Quantification of T Cell Antigen-specific Memory Responses in Rhesus Macaques, Using Cytokine Flow Cytometry (CFC, also Known as ICS and ICCS): Analysis of Flow Data

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What was initially termed ‘CFC’ (Cytokine Flow Cytometry) is now more commonly known as ‘ICS’ (Intra Cellular Staining), or less commonly as ‘ICCS’ (Intra Cellular Cytokine Staining). The key innovations were use of an effective permeant (allowing intracellular staining), and a reagent to disrupt secretion (trapping cytokines, thereby enabling accumulation of detectable intracellular signal). Because not all researchers who use the technique are interested in cytokines, the ‘ICS’ term has gained favor, though ‘CFC’ will be used here.
CFC is a test of cell function, exposing lymphocytes to antigen in culture, then measuring any cytokine responses elicited. Test cultures are processed so as to stain cells with monoclonal antibodies tagged with fluorescent markers, and to chemically fix the cells and decontaminate the samples, using paraformaldehyde.
CFC provides the powers of flow cytometry, which includes bulk sampling and multi-parametric cross-correlation, to the analysis of antigen-specific memory responses. A researcher using CFC is able to phenotypically characterize cells cultured with test antigen, and for phenotypic subsets (e.g. CD4+ or CD8+ T cells) determine the % frequency producing cytokine above background level.
In contrast to ELISPOT and Luminex methods, CFC can correlate production of multiple cytokines from particular, phenotypically-characterized cells. The CFC assay is useful for detecting that an individual has had an antigen exposure (as in population screenings), or for following the emergence and persistence of antigen memories (as in studies of vaccination, infections, or pathogenesis). In addition to quantifying the % frequency of antigen-responding cells, mean fluorescence intensity can be used to assess how much of a cytokine is generated within responding cells.
With the technological advance of flow cytometry, a current user of CFC often has access to 11 fluorescent channels (or even 18), making it possible to either highly-characterize the phenotypes of antigen-responding cells, or else simultaneously quantify the responses according to many cytokines or activation markers. Powerful software like FlowJo (TreeStar) and SPICE (NIAID) can be used to analyse the data, and to do sophisticated multivariate analysis of cytokine responses.
The method described here is customized for cells from Rhesus macaque monkeys, and the extensive annotating notes represent a decade of accumulated technical experience. The same scheme is readily applicable to other mammalian cells (e.g. human or mouse), though the exact antibody clones will differ according to host system. The basic method described here incubates 1 x 106 Lymphocytes in 1 ml tube culture with antigen and co-stimulatory antibodies in the presence of Brefeldin A, prior to staining and fixation.
Note: This is the second part of a two-part procedure. Part one has the same initial title, but the subtitle “From assay set-up to data acquisition (Sylwester et al., 2014)”. The Abstract and Historical Background is the same for both documents.

Keywords: ICS(ICS), CFC(CFC), Cytometry(流式细胞仪), Memory(记忆), PBMC(外周血单个核细胞)

[Historical Background] In 1988, Andersson, et al. first demonstrated how lymphocytes could be fixed, permeabilized, stained with antibodies against IFNg, then fluorescently labeled and enumerated by flow cytometry. In 1991, Sander et al. demonstrated improved methods to fix cells with paraformaldehyde, permeabilize them with saponin, then use fluorescently-labeled antibodies to stain intracellular cytokines for microscopic examination. In 1993, Jung et al. extended this method to use with flow cytometry, and included the use of monensin to disrupt secretion, so as to increase intracellular signal of molecules otherwise released soon after synthesis. In 1995, Prussin and Metcalfe used directly-conjugated antibodies, and optimized the incubation period to 6 h. Also in 1995, Picker et al. considerably enhanced the sensitivity and reproducibility of cytokine detection by using Brefeldin A to block the secretion apparatus for cytokines, and by using a different permeant (Tween-20). In 1997, this matured method was applied by Picker et al. to study the the antigen-specific homeostatic mechanism in HIV+ patients.  In 2001, Schuerwegh et al. confirmed that BfA provides for better cytokine signals than monensin, used by others in this method.
In two reports in 1989, one by Gardner et al. and the other by McClure et al. reported that Rhesus macaques were a useful model for studying HIV disease and AIDS. In 2002, Picker et al. reported the application of the CFC assay to Rhesus macaques. In 2012, a group created to develop multi-lab standards for use of ICS in NHP vaccine studies published their recommendations for a 96-well plate method with a 6 h total incubation (Donaldson et al., 2012 and Foulds et al., 2012).
The general procedure reported here is that 2002 tube-format method, now with a 9 h total incubation, and optimized especially for low-end sensitivity. The specific details here are the state of the art now practiced by the Louis Picker Lab, at the Oregon Health and Science University, affiliated with the Oregon National Primate Research Center. These methods have been used in several of our recent publications (Fukazawa et al., 2012; Hansen et al., 2011; Hansen et al., 2009). It is important to note that in our hands, plate-format ICS is not as reliable or sensitive for weak responses as is this tube-based method (unpublished observations). Until that problem is understood and solved, the tube-based method remains the most-sensitive format for CFC.

