Standardization of cytokine flow cytometry assays
- Holden T Maecker1Email author,
- Aline Rinfret2,
- Patricia D'Souza3,
- Janice Darden3,
- Eva Roig2,
- Claire Landry2,
- Peter Hayes4,
- Josephine Birungi5,
- Omu Anzala6,
- Miguel Garcia7,
- Alexandre Harari7,
- Ian Frank8,
- Ruth Baydo8,
- Megan Baker9,
- Jennifer Holbrook9,
- Janet Ottinger9,
- Laurie Lamoreaux10,
- C Lorrie Epling11,
- Elizabeth Sinclair11,
- Maria A Suni1,
- Kara Punt12,
- Sandra Calarota13,
- Sophia El-Bahi14,
- Gailet Alter15,
- Hazel Maila16,
- Ellen Kuta17,
- Josephine Cox17,
- Clive Gray16,
- Marcus Altfeld15,
- Nolwenn Nougarede14,
- Jean Boyer13,
- Lynda Tussey12,
- Timothy Tobery12,
- Barry Bredt11,
- Mario Roederer10,
- Richard Koup10,
- Vernon C Maino1,
- Kent Weinhold9,
- Giuseppe Pantaleo7,
- Jill Gilmour4,
- Helen Horton8 and
- Rafick P Sekaly2
© Maecker et al; licensee BioMed Central Ltd. 2005
Received: 04 December 2004
Accepted: 24 June 2005
Published: 24 June 2005
Cytokine flow cytometry (CFC) or intracellular cytokine staining (ICS) can quantitate antigen-specific T cell responses in settings such as experimental vaccination. Standardization of ICS among laboratories performing vaccine studies would provide a common platform by which to compare the immunogenicity of different vaccine candidates across multiple international organizations conducting clinical trials. As such, a study was carried out among several laboratories involved in HIV clinical trials, to define the inter-lab precision of ICS using various sample types, and using a common protocol for each experiment (see additional files online).
Three sample types (activated, fixed, and frozen whole blood; fresh whole blood; and cryopreserved PBMC) were shipped to various sites, where ICS assays using cytomegalovirus (CMV) pp65 peptide mix or control antigens were performed in parallel in 96-well plates. For one experiment, antigens and antibody cocktails were lyophilised into 96-well plates to simplify and standardize the assay setup. Results (CD4+cytokine+ cells and CD8+cytokine+ cells) were determined by each site. Raw data were also sent to a central site for batch analysis with a dynamic gating template.
Mean inter-laboratory coefficient of variation (C.V.) ranged from 17–44% depending upon the sample type and analysis method. Cryopreserved peripheral blood mononuclear cells (PBMC) yielded lower inter-lab C.V.'s than whole blood. Centralized analysis (using a dynamic gating template) reduced the inter-lab C.V. by 5–20%, depending upon the experiment. The inter-lab C.V. was lowest (18–24%) for samples with a mean of >0.5% IFNγ + T cells, and highest (57–82%) for samples with a mean of <0.1% IFNγ + cells.
ICS assays can be performed by multiple laboratories using a common protocol with good inter-laboratory precision, which improves as the frequency of responding cells increases. Cryopreserved PBMC may yield slightly more consistent results than shipped whole blood. Analysis, particularly gating, is a significant source of variability, and can be reduced by centralized analysis and/or use of a standardized dynamic gating template. Use of pre-aliquoted lyophilized reagents for stimulation and staining can provide further standardization to these assays.
Enzyme-linked immunospot (ELISPOT) and cytokine flow cytometry (CFC) (or more specifically, intracellular cytokine staining (ICS)) are popular methods for single-cell analysis of antigen-specific T cell cytokine production. T cell production of IFNγ, and increasingly also IL-2, is taken as a measure of vaccine immunogenicity in experimental vaccine trials. Of the two types of assays, ICS has the advantage of a highly multiparametric read-out (flow cytometry) that allows for precise phenotyping of the responding T cell populations. It has also recently been adapted to a 96-well plate configuration [1, 2], allowing for higher throughput analysis similar to that used for ELISPOT. However, while the precision of ELISPOT assays across sites has been recently documented , similar studies for ICS assays have been lacking.
