). All three markers, CD3, CD4, and CD8, pass the criteria for acceptable repetitive values.
10.1128/9781555818814/fig11.19.2-1-a_thmb.gif
10.1128/9781555818814/fig11.19.2-1-a.gif
Image of Figure 11.19.2–1 (A)
Figure 11.19.2–1 (A)

Results from a polychromatic ICS assay where cells from a normal donor were stimulated with one specific antigen, CMV pp65 peptide mix (middle row) or SEB (bottom row), as a positive control or were left unstimulated (top row) as a negative control. Each respective gate is shown for all three stimulations. The first gate is time versus FSC-H. Time gates are used as an in-process control. Typically, cytometer performance is measured prior to acquiring a specific sample. By far, the most common run time error that occurs during acquisition of a flow cytometry specimen is either an air bubble or a clog. Air bubbles and clogs may be either small or large and may or may not be noticed by the operator during acquisition. As a means of assessing the quality of the raw data files acquired on a flow cytometer, time may be viewed and used as a gating tool to exclude those events whereby either scatter or fluorescence appears to be non-uniform during a specific window of time over which the sample was acquired. In this figure, time gates are set to exclude the period when the sample flow rate was decreasing due to being near the end of sample acquisition and little sample remained in the tube. No other obvious run time errors are noted using time for this example set of data. Singlet gates are drawn as the second gate, to exclude aggregate or coincidence events. Singlet gates are drawn using FSC-W versus FSC-H to maximize any differences for the FSC signal, helping to identify aggregates or coincidental events more clearly. The third gates are viable T cells gated using CD3 versus an exclusion channel that includes a LIVE/DEAD fixable aqua dead cell stain multiplexed with CD14 and CD19 Pacific blue into one single channel used for exclusion gating. The fourth gates are CD4 versus CD8; note that in this instance, the CD4 CD8 (double positives [DPs]) are included with the CD8 gate. For some samples, the DP cells may be highly antigen specific and the polyfunctional subset may be comprised mostly of DP cells, making it an important subset of cells to evaluate in an ICS assay. DP cells may be gated alone or in combination with either CD4 or CD8 T cell gates. The next series of gates are the functional gates, TNF-α, CD107a, IL-2, and IFN-γ, for both CD4 and CD8 gates, respectively. Functional gates may be paired with either CD3 and CD4 for CD4 gates or CD8 for CD8 gates. When pairing functional markers with CD3, the CD3 dim antigen-specific population is better visualized, making it easier to see whether or not all of the CD3 dim antigen-specific cells have been included in the CD3 gate. When some of the CD3 dim antigen-specific cells are missing from the CD3 gate, the functional population will appear truncated on the CD3 dim side. Functional gates were set for this example using the unstimulated control, setting the positivity threshold to exclude the negative or nonfunctional population and just beyond the halo of cells that cluster near the nonfunctional or negative population that are not positive events. For each functional marker, uniform gates were applied across all stimulations for this donor. The common markers for this panel are CD3, CD4, and CD8. Since these markers are repeated, they may be used as part of the acceptability criteria as potential assessments for repetitive values. For CD3, the maximum value is 70.5 and the minimum value is 66.3, for a spread of 4.2 (maximum minus minimum value). All three markers, CD3, CD4, and CD8, pass the criteria for acceptable repetitive values.

Citation: Leber A. 2016. Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay, p 11.19.1.1-11.19.2.9. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.19
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/content/10.1128/9781555818814.chap11.19.fig11.19.2-1-b
Figure 11.19.2–1 (B)
Baseline and post-vaccination results obtained with cells from a malignant-glioblastoma patient enrolled in a therapeutic vaccine trial. Each line on the outside of the pie represents a single function. The colored slices comprising the SPICE pie represent the total response to specific antigen for all functional markers measured in the assay. The CD107a slices are identified by the corresponding blue line circling around the outside of the pie. The slices that are IFN-γ are indicated by the red line circling around the outside of the pie. IL-2 and TNF-α slices are identified by the green and black lines circling around the outside of the pie, respectively. The slices representing a monofunctional population appear as a single colored line (blue for CD107a, red for IFN-γ, green for IL-2, and black for TNF-α). Bi-functional cells appear with two corresponding lines circling around the outside of the pie, and polyfunctional cells are identified by those pieces with three or more lines circling around the outside of the pie. The bright green slice (green arrow) represents a polyfunctional population comprised of CD107a cells (blue line on the outside of the pie), IFN-γ cells (red line on the outside of the pie), and IL-2 cells (green line on the outside of the pie) that are expanded post-vaccination for this subject.
10.1128/9781555818814/fig11.19.2-1-b_thmb.gif
10.1128/9781555818814/fig11.19.2-1-b.gif
Image of Figure 11.19.2–1 (B)
Figure 11.19.2–1 (B)

Baseline and post-vaccination results obtained with cells from a malignant-glioblastoma patient enrolled in a therapeutic vaccine trial. Each line on the outside of the pie represents a single function. The colored slices comprising the SPICE pie represent the total response to specific antigen for all functional markers measured in the assay. The CD107a slices are identified by the corresponding blue line circling around the outside of the pie. The slices that are IFN-γ are indicated by the red line circling around the outside of the pie. IL-2 and TNF-α slices are identified by the green and black lines circling around the outside of the pie, respectively. The slices representing a monofunctional population appear as a single colored line (blue for CD107a, red for IFN-γ, green for IL-2, and black for TNF-α). Bi-functional cells appear with two corresponding lines circling around the outside of the pie, and polyfunctional cells are identified by those pieces with three or more lines circling around the outside of the pie. The bright green slice (green arrow) represents a polyfunctional population comprised of CD107a cells (blue line on the outside of the pie), IFN-γ cells (red line on the outside of the pie), and IL-2 cells (green line on the outside of the pie) that are expanded post-vaccination for this subject.

Citation: Leber A. 2016. Measurement of Antigen-Specific Cellular Responses Using the Polychromatic Flow Cytometry Intracellular Cytokine Staining Assay, p 11.19.1.1-11.19.2.9. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.19
Permissions and Reprints Request Permissions
Download as Powerpoint

References

/content/book/10.1128/9781555818814.chap11.19
1. McLaughlin BE, Baumgarth N, Bigos M, Roederer M, De Rosa SC, Altman JD, Nixon DF, Ottinger J, Oxford C, Evans TG, Asmuth DM. 2008. Nine-color flow cytometry for accurate measurement of T cell subsets and cytokine responses. Part I: panel design by an empiric approach. Cytometry A 73: 400– 410.
2. Murdoch DM, Staats JS, Weinhold KJ. 2012. OMIP-006: phenotypic subset analysis of human T regulatory cells via polychromatic flow cytometry. Cytometry A 81: 281– 283.
3. Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay P, Roederer M. 2006. Quality assurance for polychromatic flow cytometry. Nat Protoc 1: 1522– 1530.
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6. Roederer M. 2001. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45: 194– 205.
7. Chattopadhyay PK, Gaylord B, Palmer A, Jiang N, Raven MA, Lewis G, Reuter MA, Nur-ur Rahman AK, Price DA, Betts MR, Roederer M. 2012. Brilliant violet fluorophores: a new class of ultrabright fluorescent compounds for immunofluorescence experiments. Cytometry A 81: 456– 466.

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