Materials and Reagents

  1. Data required: Flow cytometry data files, from CFC assay samples as described in “From Assay Set-up to Data Acquisition” (Sylwester et al., 2014).


  1. Incubator for tissue culture (humidified, stable at 37 °C, 5% CO2 atmosphere)
  2. Flow cytometry analyser


  1. Software-equipped workstation, for analysis of cytometry files
    e.g. FlowJo (Tree Star)
    e.g. SPICE (NIAID)


  1. Choosing a gating strategy
    Because there are so many ‘right’ ways to analyse data, this document will not attempt to impose a definitive prescription. Rather, this document presents one rigorous example that illustrates the role and value of advisable steps, some essential, others optional. This example is an illustration of the second of two mindsets one can have when gating: (1) An analyst can seek to minimize gating steps by custom-crafting well-fitting gates to each datafile. The advantages of this approach are fewer steps, and perhaps higher elegance. The drawbacks are that each gate will have many control points, and require careful and time-consuming adjustment when applied to a new file. This strategy is appealing when an analyst has relatively few files to gate, but cumbersome when confronted with hundreds or thousands of files. (2) Alternately, an analyst can seek to minimize time spent adjusting gates after applying a gating hierarchy to a set of files. This strategy makes use of many gates, each large and forgiving enough that they individually require little attention or adjustment as files change, but with the aggregate effect of ‘cleaning’, ‘focusing’, and ‘winnowing’ the data, and achieving the same quality of gating as the first strategy.
    The illustration below is this second approach, making use of many ‘low-attention’ gates.
    1. Outlined gating hierarchy
      1. ‘Data cleaning’ steps
        1. Data reduction gate (optional)
        2. Time vs CD45 (time advised, but CD45 optional)
        3. Singlets (advised)
        4. Drop aggregates (optional)
        5. CD45 vs CD3 (optional)
        6. Specific stain, CD3 cleanup (optional)
        7. Small lymphocytes (essential)
        8. Additional cleanup of T cell population (optional)
      2. Division into CD4+ and CD8+ T cells
        1. CD4 vs CD8
        2. CD4+, CD4-, followed by CD8+ in CD4- population
      3. Gating cytokine responses
        1. CD69 vs cytokine

  2. ‘Data cleaning’ steps
    1. Data reduction gate (optional)
      Most analysis software depicts data as density plots, illustrated with topographical lines or color-coding. Often, the greatest event density is in the debris field in the lower left corner of a scatterplot, or in debris aggregates in the upper left corner. Since CFC is focused on analysis of lymphocytes, it is not necessary to leave in anything else. Unfortunately, when all these irrelevant events are in the scatterplot, their presence reduces the ‘resolution’ an analyst has regarding the margins of the lymphocyte population. Therefore, crudely removing most of the irrelevant data will improve resolution in the very next gate. It is possible to make a ‘Data Reduction’ gate that reliably works with all data files, needing only the most-minimal attention, yet significantly improving resolution immediately. An example is shown below:

      Notice that the highest event density is in the debris field in the lower left, and that the leftmost margin of the small lymphocyte population is obscured by blending with the rightmost margin of the debris field. The oval gate, in this example, removes 30% of the events from subsequent analysis.

    2. Time vs CD45 (time advised, but CD45 optional; CD3 is an alternate option)
      Cytometers that feed sample into the instrument by pressurizing commonly produce pressurizing artifacts when data recording begin and ends. Sometimes, other pressure effects (e.g., breakup of sample clumps) alter the data in between. It is therefore highly advised that the time-course data be checked, and data at the start/stop boundaries be removed. If the time gate data ‘centroid’ ever moves, it is likely that data placement has also shifted in downstream gates. It is therefore advisable that analysis only consider data segments with continuous time gate centroids.
      It is not necessary to use CD45 for this analysis, but if CD45 can be used, it is convenient here. Otherwise, any parameter (CD3, or even SSC or FSC) is interchangeable with CD45 in the examples below.
      Examples of ‘clean’ time data:

      Note that the gate is generous along the CD45 axis. This is because we do not want to exclude CD45 dim WBC, and our general method is to apply many ‘low-maintenance’ gates, whose aggregate effect is to produce clean, focused data.
      Examples of pressure artifacts not changing the data centroid:

      Example of a pressure effect changing the centroid, and affecting gating results downstream:

      It is very important to exclude events that may be collected after a tube is sucked dry, since these events can affect cyotokine-response quantification.
      Below is an example wherein the time gate extends past the sample, and the cytokine response plot that comes from it:

      Here is the same datafile, with the time gate restricted to the sample. Notice the profound difference in the cyotokine response value, and the trivial cause of this difference.