Numerous phase I and phase II clinical trials have been initiated using candidate prophylactic HIV vaccines (reviewed in ). Many of these trials use ICS as part of their immune monitoring. While most current HIV trials are not powered to determine efficacy, and cytokine production has not been validated as a surrogate marker of protection from HIV infection or progression, there is nevertheless a desire to measure immunogenicity of candidate vaccines as well as safety in early clinical trials . Because many different groups are performing immune monitoring for these clinical trials, there is currently a lack of standardization that would allow accurate comparisons of immunogenicity across candidate vaccines in different clinical trials.
There is some published literature on the intra-and inter-assay precision of ICS assays in whole blood . These values were determined to be about 8% and 20% C.V., respectively. Guidelines for performance of ICS assays have also been recently published . However, there are no existing data documenting the precision of ICS between laboratories, or comparing the precision of ICS using different sample types (e.g., whole blood versus cryopreserved PBMC). In order to allow more meaningful comparisons between laboratories and prioritization of emerging vaccine candidates, and thereby accelerate HIV vaccine development, this ICS standardization study was undertaken.
Activated, fixed, and frozen whole blood
Fresh whole blood
Cryopreserved PBMC with preconfigured lyophilised reagent plates
Study participants and institutions.
Exp. 1 (fixed activated blood)
Exp. 2 (shipped whole blood)
Exp. 3 (cryo-preserved PBMC)
Exp. 4 (cryo-preserved PBMC with lyoplates)
University of Montreal (CANVAC)
Rafick Sekaly, Eva Roig, Claire Landry
Chelsea and Westminster Hospital (IAVI)
Jill Gilmour, Peter Hayes
Uganda Virus Research Institute (IAVI)
Josephine Birungi, Omu Anzala
Centre Hospitalier Universitaire Vaudois (EUROVAC)
Giuseppe Pantaleo, Alexandre Harari, Miguel Garcia
Fred Hutchison Cancer Research Center (HVTN)
Helen Horton ,Ruth Baydo, Ian Frank
Duke University (HVTN)
Kent Weinhold, Janet Ottinger, Megan Baker, Jennifer Holbrook
Vaccine Research Center, NIH
Mario Roederer, Richard Koup, Laurie Lamoreaux
Merck and Co.
Timothy Tobery, Lynda Tussey, Kara Punt
University of California, San Francisco
Barry Bredt, Elizabeth Sinclair, Lorrie Epling
Vernon Maino, Holden Maecker, Maria Suni
Nolwenn Nougarede, Sophia El-Bahi
National Inst. Communicable Diseases, South Africa
Clive Gray, Hazel Maila
Massachusetts General Hospital
Marcus Altfeld, Gailet Alter
University of Pennsylvania
Jean Boyer, Sandra Calarota
Henry Jackson Foundation
Josephine Cox, Ellen Kuta
When data for this experiment were centrally analysed (Figure 7B), the average C.V.'s were considerably reduced, much like in the first experiment (Figure 3). This could reflect the fact that new laboratory sites had been added that had not yet standardized their gating strategies with the existing sites; thus more benefit was realized by centralized analysis. The difference in average C.V. between cocktail 1 and cocktails 2 and 3 was preserved even after centralized analysis. The mean C.V. for cocktails 2 and 3 was now 18%, the lowest variability seen in any of the experiments. For comparison, the mean inter-lab C.V. of the percent CD4+ or CD8+ cells in the unstimulated samples from this experiment was 3% and 7%, respectively (data not shown).
