The CIMT-monitoring panel: a two-step approach to harmonize the enumeration of antigen-specific CD8+ T lymphocytes by structural and functional assays

The interpretation of the results obtained from immunomonitoring of clinical trials is a difficult task due to the variety of methods and protocols available to detect vaccine-specific T-cell responses. This heterogeneity as well as the lack of standards has led to significant scepticism towards published results. In February 2005, a working group was therefore founded under the aegis of the Association for Immunotherapy of Cancer (“CIMT”) in order to compare techniques and protocols applied for the enumeration of antigen-specific T-cell responses. Here we present the results from two consecutive phases of an international inter-laboratory testing project referred to as the “CIMT monitoring panel”. A total of 13 centers from six European countries participated in the study in which pre-tested PBMC samples, synthetic peptides and PE-conjugated HLA-tetramers were prepared centrally and distributed to participants. All were asked to determine the number of antigen-specific T-cells in each sample using tetramer staining and one functional assay. The results of the first testing round revealed that the total number of cells analyzed was the most important determinant for the sensitive detection of antigen-specific CD8+ T-cells by tetramer staining. Analysis by ELISPOT was influenced by a combination of cell number and a resting phase after thawing of peripheral blood mononuclear cells. Therefore, the experiments were repeated in a second phase but now the participants were asked to change their protocols according to the new guidelines distilled from the results of the first phase. The recommendations improved the number of antigen-specific T-cell responses that were detected and decreased the variability between the laboratories. We conclude that a two-step approach in inter-laboratory testing allows the identification of distinct variables that influence the sensitivity of different T-cell assays and to formally show that a defined correction to the protocols successfully increases the sensitivity and reduces the inter-center variability. Such “two-step” inter-laboratory projects could define rational bases for accepted international guidelines and thereby lead to the harmonization of the techniques used for immune monitoring.
Introduction
In the last two decades, more than 200 clinical trials of different anti-tumor vaccines aiming to induce tumor-specific immunity in cancer patients have been described [1]. Most of these trials primarily assessed safety and immunogenicity while reporting partial or complete clinical responses in a minority of patients [2, 3]. Despite the fact that the low fraction of clinical responders still precludes the establishment of a direct correlation between clinical efficacy and T-cell reactivity, it has become clear from animal models and clinical observations that naturally-occurring or vaccine-induced CD8+ or CD4+ T-cells play an important role in the control and regression of tumors [4–9]. Therefore, the number of subjects that mount a vaccine-induced T-cell response as well as the strength of a detected T-cell response represent important surrogate markers for vaccine efficacy. The enzyme-linked immunospot (ELISPOT) assay [10, 11], staining with HLA-peptide multimers [12] and intracellular cytokine staining (ICS) [13, 14] are technologies used commonly for the monitoring of antigen-specific immune responses. For these three assays, a huge variety of different protocols are available worldwide. This heterogeneity, together with the fact that the sensitivity of the individual protocols can vary significantly, makes a comparison of the results obtained in different trials a difficult task. Moreover, an increasing number of new technologies are constantly being introduced to the field, which makes interpretations even more complex [15–25].
Current data and opinion support the use of a functional assay like the ELISPOT or ICS in combination with a phenotyping assay like HLA-multimers [26, 27], but recognized international standards for all these methodologies are still lacking.
The main aim of the “CIMT monitoring panel” is to harmonize and optimize the monitoring of antigen-specific T-cells among the participating laboratories, based on objective rationales with respect to the testing procedure, the analysis and the interpretation of results. Important requirements for an immunological test are sensitivity, applicability to large amounts of clinical material and feasibility at reasonable cost. The results generated by the tests should be reproducible and sensitive, independently of the place where they have been performed. After the first meeting of the working group, a series of inter-laboratory testing projects was initiated, in which individual laboratories could compare their performance, express their needs and exchange experience in order to improve their local assays. Here we report the results of the first two phases of the CIMT monitoring panel, with 13 participating centers from six European countries.