    3. Singlets (advised)
      Amoeboid cells moving in a pipe tend to adopt a spherical shape, such that diameter is proportional to cross-sectional area. Therefore, if one plots FSC-H (H = height) vs FSC-A (A = area) (and/or SSC-H vs SSC-A), a line going up the midline should result. Deviations off that line can occur when two cells are stuck together (a ‘doublet’, forming a ‘figure-eight’). Triplets, quadruplets, and higher-order clusters look progressively like spheres again, so their deflections become less and less from the midline. But since the greatest deflection (doublets) is also the most common small cluster, it is easy to remove these contaminants (That might otherwise suggest that a particular event is simultaneously a B and T cell, or CD4+ and CD8+ cell, or might combine the response outcomes of two or three separate cells.). Other cytometrists prefer to plot FSC-H vs. FSC-W, because Area is an integrated value, whereas Height is a particle’s maximum signal and Width is a particle’s duration of signal (i.e., size), and independent of PMT voltage. What is described here is what the Picker Lab actually does. If Area Scaling is set properly (with cells), H vs A should line up at the midline. If two (or more) cells are stuck together, signal area will no longer line up with signal height.
      Examples of this gate is given below:

      Note: The debris in the lower left corner of the displayed data. In the first example it is gated out. In the second example, the gate could be improved by adjusting it to reduce that debris. The subpopulations are shown more clearly in the stylized illustration.

    4. Drop aggregates (optional)
      Although the singlet gate removes most of the low-cell clusters from analysis, the singlet gate does poorly with higher-order clusters that made it past the ‘data reduction’ gate. Even when these are rare, they can cause havoc when a researcher is focusing on very weak responses, because even a few such events can significantly affect the % in a gate of very few events. Fortunately, it is simple to install a low-maintenance gate that reliably removes the threat of these events, a gate that takes advantage of the fact that clusters of cells will behave as events (‘cells’) that are unnaturally bright.
      Be careful with this gate, however, because you can sometimes get very robust responses like:
      In the five panels below, it is important to notice that these axes are logarithmic, but include zero, and have events apparently extending below ‘zero’, and off the plot. When crafting any gate meant to include events below the lower limit of the axis, be sure to create an oversized gate that extends WELL past the lower boundary. That way, it is less likely you’ll unintentionally truncate your data. In the fifth plot below, note that the cytokine-positive events are so bright that they’re going off-scale. In this case, it is important to extend the gate off-scale so that these events are captured. The drawback to this is that aggregates will also be perpetuated in the data.

      In the examples above, the first panel is a no-antigen (no-response) sample, still with considerable pre-gated TNFa signal, and with some IFNg signal. The second panel of the upper row shows cells responding to superantigen SEB, with a notable population that is responding with both IFNg and TNFa. The first panel of the second row shows a population weakly responding to SIV Gag peptide mix. The last panel shows a diagonal artifact commonly induced by toxic peptides in SIV Env peptide mixes. Our lab refers to this as the ‘death spike’, and whichever of these events that persist through gating contaminate downstream plots by running up the midline. Leaving this artifact in can dramatically – and incorrectly -- inflate the reported response.
      Note: In all the examples above, there are super-bright events on the top and far-right axes.

    5. CD45 vs CD3 (optional)
      If the staining panel has incorporated CD45, an excellent use is to apply it here. CD45 is a marker on all leukocytes (WBC), and so this particular gate is highly valuable when the CFC is investigating responses in tissues in which lymphocytes are a minority population [e.g. lung wash (< 5%), small intestine, liver, vaginal mucosa, etc.]. Without CD45, one can still reduce non-leukocyte contamination with a CD3 vs CD4 (or CD8) gate, but it doesn’t produce as satisfying as cleanse. Moreover, this gate allows some reduction of any ‘death spike’ that’s present, or subcellular debris released when cells go through a freeze-thaw cycle.
      Examples of this gate are shown below:

      The top row shows a negative and weakly-responding sample, respectively. The bottom row shows samples showing the ‘death spike’ induced by SIV Env toxic peptides. Notice how this gate can reduce death spike events from downstream analysis.
      In the absence of a death spike, this gate tends to be reliably low-maintenance, but very effective at cleaning out debris that is otherwise hard to remove.

    6. Specific stain, and CD3 cleanup 1 (optional)
      If your panel allows the luxury of an unused channel, you can obtain benefit from actually collecting data from that parameter. The idea is simple: If you have positive events in an unused channel, they cannot possibly be valid. Therefore, this gate is an opportunity to remove them, and it is very valuable when trying to measure very weak, threshold-level responses.
      But this same gate can serve another purpose when the antigen roster includes something like SIV Env peptide mix, which includes toxic peptides. It can be very difficult to remove all the cells affected by these toxic peptides, because live-dead stains don’t always gate them out, and because the events characteristic of a ‘death spike’ run up the midline, often obscuring populations that interest us. By using a plot that combines an unused channel vs CD3, the CD3+ population that interests us is shoved off the midline, and any death spike ‘glances’ it, in a way that can be partially gated out.
      Examples are shown below:
      The first panel shows a typical, well-behaved sample. The second panel in the top row shows the location of Env toxic peptide-induced carcasses (notice how the gate reduces them from the downstream data). The second row panel shows an example with a surprising amount of positive signalling, even though that fluorophore was not used in the stain.