Lyophilised control cells
As a positive control in Experiment 4, a set of PBMC were SEB-activated, processed, stained, and then lyophilised in certain wells of the lyophilised antibody plates. They were hydrated and transferred to the plate containing activated cells, along with the staining antibodies. These cells served as a control for instrument setup and gating, since all the activation and processing steps were done centrally. The results reported by the individual sites for these cells are shown in the left panel of Figure 7C. Surprisingly, the average C.V. (20.5%) was only slightly lower than that for the rest of Experiment 4, in which cells were activated and processed independently by each site. However, when the control cell data were centrally analysed using a dynamic gating template (right panel), the C.V.'s were reduced to 3–7%. This reinforces the notion that the vast majority of inter-lab variability is due to gating.
Spontaneous cytokine production in the three sample types
While CD4 backgrounds were very similar between experiments, CD8 backgrounds varied. The median CD8 background in the PBMC experiments was significantly lower than that of the frozen activated whole blood experiment (p < 0.0001) or the fresh whole blood experiment (p < 0.05). The differences were significant after centralized analysis as well. However, this could be due to the fact that different donors were used in the four experiments, rather than being due to any inherent difference between assay types. In experiment 4, the CD4 backgrounds for cocktail 1 were significantly higher than those for cocktail 2 (p < 0.05, data not shown), while there was no significant difference for CD8 backgrounds. This could be due to the inclusion of IL-2 in cocktail 1, which would be expected to be produced by more CD4+ than CD8+ cells, and thus contribute selectively to the CD4 background.
This study examined the reproducibility of ICS assays across sites using different assay formats. It was not designed to compare ICS with other immune monitoring assays, comparisons of which have been published [15–21]. The current study used 96-well plate-based protocols exclusively, as these were considered more convenient, and have recently been validated against tube-based protocols for both PBMC and whole blood .
There are some potential drawbacks to the use of 96-well plates. One of these is the possibility of well-to-well contamination during the assay. This was observed in an initial subset of Experiment 4 (data not shown), in which some sites received lyophilised plates with SEB as a positive control. Some of these sites experienced high backgrounds in the negative control wells adjacent to the SEB-containing wells. It was later determined that cross-contamination probably occurred during the initial distribution of the antigens on the plates, and this was compounded by the fact that the donors used were unusually sensitive to SEB stimulation (responses >30% of CD4+ and CD8+ T cells). When SEB was replaced with CEF as a positive control, no such problems were noted. This experience suggests that the choice and placement of positive control wells on a plate deserves consideration.
The current study was designed to determine inter-lab variability in ICS assays. As such, there were no data "filters" applied to exclude potentially erroneous data or outliers. However, improved precision of ICS results might be obtained if certain acceptance criteria were applied before data were taken as valid. For example, a minimum number of collected events could be specified (sites in this study were asked to collect 10,000–40,000 CD4+ or CD8+ T cells per sample, or 60,000 CD3+ cells). This number of events was designed to yield precision levels that would minimize event number as a factor in inter-lab reproducibility. There could also be acceptance criteria based upon the absolute level of background, or the degree of reproducibility between duplicate samples, if run (the current study did not use duplicate samples).
It is also possible to apply statistics to derive further meaning from numerical results. For example, statistical tests could be used to determine whether a given response can be discriminated from a given background, for a particular number of events collected [23, 24]. This can be given by a power calculation as follows:
N = [2*Pav(1-Pav)(Zα +Zβ)2]/Δ2
where N is the number of events in each sample needed for significance, Pav is the average proportion (between the background and test samples), and Δ is the difference between these two proportions. The term (Zα +Zβ)2 is referred to as a power index, and varies depending upon the desired power and p value. For example, (Zα +Zβ)2 = 23.9 for 99% power and p < 0.005 .
In addition, a confidence interval could be derived around the difference of the test result and the negative control , in order to allow discrimination of significant differences between various samples. Other statistical methods have also been employed in order to determine cut-off values for positive responses in ICS [25, 26]. No attempt was made in the current study to define which results were positive, as all data were reported objectively, and all donors were known to be CMV seropositive.
Percent C.V. by mean percent cytokine-positive T cells.
Mean % cytokine-positive cells
0.1 – 0.5%
Number of samples in range
Average percent C.V.
Individual (Manual) Analysis
Central (Automated) Analysis
Percent C.V. by assay format.