Materials and methods
Preparation and screening of PBMC samples
Buffy coats from HLA-typed healthy volunteers were kindly provided from the Blood Bank of the University Mainz. HCMV sero-status was known. PBMC were isolated by Ficoll density gradient separation (Pharmacia, Uppsala, Sweden), washed two times in RPMI 1640 (GIBCO BRL, Grand Island, NY, USA) containing 10 mM Hepes buffer, l-arginine (116 mg/ml), l-glutamine (216 mg/ml), penicillin (10 IU/ml), streptomycin (100 mg/ml) and 10% FCS (GIBCO BRL), counted and frozen at 10 to 20 × 106 cells per cryovial in 1 ml of FCS 90% + DMSO 10% at −80°C in freezing-boxes filled with iso-propanol. After 20 h, all cryovials were transferred to liquid nitrogen and stored until distribution to the participating laboratories.
Pre-screening and selection of the PBMC donors for influenza- and CMV- T-cell reactivities were performed by a central lab using the IFNγ ELISPOT assay following a local protocol as described previously [38]. Five donors were selected for the first phase of the panel and eight for the second phase. One HLA-A*0201-negative donor was included in each phase (negative control), all other samples were HLA-A*0201-positive.
Synthetic peptides and HLA-tetramers
Peptides were synthesized using standard Fmoc chemistry, dissolved at 10 mg/ml in DMSO, aliquotted and stored at −80°C. The purity was checked by reverse-phase HPLC and was found to be >80%. Two known HLA-A*0201 T-cell epitopes were used: influenza MP 58–66 GILGFVFTL and HCMV pp65 495–503 NLVPMVATV (http://www.syfpeithi.de). Biotinylated recombinant HLA-A*0201 monomers folded with the influenza MP 58–66 or the HMCV pp65 peptides were produced essentially as described, purified by gel filtration and stored as aliquots at −80°C [12]. Fluorescent multimers were obtained by incubation with streptavidin-PE (Molecular Probes, Leiden, The Netherlands), then frozen as aliquots after addition of 0.5% BSA and 16% glycerol. HLA-concentrations of influenza-tetramer and HCMV-tetramers were 700 and 350 μg/ml, respectively. Both tetramers were checked by HPLC and/or validated by staining of a specific CD8+ T-cell line (Influenza) or PBMC from HLA-A2-negative and HLA-A2-positive CMV seronegative donors (CMV). Such tetramers are stable at 4°C for at least 1 month (personal observation) and participants were asked to perform all tests within this time period.
Participating centers
Twelve centers from five European countries participated in the first phase of the monitoring panel. As one of the investigators moved to another institution during the study a 13th center from a 6th European country was added to the group in the second phase of the panel. Participation in the panel was open to all interested laboratories with a focus on T-cell monitoring, independently of membership in the Association for Immunotherapy of Cancer.
Reagent distribution and assay guidelines
Coded PBMC samples, synthetic peptides and HLA-A*0201 tetramers were shipped on dry ice to the participants. Additionally, guidelines for the two T-cell assays were distributed for each phase:
Phase I/2005. A protocol for tetramer staining was included. Briefly, 1 × 106 PBMC per test were transferred directly after thawing into one well of a 96 well u-bottom plate and washed in FACS buffer consisting of PBS, 2% FCS, 2 mM EDTA, 0.02% azide. Incubation with 5 μg/ml HLA-tetramer was then performed in FACS buffer with 50% FCS for 30 min at room temperature in the dark. After one wash in FACS buffer, mAb for T-cell staining were added for 20 min at 4°C. Finally, cells were washed twice before fixing in FACS buffer containing 1% formaldehyde solution. Three mAb combinations were proposed, CD8 alone, CD3 plus CD8, or CD4 plus CD8. Each lab could choose here the antibody clones, fluorescent dye and concentrations used. Stainings were performed in duplicate.