    7. Small lymphocytes (essential)
      In CFC, it is essential that the analysis be focused first, in a scatterplot, on lymphocytes, then separately by CD3 fluorescence, on the subset of small lymphocytes that are T cells. Thus, this gate is essential. In the gating scheme shown here, the first gate (‘Data Reduction’) crudely approximated the small lymphocyte population. But that gate is often blended with the debris field, and in some tissues (e.g. lung wash, intestine, liver) the debris can be so overwhelming that an analyst is forced to guess where the lymphocytes are in a scatterplot. Moreover, it is important to stress that pure B lymphocytes and NK lymphocytes have scatter profiles distinct, though overlapping, with T lymphocytes. It is therefore beneficial to apply the ‘Small Lymphocyte’ gate after the population has gone through a CD3+ gate.
      Below are examples of original scatterplots, showing how well the previous steps have clarified the data and improved the resolution of the lymphocytes, and how a tighter (and more believable) ‘Small Lymphocyte’ gate can now be applied:

    8. Additional cleanup to the T cell (CD3+ small lymphocytes) population (optional)
      Discrete populations often have a ‘fuzz’ of events between them, complicating the decision of where to place gates. Some plots make the decision clearer, and this gating step is like that. At this stage, we have a fairly clean population of T cells, except for the problem that the bright CD3+ is usually trailed by a smear of CD3-medium events, down into a CD3-population (which we have removed). The problem is that CD3-medium population; should we (can we) gate any parts out?
      By plotting CD3 vs CD69 (an activation marker), or alternately by plotting CD3 vs a cytokine, the CD3-medium population is resolved in a way that helps with gating:

      The first panel above shows a sample lacking any test antigen; the second panel shows sample stimulated with superantigen SEB. Note how in both cases it’s clear where to draw the gate against CD3-medium/dim cells.
      Below, a different SEB sample is shown first by CD3 vs CD69, and then by CD3 vs TNFa. Either way can be used, though CD69 activation is more reliable than TNFa, since not all cells elaborating TNFa also elaborate IFNg or IL2, etc.

      This gate is also useful for removing more ‘death spike’ cells from analysis, as the SIV Env peptide mix examples below illustrate:

  3. Division into CD4+ and CD8+ T cell populations (essential)
    Now that one has ‘clean’ T cells, there are two ways of separating them into separate CD4+ and CD8+ T cell bins: (1) A direct plot of CD4 vs CD8. This has the virtue of simplicity, but the drawback of sometimes having ambiguity with where to put the CD4+CD8+ double-positive population (If, indeed, it will be included at all; in the examples below, the CD4+CD8+ population is included with the CD4+ T cells. A user can opt to gate the CD4+CD8+ double-positive T cells as a separate, unique population.). (2) A two-step approach that yields more consistent results, but involves more input.
    1. CD4 vs CD8
      The direct approach:

    2. Reducing the SIV Env toxic peptide-induced ‘death spike’:

    3. (CD4+, CD4-), followed by (CD8+ in CD4- population)
      The two-step approach: (1) CD4+, CD4-, then (2) regate CD4- for CD8+

      Reducing the SIV Env toxic peptide ‘death spike’: The goal is to remove the debris spike, and the gate-shape above is just a suggestion.

  4. Gating cytokine responses
    1. CD69 vs cytokine
      Everything up to this point has been with the goal of getting maximally-clean populations of CD4+ and CD8+ T cells. The last step is to gate the antigen-stimulated cytokine-positive cells. The Picker Lab plots activation marker CD69 vs the various cytokines (TNFa, IFNg, MIP1b, IL2, etc), because we have seen sometimes significant cytokine signal emanating from unactivated (i.e., CD69-) cells. If your tests produce robust positive responses, this may not matter to you. However, if you, like us, are interested in very weak responses, then inclusion of any events that aren’t actually antigen-stimulated is a degradation of assay sensitivity.
      Example of a ‘negative’ sample, showing IL2 emanating from the CD69- population:

      Examples of how SEB responses are gated.
      Note: The numbers in the upper right corners report the % of all events in the plot that are in the gate; that is, the ‘% frequency of responding cells.

      Example of an SIV Gag peptide mix response:


The methods described in this protocol have been evolving since the 1995 paper cited in this protocol (Picker et al., 1995), and were used in the two 2013 papers published by our group (Hansen et al., 2013a and Hansen et al., 2013b). Funding has been provided by NIH and by the Bill and Melinda Gates Foundation.