Fixed Activated Blood
Shipped Whole Blood
Cryopreserved PBMC with lyophilised reagents
Number of samples
Average percent C.V.
Individual (Manual) Analysis
Central (Automated) Analysis
Mitigation of gating variability was achieved in these experiments by centralized analysis with a dynamic gating template (see Figure 2B). The dynamic gating template allowed for more automated, batch analysis of the data. Once such a template was created and optimized (see Materials and Methods section for description), it could also have been provided to individual sites in order to yield the same results. It is further possible that similar results could be achieved by manual analysis, provided it was done by a single operator. Standardization of gating techniques, in the absence of centralized analysis or dynamic gating templates, could also improve precision. The improvement in C.V. made by centralized analysis was most marked in the first experiment, and progressively less in experiments 2 and 3, perhaps because of standardization of gating among sites over time. Experiment 4 included many new sites, and the improvement in C.V. from centralized analysis was again more marked.
In addition to differences in reproducibility, the various assay formats have other benefits and drawbacks as well. Cryopreserved PBMC are much more amenable to peptide (and superantigen) stimulation than to whole protein stimulation ; while whole blood assays are equally amenable to stimulation with either type of antigen. Also, consistently good cryopreservation of PBMC at multiple clinical sites is difficult to achieve, but highly important for achieving reproducible results with PBMC [27, 28] (DeLaRosa et al., manuscript in preparation). This could become less of a factor if a stabilizing matrix for preserving whole blood or PBMC function during shipping were discovered. All in all, the choice of assay format for a clinical trial will depend not only upon considerations of assay precision, but also upon the type of antigen(s) used and the capabilities of the participating clinical sites.
The use of lyophilised reagent plates appeared to reduce inter-lab variability. This conclusion cannot be drawn with certainty, because different participating laboratories and different donors were used between experiments 3 and 4. However, it is intriguing to note that, when centrally analysed data was compared (to remove gating as a source of variability), the mean C.V.'s of experiment 4 were the lowest of all four experiments (18%, Table 3). This is despite the fact that the donors and stimuli used in experiment 4 resulted in lower mean response levels, which should tend to increase the C.V. This is also borne out by the analysis of Figure 11B, where the results for experiment 4 appeared to be generally closer to the theoretical minimum SD than did the results for the other experiments.
With the possibility of achieving inter-laboratory C.V.'s of less than 20%, even with relatively low responses, ICS compares favourably to ELISPOT, for which interassay C.V.'s of 17–18% for PHA and 55–65% for Candida have been reported [29, 30]. ICS is also comparable to cytokine ELISA, the latter having reported interassay C.V.'s of <25% [31, 32]. Phenotypic staining, such as used for CD4 counting, can achieve higher precision levels than functional assays, and averaged around 10% C.V. in one multisite study . For comparison, the inter-lab C.V. of the CD4+ or CD8+ cell percentages was around 5% in experiment 4 of the present study (data not shown). CD4 counting precision has also been shown to be dependent upon the number of events collected, gating, and use of automated analysis [33, 34]. Since functional assays are subject to more variables than phenotypic staining, the ability to achieve precision levels such as those reported here should be considered favourable. ICS could thus be a viable tool for comparing immune responses even across clinical trials, provided the methodology was standardized.
ICS assays could be performed with inter-laboratory C.V.'s of approximately 20% at response levels of >0.5%, and C.V.'s of approximately 25–30% at response levels of 0.1–0.5%. The C.V. increased further at response levels of =0.1%. A significant portion of inter-laboratory variability could be eliminated by use of centralized analysis and/or a dynamic gating template.
Whole blood and cryopreserved PBMC showed grossly similar levels of reproducibility. However, when analysis variability was removed, cryopreserved PBMC processed with lyophilized reagents showed significantly better reproducibility than shipped whole blood. Shipped whole blood assays were also subject to data loss when samples were not delivered in a timely fashion.