For the functional assays, synthetic peptides were diluted at 1 mg/ml in PBS as a stock solution. Concentrations in further tests were 1–10 μg/ml, left to the choice of the participants. There were no recommendation which functional test should be performed, so that each group could choose the test either routinely used, or to be implemented for its own needs. In this first phase, 11/12 laboratories chose the IFNγ ELISPOT assay, one lab (Z10) a FACS-based intracellular IFNγ staining and one lab performed both assays (Z7). Spot counting was performed locally.
Phase II/2006. Following the results obtained in the first testing phase, requirements were introduced and participants were asked to apply exactly these new criteria (two for the tetramer staining, and four for the ELISPOT, see “Results” section). The assay guidelines were modified accordingly. However, in order to reduce the variability in the FACS analysis of the 13 laboratories, a figure showing exemplary dot-plots, settings of gates and quadrants, and statistical analysis was provided. All laboratories were now required to perform an IFNγ ELISPOT as the functional test, with a fixed peptide concentration of 1 μg/ml. Participants were encouraged to use a distributed model protocol but were allowed to use their local protocol, provided that they applied the four new requirements introduced in the second phase.
Collection and analysis of results
After performing the required tests in each phase, participants returned a completed report form containing all relevant information. Number of cells recovered after thawing was included to assess viability after transport. For the tetramer staining experiments, mAb clone, manufacturer, amount, cytometer type and number of lymphocytes and/or CD8+ cells analyzed were noted. Results were expressed as percentage of tetramer-positive cells among CD8+, CD3+CD8+, or CD4− lymphocytes, depending on which mAb combination was used for the staining. Additionally, FACS dot-plots containing all gates, quadrants and deduced statistical analysis were collected. For the functional test, medium and thawing procedure (e.g. addition of DNAse, of a resting phase, etc.) had to be described, as well as the number of cells per test, the antibodies used (clone, manufacturer, concentration), the final peptide concentration and the incubation times. For the ELISPOT assay, the type of plate, the enzymatic visualization system and the spot reader were also noted. Absolute spot numbers were given by each participant, and filter plates were kept for possible second analysis.
All results from both phases were collected and centrally analyzed. For the tetramer stainings, the number of lymphocytes, number of CD8+ T-cells and frequencies of tetramer-positive cells were calculated on the basis of the stainings and statistics provided by the participants. Apart from these calculated frequencies, a “visual evaluation” was necessary (see “Results”). For the ELISPOT, analysis was performed based on the spot numbers reported by the participants, followed by a student t test. Results were accepted as positive reaction only when the numbers of antigen-specific spots exceeded the number of spots in the background wells by atleast a factor two. The coefficients of variation (CV) were calculated for all results (CV = SD/mean × 100) and are shown in supplementary Tables S1a, b.
The raw data from both panel phases will be provided to interested readers upon request.
Results
Phase I/2005 of the interlaboratory testing project—general aspects
Coded PBMC samples from four HLA-A*0201-positive and one HLA-A*0201-negative healthy donor (D1–D5) were included in this first testing phase. The thawing procedure for PBMC samples in the test centers was not standardized and the recovery of viable cells varied greatly between 45 and 102% (mean 73%) in the 12 labs. However, the number of cells recovered was in all cases sufficient to perform the required analyses. When all the data from the tetramer staining and functional tests were combined it became clear that subjects D1 and D5 had responded to the HLA-A*0201 restricted CMV-derived peptide, consistent with their CMV seropositive-status, and that subjects D1, D2, D3, and D5 had responded to influenza. In total, each laboratory should in theory have been able to measure six positive (2× CMV and 4× influenza) responses.