  1. Andersson, U., Hallden, G., Persson, U., Hed, J., Moller, G. and DeLey, M. (1988). Enumeration of IFN-γ-producing cells by flow cytometry. Comparison with fluorescence microscopy. J Immunol Methods 112(1): 139-142.
  2. Donaldson, M. M., Kao, S. F., Eslamizar, L., Gee, C., Koopman, G., Lifton, M., Schmitz, J. E., Sylwester, A. W., Wilson, A., Hawkins, N., Self, S. G., Roederer, M. and Foulds, K. E. (2012). Optimization and qualification of an 8-color intracellular cytokine staining assay for quantifying T cell responses in rhesus macaques for pre-clinical vaccine studies. J Immunol Methods 386(1-2): 10-21.
  3. Foulds, K. E., Donaldson, M. and Roederer, M. (2012). OMIP-005: Quality and phenotype of antigen-responsive rhesus macaque T cells. Cytometry A 81(5): 360-361.
  4. Fukazawa, Y., Park, H., Cameron, M. J., Lefebvre, F., Lum, R., Coombes, N., Mahyari, E., Hagen, S. I., Bae, J. Y., Reyes, M. D., 3rd, Swanson, T., Legasse, A. W., Sylwester, A., Hansen, S. G., Smith, A. T., Stafova, P., Shoemaker, R., Li, Y., Oswald, K., Axthelm, M. K., McDermott, A., Ferrari, G., Montefiori, D. C., Edlefsen, P. T., Piatak, M., Jr., Lifson, J. D., Sekaly, R. P. and Picker, L. J. (2012). Lymph node T cell responses predict the efficacy of live attenuated SIV vaccines. Nat Med 18(11): 1673-1681.
  5. Gardner, M. B. (1989). SIV infected rhesus macaques: an AIDS model for immunoprevention and immunotherapy. Adv Exp Med Biol 251: 279-293. 
  6. Hansen, S. G., Vieville, C., Whizin, N., Coyne-Johnson, L., Siess, D. C., Drummond, D. D., Legasse, A. W., Axthelm, M. K., Oswald, K., Trubey, C. M., Piatak, M., Jr., Lifson, J. D., Nelson, J. A., Jarvis, M. A. and Picker, L. J. (2009). Effector memory T cell responses are associated with protection of rhesus monkeys from mucosal simian immunodeficiency virus challenge. Nat Med 15(3): 293-299.
  7. Hansen, S. G., Ford, J. C., Lewis, M. S., Ventura, A. B., Hughes, C. M., Coyne-Johnson, L., Whizin, N., Oswald, K., Shoemaker, R., Swanson, T., Legasse, A. W., Chiuchiolo, M. J., Parks, C. L., Axthelm, M. K., Nelson, J. A., Jarvis, M. A., Piatak, M., Jr., Lifson, J. D. and Picker, L. J. (2011). Profound early control of highly pathogenic SIV by an effector memory T-cell vaccine. Nature 473(7348): 523-527.
  8. Hansen, S. G., Sacha, J. B., Hughes, C. M., Ford, J. C., Burwitz, B. J., Scholz, I., Gilbride, R. M., Lewis, M. S., Gilliam, A. N., Ventura, A. B., Malouli, D., Xu, G., Richards, R., Whizin, N., Reed, J. S., Hammond, K. B., Fischer, M., Turner, J. M., Legasse, A. W., Axthelm, M. K., Edlefsen, P. T., Nelson, J. A., Lifson, J. D., Fruh, K. and Picker, L. J. (2013). Cytomegalovirus vectors violate CD8+ T cell epitope recognition paradigms. Science 340(6135): 1237874.
  9. Hansen, S. G., Piatak, M., Jr., Ventura, A. B., Hughes, C. M., Gilbride, R. M., Ford, J. C., Oswald, K., Shoemaker, R., Li, Y., Lewis, M. S., Gilliam, A. N., Xu, G., Whizin, N., Burwitz, B. J., Planer, S. L., Turner, J. M., Legasse, A. W., Axthelm, M. K., Nelson, J. A., Fruh, K., Sacha, J. B., Estes, J. D., Keele, B. F., Edlefsen, P. T., Lifson, J. D. and Picker, L. J. (2013). Immune clearance of highly pathogenic SIV infection. Nature 502(7469): 100-104.
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  12. Picker, L. J., Singh, M. K., Zdraveski, Z., Treer, J. R., Waldrop, S. L., Bergstresser, P. R. and Maino, V. C. (1995). Direct demonstration of cytokine synthesis heterogeneity among human memory/effector T cells by flow cytometry. Blood 86(4): 1408-1419.
  13. Pitcher, C. J., Hagen, S. I., Walker, J. M., Lum, R., Mitchell, B. L., Maino, V. C., Axthelm, M. K. and Picker, L. J. (2002). Development and homeostasis of T cell memory in rhesus macaque. J Immunol 168(1): 29-43.
  14. Prussin, C. and Metcalfe, D. D. (1995). Detection of intracytoplasmic cytokine using flow cytometry and directly conjugated anti-cytokine antibodies. J Immunol Methods 188(1): 117-128.
  15. Sander, B., Andersson, J. and Andersson, U. (1991). Assessment of cytokines by immunofluorescence and the paraformaldehyde-saponin procedure. Immunol Rev 119: 65-93.
  16. Schuerwegh, A. J., Stevens, W. J., Bridts, C. H. and De Clerck, L. S. (2001). Evaluation of monensin and brefeldin A for flow cytometric determination of interleukin-1β, interleukin-6, and tumor necrosis factor-alpha in monocytes. Cytometry 46(3): 172-176.
  17. Sylwester, A. W., Scott, G. H. and Louis, J. P. (2014). Quantification of T cell antigen-specific memory responses in rhesus macaques, using cytokine flow cytometry (CFC, also known as ICS and ICCS): from assays et-up to data acquisition. Bio-protocol 4(8): e1110.
  18. Waldrop, S. L., Pitcher, C. J., Peterson, D. M., Maino, V. C. and Picker, L. J. (1997). Determination of antigen-specific memory/effector CD4+ T cell frequencies by flow cytometry: evidence for a novel, antigen-specific homeostatic mechanism in HIV-associated immunodeficiency. J Clin Invest 99(7): 1739-1750.