Background cytokine production was mostly =0.05% for both CD4 and CD8 cells. While CD8 backgrounds were lower in cryopreserved PBMC than in whole blood, this could have been due to the use of different donors in the four experiments. With the high viabilities and recoveries obtained for cryopreserved PBMC in this study, there was no obvious relationship between viability/recovery and response.
The use of microtiter plates containing lyophilised reagents simplified the ICS protocol, and appeared to improve assay reproducibility. This format lends itself to international shipping of reagents (because there is no need for refrigeration), and also to larger clinical trials (because of the stability of the lyophilised reagents). It is also a way to reduce the chance of pipetting errors, because the plates are pre-formatted.
The results of this study indicate that ICS assays can be reasonably standardized between sites, but that considerations of sample format and expected response levels can influence the precision of the results. These data should guide comparisons of ICS results between different groups or in different clinical trials.
Whole blood preparation
Heparinized whole blood was collected from healthy CMV seropositive volunteers for experiments 1 and 2. For experiment 1, the blood was activated in 15 mL conical tubes according to the method of Nomura et al.. Activated blood was treated with 2 mM final concentration of EDTA for 15 minutes at room temperature, then 10 volumes of FACS Lysing Solution (BD Biosciences, San Jose, CA) were added. After 10 minutes at room temperature, the tubes were frozen at -80°C, then shipped to participating laboratories on dry ice. The protocol used by each laboratory for handling these samples is provided in Additional File 1.
For experiment 2, 5 mL of heparinized whole blood was overnight shipped in an insulated container at ambient temperature to each participating lab. The protocol used by each lab for handling these samples is provided in Additional File 2.
PBMC preparation and cryopreservation
For experiments 3 and 4, PBMC from leukapheresis of CMV seropositive donors were isolated using Ficoll gradient separation. They were then cryopreserved according to a standard protocol (Disis et al., submitted for publication). These cryopreserved PBMC were shipped to participating labs using liquid nitrogen dry shippers. The protocol used by each lab for thawing and processing of these cells is provided as Additional Files 3 and 4.
Instrumentation and setup
The flow cytometry instrumentation used in this study included 12 BD FACS Caliburs (BD Biosciences), 3 BD LSRIIs (BD Biosciences), and 1 CyAn (Dako Cytomation, Fort Collins, CO). Instrument setup was at the discretion of the individual laboratory, and was either manual (using isotype control stained cells to set PMT voltages, and single-stained cells to set compensation) or automated (using BD FACSComp software and BD Calibrite beads (BD Biosciences)). In some labs, automated setup was followed by manual adjustment using stained cells as above.
Dynamic gating templates
Original FCS files from each site were sent to BD Biosciences for analysis using a dynamic gating template (Figure 2B). This template was built using "Snap-To Gating" and "Tethering" tools available in CellQuest Pro software (BD Biosciences). The shape of the snap-to gates is determined by a clustering algorithm, and this algorithm allows for their movement from sample to sample in a data-dependent manner. The size and amount of allowable movement of each snap-to gate was adjusted by inspection of a subset of the files to be used, with iterative changes being made until the template performed as desired. The template was then used, without further adjustment, on all the files of a given experiment. Since the template was generated in CellQuest Pro software, only files generated on FACS Calibur instruments were analyzable by this method.
The %CV was calculated as 100*SD/mean for each sample, from the percentage of cytokine-positive cells reported by each laboratory or derived from centralized analysis of that sample. The mean CV for each experiment was taken as the average of all the individual sample CVs. Statistical significance of differences in the average CV between experiments was calculated using a Kruskal-Wallis test, with Dunn's Multiple Comparison test to determine where significant differences were found. The significance of the difference between individually and centrally analyzed data was calculated by comparing the aggregate CVs of all samples from all experiments using a Wilcoxin signed rank test for matched pairs. A two-tailed Student t test was used to calculate significance of differences in background within or between experiments.
The authors acknowledge the National Institute for Allergy and Infectious Diseases and CANVAC for financial and logistical support, and BD Biosciences for providing reagents. They also thank Doug Haney (BD Biosciences) for advice on statistical analysis.
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