Detection of antigen-specific T-cells by tetramer staining and IFNγ ELISPOT
The protocol required that all PBMC samples should be analyzed by the 12 participants for the presence of HLA-A*0201-restricted CMV-specific and influenza-specific CD8+ T-cells using centrally-prepared tetramers. The indicated frequencies of antigen-specific CD8+ cells generally represent the mean of two separate stainings with CD3 Ab/CD8 Ab/tetramer, except for centers Z1 (CD8/tetramer), Z7 (CD3/CD4/CD8/tetramer), Z5 and Z10 (one staining CD3/CD8/tetramer and one staining CD3/CD4/tetramer) and are based on the analysis and dot plots provided by each participant. As illustrated in Fig. 1, the absolute numbers of tetramer-positive T-cells were influenced by the individual decision of where to set the gates and quadrant markers for the analysis. For example, the inclusion of the subset of T lymphocytes expressing CD8 at a low density influenced the number of CD8+ and consequently the frequency of tetramer+ cells. Moreover, non-specific binding of the tetramer (as seen on the CD8-negative subset) also varied between the different laboratories. For these reasons, not only the frequencies, but also the appearance of the tetramer-positive populations was carefully examined. Two parameters were chosen for validation of “positive” results: (1) a clustered, but not diffuse, tetramer binding-population, and (2) strong intensity of tetramer staining, especially marked for the CMV-tetramer-binding population (Fig. 1). Table 1 shows: (I) the minimum, mean and maximum frequencies of antigen-specific CD8+ T-cells, (II) the results obtained from the individual centers Z1–Z12, and (III) the number and percentage of centers that detected a response. The high frequencies of CMV-specific CD8+ T-cells in donors D1 and D5 were readily detected by all participants (mean of 1 per 141 CD8 ± 113 in D1 and mean of 1 per 80 CD8 ± 24 in D5, respectively). For influenza-specific CD8+ T-cells, the results were more variable. Influenza-tetramer+ cells in donor D3 were detected by all participants with a mean frequency of one cell in 1014 CD8+ T-cells ± 355. In Donor D5, 11 of 12 laboratories detected a mean of one tetramer binding cell per 1106 CD8+ T-cells ± 508. Influenza-specific cells were less numerous in healthy subjects D1 and D2 and were only detected by five and eight laboratories, respectively. No false positive reactivity was reported by any of the participants.
Fig. 1Example of tetramer staining results as provided by four selected participating centers Z5, Z12, Z8 and Z1. All stainings were performed on donor D1 from phase I/2005 who showed reactivity with both of the tested tetramers. Cells were gated either on the lymphocyte population (Z1), or the subsets of CD3+CD8+ (Z5) or CD3+ (Z8, Z12), according to the Ab combination used by each lab. The upper panel shows results for tests with the CMV-tetramer, the lower panel shows results for tests with the influenza-tetramer. In all dot-plots, the tetramer staining is displayed on the y-axis and anti-CD8-staining on the x-axis. Number of counted CD8+ T-cells and percentage of tetramer-positive cells among the CD8 subset are indicatedTable 1Overview of the tetramer results from phase I/2005 of the CIMT monitoring panelD1 CMVD1 FluD2 FluD3 FluD5 CMVD5 FluMin44820,0006,6671,8181061,786(I)Mean1418,0951,9091,014801,106Max353,77452659526588Z11293,774635870106758(II)Z2112––1,818701,770Z3n.d––701n.d–Z497––8541061,342Z5161––90988606Z61396,8961,3161,00091651Z744820,0006,6671,266821,724Z81005,12854559587877Z9n.d–1,3161,274n.d1,786Z1094–2,5971,360861,439Z11994,6751,66787758588Z1235–52664526625Detected by10/105/128/1212/1210/1011/12(III)Detected %100426710010092(I) Minimum, mean and maximum frequencies of antigen-specific T-cells(II) Results obtained from the individual centers Z1–Z12. All frequencies are indicated as 1 per x counted CD8+ T-cells(III) Number and percentage of centers which detected a given reactivity in donors D1, D2, D3 and D5
Eleven laboratories analyzed the five PBMC samples for the presence of HLA-A*0201-restricted CMV-specific and influenza-specific IFNγ-producing T-cells by ELISPOT assay. Only one group (Z10) used an intracellular cytokine staining as a functional test (data not shown because no comparison with other groups possible). Table 2 shows (I) the minimum, mean and maximum frequencies of antigen-specific cells, (II) the results obtained from the individual centers Z1–Z12, and (III) the number and percentage of centers that detected each reactivity. As described in the “Materials and methods”, results of spot-forming cells per seeded PBMC were accepted as a positive reaction only when passing statistical testing and when the number of antigen-specific spots exceeded the number of spots in the background wells by at least a factor of two. IFNγ-producing cells reactive against CMV were detected by 10 of the 11 laboratories in donor D1 (mean reactivity was 1 per 1,855 PBMC ± 825) but only by 8 of 11 in donor D5 (mean reactivity was 1 per 4,405 PBMC ± 3,762). The influenza-specific T-cells present in subject D3 were detected by six laboratories, while the responses in the healthy subjects with markedly lower numbers of peripheral specific T-cells (D1, D2 and D5) were detected by three laboratories only.