CFC提供流式细胞术的功能,包括批量采样和多参数互相关,用于抗原特异性记忆反应的分析。使用CFC的研究者能够表型表征用测试抗原和表型亚型(例如,CD4 或CD8 T细胞)培养的细胞,确定高于背景水平的产生频率的细胞因子。
与ELISPOT和Luminex方法相反,CFC可以关联来自特定的,表型特征的细胞的多种细胞因子的产生。 CFC测定可用于检测个体具有抗原暴露(如在群体筛选中),或用于跟踪抗原记忆的出现和持续(如在疫苗接种,感染或发病机理的研究中)。除了量化抗原响应细胞的%频率外,平均荧光强度可用于评估响应细胞中产生多少细胞因子。随着流式细胞术的技术进步,目前的CFC用户常常可以获得11个荧光通道(或甚至18个),使得可以高度表征抗原反应细胞的表型,或者同时定量根据许多细胞因子或活化标记的反应。强大的软件如FlowJo(TreeStar)和SPICE(NIAID)可用于分析数据,并对细胞因子反应进行复杂的多变量分析。
此处描述的方法针对恒河猴的细胞进行定制,注释笔记代表了十年的积累的技术经验。相同的方案容易应用于其它哺乳动物细胞(例如人或小鼠),尽管确切的抗体克隆将根据宿主系统而不同。这里描述的基本方法在染色和固定之前,在布雷菲德菌素A的存在下,用抗原和共刺激抗体孵育1ml管培养物中的1×10 6个淋巴细胞。
注意:这是一个两部分过程的第二部分。第一部分具有相同的初始标题,但字幕 (Sylwester et al。,2014)"。两个文档的摘要和历史背景相同。

关键字:ICS, CFC, 流式细胞仪, 记忆, 外周血单个核细胞

[历史背景] 在1988年,Andersson等人首次证明了淋巴细胞如何被固定,透化,用针对IFNg的抗体染色,然后通过流式细胞术进行荧光标记和计数。 1991年,Sander等人证实了用多聚甲醛固定细胞,用皂苷使其透化,然后使用荧光标记的抗体染色细胞内细胞因子用于显微镜检查的改进的方法。 1993年,Jung等人将该方法扩展到与流式细胞术一起使用,并且包括使用莫能菌素来破坏分泌,以便增加在合成后不久释放的分子的细胞内信号。 1995年,Prussin和Metcalfe使用直接偶联的抗体,并将孵育时间优化至6小时。同样在1995年,Picker等人通过使用布雷菲德菌素A阻断细胞因子的分泌装置,以及通过使用不同的透膜物(吐温-20),显着增强了细胞因子检测的灵敏度和再现性。在1997年,Picker等人应用这种成熟的方法来研究HIV + 患者中的抗原特异性内稳态机制。 2001年,Schuerwegh等人证实,BfA提供比莫能菌素更好的细胞因子信号,其他人在该方法中使用。
在1989年的两篇报道中,一篇由Gardner等人描述,另一篇由McClure等人报道,恒河猴是研究HIV疾病和AIDS的有用模型。 2002年,Picker等人报道了对恒河猴的CFC测定的应用。 2012年,一个为了在NHP疫苗研究中使用ICS而开发多实验室标准的小组发表了他们对于具有6小时总孵育的96孔板方法的建议(Donaldson等人,2012和Foulds等人,2012)。
这里报道的一般程序是2002管形式方法,现在与9小时总孵化,并优化特别是低端敏感性。这里的具体细节是现在由路易斯皮克实验室在俄勒冈健康和科学大学,附属于俄勒冈国家灵长类研究中心实施的最先进的技术。这些方法已经用于我们最近的几个出版物中(Fukazawa等人,2012; Hansen等人,2011; Hansen等人)。 >,2009)。重要的是要注意,在我们手中,平板格式ICS对于弱响应不如这种基于管的方法(未公开的观察)那么可靠或敏感。直到这个问题被理解和解决,基于管的方法仍然是CFC最敏感的格式。


  1. 所需数据:流式细胞术数据文件,来自CFC测定样品,如" 从分析设置到数据获取"(Sylwester等人,2014)。


  1. 用于组织培养的培养箱(加湿,在37℃,5%CO 2气氛下稳定)
  2. 流式细胞仪分析仪


  1. 配有软件的工作站,用于分析细胞仪文件
    例如 FlowJo( Tree Star
    例如 SPICE( NIAID < a>)