Table 2Overview of the IFNγ ELISPOT results from phase I/2005 of the CIMT monitoring panelD1 CMVD1 FluD2 FluD3 FluD5 CMVD5 FluMin3,06162,50055,55533,33311,42850,000(I)Mean1,85538,14143,58917,5474,40530,811Max88810,25630,7698,5711,03914,705Z11,006––8,571––(II)Z22,439–––2,816–Z31,29562,500–22,7271,41227,727Z41,312––10,9091,980–Z5––––––Z6233*––1,960*253*–Z71,89510,25644,44433,33311,42850,000Z83,061–––6,896–Z988841,66655,55512,1951,03914,705Z10NDNDNDNDNDNDZ111,769–30,769–––Z123,030–––5,263–Detected by10/113/113/116/118/113/11(III)Detected %912727557327(I) Minimum, mean and maximum frequencies of antigen-specific cells(II) Results obtained from the individual centers Z1–Z12. All frequencies are indicated as 1 per x seeded PBMC except for Z6* where it is indicated as 1 per x seeded CD8+ T-cellsResults from Z6 were not included for calculation of the mean frequency of antigen-specific T-cells in D1, D3 and D5(III) Number and percentage of centers that detected a given response in donors D1, D2, D3 and D5ND not determined
Subgroup analysis reveals that the number of CD8+ T-lymphocytes analyzed affects the sensitivity of the tetramer staining
Although the tetramer stainings were performed with centrally prepared reagents following set guidelines, centers were left free to select several parameters according to their own protocols, and this could have influenced the test results (see “Materials and methods”). Most of the participants used monoclonal antibodies specific for CD3 and CD8 to co-stain the cells. There were no obvious differences in the performance of the centers depending on which antibody clones, antibody combinations or cytometer were used (data not shown).
There was a high degree of variability in the number of CD8+ cells which were analyzed per staining, ranging from only 0.5 × 104 to about 19 × 104 (inter-center variation). In addition, a non-negligible intra-center variation was observed for the number of counted CD8+. We therefore analyzed each individual staining independently of the center that performed it and focused on the number of CD8+ T-cells that had been counted. For the six different antigen-specific populations detectable, a total of 68 tests was performed by the group (see Table 1). Overall, antigen-specific T-cell reactivities were reported in 82% of the tests (56/68, mean of duplicate stainings). When less than 30,000 CD8+ T-cells were counted, only 70% of all responses were found. In contrast, 89% of all responses were manifest when more than 30,000 CD8+ T-cells were counted (Fig. 2a). When antigen-specific T-cells were present at high frequency, the number of cells counted did not influence the result, because CMV-specific T-cells from donors D1 and D5 were detected irrespective of the number of CD8+ T-cells in the test. However, for the influenza-specific cells, positivity was registered in only 75% of all tests performed (36 of 48 tests). Strikingly, we observed a marked difference for the results derived from those tests involving less than 30,000 CD8+ T-cells (56% success in detection) as compared to tests performed with more than 30,000 CD8+ T-cells (84%).
Fig. 2a Subgroup analysis of tetramer results from phase I/2005. Bars indicate the percentage of positives that could be detected by tetramer staining. The first group of bars shows the results for all of the six detectable positives, the second group shows results from stainings with the CMV-tetramer and the third group of columns shows results from stainings with the influenza-tetramer. The open bars in each group represent all tests performed, grey bars represent results obtained in tests that were performed on more than 3 × 104 CD8+ T-cells and black bars represent results obtained in tests that were performed on less than 3 × 104 CD8+ T-cells. The boxes within each bar indicate the fraction of tests with a positive result. The asterisk indicates a P-value < 0.05 by Chi-square analysis. b Subgroup analysis of ELISPOT results from phase I/2005. The bars indicate the percentage of positive reactivities detected by IFNγ ELISPOT assays. The open bar shows the percentage of all reactivities detected by all 11 centers that performed the ELISPOT assay as the functional test. Criteria for division of centers into two subgroups were based on the following requirements: do not use allo-APC (first subgroup analysis), use a resting time (second subgroup analysis) or use equal or more than 400,000 PBMC per well (third subgroup analysis). Grey bars always represent centers that were in conformity with the indicated minimum requirement, black bars show results from centers that did not fulfil that requirement. The boxes within each column indicate the fraction of centers in each category. The asterisks indicate a P-value < 0.05 in Chi-square analysis
In conclusion, the ability to detect antigen-specific T-cell reactivities by tetramer staining was mainly affected by the number of CD8+ T-cells stained and analyzed, especially when the antigen-specific T-cells were present at low or moderate frequencies. We therefore modified our guidelines for the tetramer assay and recommended staining at least 1 × 106 PBMC and analyzing all cells in the tube. In addition, we provided an example of how optimal cell gates and dot-plot quadrants could be selected.
ELISPOT assays are heterogeneous and require standardization
The ELISPOT analyses were performed according to 11 more or less different protocols. The most discernible differences that were observed in these protocols concerned (1) the different types of multi-screen plates, (2) the serum origin, (3) the use of duplicates, triplicates or quadruplicates, (4) the use of allogeneic APC, (5) the inclusion of a resting phase after thawing the PBMC, (6) the number of PBMC per well, (7) the type of antibodies used, (8) the type of spot-reader, and the (9) enzyme and substrate for staining of the spots. Each center also used a different plate protocol (distribution of the wells, number of replicates, control tests).
The influence of each of these parameters on the number of positive responses was studied by further analysis in which the laboratories were divided into two subgroups. As a result, several criteria were identified which could help to improve the sensitivity and comparability of detection.
All data sets (duplicates, triplicates or quadruplicates) were first analyzed by Student t test for unpaired samples (“Materials and methods”). In our panel, one center used quadruplicates, nine centers used triplicates and one center performed the ELISPOT analysis in duplicates. Due to the variety in the replicates, responses measured by duplicate wells failed to pass the Student t test more often as compared to triplicates.
Overall, the 11 centers were able to detect 50% of all possible reactivities in this panel phase (Table 2; Fig. 2b). In a subgroup of three laboratories (Z5, Z6 and Z8), an allogeneic APC population (T2 or K562-A*0201 cells) was added for binding and presentation of the synthetic peptides. The three centers that used allo-APC detected only 28% of all responses, while the other centers detected 58% of all responses.
In five laboratories (Z3, Z4, Z7, Z8, and Z9) PBMC were thawed, and then incubated in culture medium at 37°C. After this resting phase of 2–20 h, living cells were washed, counted and seeded into ELISPOT plates. Laboratories using a resting phase detected 73% of the positive reactivities (22 out of 30 potentially positive tests). No significant difference in the ability to detect antigen-specific T cells was found using shorter or longer resting-times. In contrast, the laboratories that did not use a resting procedure detected only 30% of all positives (Fig. 2b).