  1. 选择门控策略
    因为有这么多"正确"的方式来分析数据,本文档不会试图强加一个明确的处方。相反,本文提出了一个严谨的例子,说明了可取的步骤的作用和价值,一些必要的,其他可选的。此示例说明了门控时可以具有的两种心态中的第二种:(1)分析人员可以通过为每个数据文件定制精心设计的门,尽可能减少门控步骤。这种方法的优点是更少的步骤,或许更高雅。缺点是每个门将具有许多控制点,并且当应用于新文件时需要仔细和耗时的调整。当分析师有相对较少的文件要门时,这种策略是吸引人的,但当面对成百上千的文件时,这种策略很麻烦。 (2)或者,分析人员可以设法在将选通层级应用于一组文件之后最小化调整门所花费的时间。这个策略使用了许多门,每个门都是大的和宽恕的,它们单独需要很少的注意力或调整,因为文件的变化,但具有"清理","聚焦"和"winnowing"数据的总体效果,门控质量作为第一策略 下面的例子是第二种方法,利用许多"低注意"门
    1. 概述的门控层次结构
      1. "数据清理"步骤
        1. 数据减少门(可选)
        2. 时间vs CD45(建议时间,但CD45可选)
        3. 单字(建议)
        4. 删除聚合(可选)
        5. CD45与CD3(可选)
        6. 特定污渍,CD3清除(可选)
        7. 小淋巴细胞(必需)
        8. 额外清除T细胞群(可选)
      2. 分为CD4 + 和CD8 + T细胞
        1. CD4与CD8
        2. CD4 + ,CD4 - ,其次是CD4 -
      3. 门控细胞因子反应
        1. CD69与细胞因子

  2. "数据清理"步骤
    1. 数据减少门(可选)


    2. 时间vs CD45(建议时间,但CD45可选; CD3是备用选项)
      当数据记录开始和结束时,通过加压将样品进入仪器的流式细胞仪通常产生加压伪影。有时,其他压力效应(例如,,破碎样品团块)改变了数据 之间。因此,强烈建议检查时间过程数据,并删除开始/停止边界处的数据。如果时间门数据"质心"移动,则数据放置在下游门中也可能移动。因此,建议分析只考虑具有连续时间门质心的数据段。
      在此分析中不需要使用CD45,但是如果可以使用CD45,在这里是方便的。否则,在下面的示例中,任何参数(CD3,甚至SSC或FSC)都可以与CD45互换 "干净"时间数据示例:





    3. 单字(建议)
      在管中移动的变形虫细胞倾向于采取球形形状,使得直径与横截面积成比例。因此,如果绘制FSC-H(H =高度)对比FSC-A(A =面积)(和/或SSC-H对SSC-A),应当得到沿中线向上的线。当两个单元粘在一起时("双线",形成"数字八"),可能发生该线的偏离。三重,四重和高阶聚类看起来逐渐像球体,因此它们的偏转变得越来越少从中线。但是由于最大的偏差(双峰)也是最常见的小簇,所以容易除去这些污染物(否则可能暗示特定事件同时是B和T细胞或CD4 + 和CD8 +/+细胞,或者可以组合两个或三个分离的细胞的应答结果)。其他细胞学家更喜欢绘制FSC-H对FSC-W,因为Area是一个积分值,而Height是粒子的最大信号,Width是粒子信号的持续时间(即,size)独立于PMT电压。这里描述的是Picker实验室实际上做的。如果区域缩放设置正确(使用单元格),H和A应在中线对齐。如果两个(或更多)单元粘在一起,信号区域将不再与信号高度对齐。

      注意:碎片在显示数据的左下角。在第一个例子中它是门控。在第二示例中,可以通过调整门以减少碎片来改善门。更清楚地示出了亚群 在风格化的插图。

    4. 删除聚合(可选)
      虽然单线门从分析中去除了大多数低单元簇,但是单线门对于高阶簇的效果不好,使得它通过"数据减少"门。即使当这些是罕见的,当研究人员关注非常弱的反应时,它们可能导致破坏,因为即使少数这样的事件可以显着影响极少事件的门中的%。幸运的是,安装一个低维护门可以很容易地消除这些事件的威胁,一个门利用了细胞簇将表现为非自然亮的事件("细胞")的事实。 > 小心这门,但是,因为你有时可以得到非常鲁棒的响应,如:

      在上述实施例中,第一组是无抗原(无应答)的样品,仍然具有相当大的预先门控的TNFα信号和一些IFNg信号。上排的第二幅图显示了响应超抗原SEB的细胞,其中显着的群体对IFNg和TNFa都有反应。第二行的第一组显示对SIV Gag肽混合物弱响应的群体。最后一个图显示了通常由毒性肽诱导的对角假象 SIV Env肽混合物。我们的实验室将这称为"死亡尖峰",并且通过门控持续的这些事件中的任何一个通过跑到中线污染下游地块。离开这个工件可以大大地 - 不正确地 - 膨胀报告的响应。

    5. CD45与CD3(可选)
      如果染色板结合了CD45,在这里应用它是一个很好的用法。 CD45是所有白细胞(WBC)上的标记物,因此当CFC研究淋巴细胞是少数群体的组织中的应答时,该特定的门是非常有价值的[例如肺清洗(<5% ),小肠,肝,阴道粘膜等)。没有CD45,仍然可以用CD3对CD4(或CD8)门减少非白细胞污染,但是它不产生与清洁一样令人满意的。此外,该门允许存在的任何"死亡尖峰"的一些减少,或当细胞经历冻融循环时释放的亚细胞碎片。

      顶行分别显示阴性和弱响应样品。底行显示了显示由SIV Env毒性肽诱导的"死亡尖峰"的样品。注意这个门如何可以减少来自下游分析的死亡尖峰事件 在没有死亡尖峰的情况下,这种门通常可靠地维护较少,但是在清除否则难以去除的碎片时非常有效。

    6. 特殊染色和CD3清除1(可选)
      但是当抗原名包括像SIV Env肽混合物(其包括毒性肽)时,该相同的门可以用于另一目的。移除所有受这些有毒肽影响的细胞可能是非常困难的,因为活死亡染色不总是将它们排除在外,并且因为"死亡尖峰"的特征事件跑到中线,常常掩盖感兴趣的群体我们。通过使用结合未使用的通道与CD3的图,使得我们感兴趣的CD3 + 群体被推离中线,并且任何死亡尖峰"扫视"它,以可部分门控。

    7. 小淋巴细胞(必需)
      在CFC中,重要的是首先将分析集中在淋巴细胞上的散点图上,然后通过CD3荧光分别集中在作为T细胞的小淋巴细胞亚群上。因此,这门是必不可少的。在门控方案中 这里显示的第一个门("数据减少")粗略地近似于小淋巴细胞群。但是该门通常与碎片场混合,并且在一些组织(例如肺洗液,肠,肝)中,碎片可能是如此压倒性的,使得分析者被迫猜测淋巴细胞在何处散点图。此外,重要的是强调纯B淋巴细胞和NK淋巴细胞具有与T淋巴细胞不同但是重叠的散射轮廓。因此,在群体经历CD3 + 门后应用"小淋巴细胞"门是有益的。

    8. 对T细胞(CD3 + 小淋巴细胞)群体的额外清除(可选)
      离散的人口通常在他们之间有事件的"模糊",使得决定在哪里放门。一些地块使决定更清晰,这个选通步骤就是这样。在这个阶段,我们有相当干净的T细胞群体,除了明亮的CD3 + 通常被CD3-介质事件的拖尾,下降到CD3群体的问题我们已经删除)。问题是CD3-培养基群体;我们应该(可以)将任何部分打开吗?

      上面的第一组显示了缺少任何测试抗原的样品;第二个图显示用超抗原SEB刺激的样品。 请注意,在这两种情况下,都清楚地在哪里打开CD3-中/暗单元格。

      这个门也有助于从分析中去除更多的"死亡尖峰"细胞,如下面的SIV Env肽混合实例所示:

  3. 分为CD4 + 和CD8 + T细胞群体(必需)
    现在有一个"干净的"T细胞,有两种方法将它们分成单独的CD4 + 和CD8 + T细胞箱:(1)CD4对比CD8。这具有简单的优点,但是缺点是有时在何处放置CD4 + CD8 + 双阳性群体(如果,确实,将包括在下面的实施例中,CD4 + CD8 + 群体包括在CD4 + T细胞中。选择选择CD4 + CD8 + 双阳性T细胞作为单独的,独特的群体。)。 (2)产生更一致的两步法 结果,但涉及更多的输入
    1. CD4与CD8

    2. 减少SIV Env毒性肽诱导的"死亡尖峰":

    3. (CD4 + ,CD4 - ),然后是(CD8 + 两步法:(1)CD4 + ,CD4 - ,然后(2)注册CD4 br />

      减少SIV Env毒性肽"死亡尖峰":目标是消除碎片尖峰,并且上面的门形只是一个建议。

  4. 门控细胞因子反应
    1. CD69与细胞因子
      到目前为止所有的一切都是为了获得最大限度地清洁的CD4 +和CD8 + T细胞。最后一步是筛选抗原刺激的细胞因子阳性细胞。 Picker实验室绘制激活标记CD69与各种细胞因子(TNFa,IFNg,MIP1b,IL2,等),因为我们已经看到有时从未激活的细胞因子信号发出( ,CD69 +)细胞。如果你的测试产生强烈的积极反应,这可能对你不重要。然而,如果你像我们一样对非常弱的反应感兴趣,则包括实际上不是抗原刺激的任何事件是测定灵敏度的降低。
      "负"样本的示例,显示从CD69 - 群体发出的IL2:

      如何门控SEB响应的示例 注意:右上角的数字报告了绘图中所有事件的百分比; 即响应细胞的%频率。

      SIV Gag肽混合物反应的实施例:


本协议中描述的方法自本协议中引用的1995年论文(Picker等人,1995年)以来一直在发展,并且在我们的小组(Hansen等人)发表的两篇2013年论文中使用, et al。,2013a和Hansen等人,2013b)。 NIH和比尔和梅林达·盖茨基金会提供了资金。


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引用:Sylwester, A. W., Hansen, S. G. and Picker, L. J. (2014). Quantification of T Cell Antigen-specific Memory Responses in Rhesus Macaques, Using Cytokine Flow Cytometry (CFC, also Known as ICS and ICCS): Analysis of Flow Data. Bio-protocol 4(8): e1109. DOI: 10.21769/BioProtoc.1109.

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