Finally, the number of cells seeded per well differed considerably between all participants and ranged from 1 to 6 × 105 PBMC. We divided the laboratories arbitrarily into two groups, those using either more than 4 × 105 PBMC (Z4, Z7, Z8, and Z9) or less than 4 × 105 PBMC (Z1, Z2, Z3, Z11 and Z12). The first group detected 71% of all positive samples, whereas the second group was able to detect only 43% of all positives (Fig. 2b). Centers Z5 and Z6 used a defined number of separated CD8+ T-cells in the ELISPOT and were therefore not included in this subgroup analysis.
None other of the nine depicted protocol variables had any obvious impact on the detection of specific T-cells. As a conclusion from these results, four minimum requirements were formulated for the ELISPOT protocol: (1) perform triplicates for each test antigen (2) do not use allo-APC (3) add a resting time to increase the proportion of living cells seeded and (4) use a minimum number of 4 × 105 PBMC per well.
Phase II/2006 of the interlaboratory testing project—general aspects
To formally prove that the requirements formulated for tetramer staining and ELISPOT analysis increase the ability of the participants to detect antigen-specific CD8+ T-cells and reduce the inter-center variability, we decided to repeat the analysis in a second phase of the panel, with the same participants (phase II/2006). In this round, all groups were asked to follow our modified guidelines for the tetramer- and the ELISPOT-assays.
Again, all PBMC samples were prepared and pre-tested in one central lab and peptide antigens and PE-conjugated tetramers were also provided from one source. As one investigator had meanwhile moved to another lab, we added a 13th center to the group. PBMC from seven selected healthy HLA-A*0201-positive donors and 1 HLA-A*0201-negative donor (D3) were required to be analyzed for the presence of HLA-A*0201-restricted CMV-specific T cells and for influenza-specific T-cells. The mean number of recovered cells after thawing was sufficient to perform the tests. When all the data were combined, it became clear that subjects D2, D5 and D8 possessed CMV-specific CD8+ T-cell subsets, and D1, D2, D4, D6 and D7 possessed influenza-specific CD8+ T-cells. Therefore, each laboratory could theoretically have measured eight positives (3× CMV and 5× Influenza) in this second phase.
Analysis of CD8+ T-cell tetramer binding using the new guidelines
In the second phase, a total of 104 tests were performed to detect the eight possible tetramer reactivities. Following the modified guidelines for tetramer staining, the mean number of CD8+ T-cells that were counted in each separate test increased markedly (+36%): a mean of about 49,000 CD8+ cells were analyzed in the phase I (n = 68 tests) and a mean of 67,000 CD8+ T-cells in phase II (n = 104 tests). The number of cells per test ranged from 12,000 to 467,000 CD8+. In 81% (84 of 104) of the tests >30,000 CD8+ were counted (compared to 66% of all relevant tests in the first phase). Table 3 shows (I) the minimum, mean and maximum frequencies of antigen-specific T-cells, (II) the results obtained from the individual centers Z1–Z13, and (III) the number and percentage of centers that detected each T-cell specificity. Donors D2, D5 and D8 showed very strong reactivities with the CMV-tetramer, with mean frequencies of 1/45 CD8+ T-cells, 1/37 CD8+ T-cells, and 1/19 CD8+ T-cells, respectively. All 13 laboratories were able to detect these populations (Table 3). All but one center detected the influenza-specific cells present at high frequencies in donors D6 (1/1116 CD8+ T-cells) and D7 (1/347 CD8+ T-cells). Donors D1, D2 and D4 possessed fewer specific cells (1/3,739, 1/3,573 and 1/5,278 CD8+ T-cells) which were found by 12, 9 and 9 centers, respectively. Three laboratories also reported influenza tetramer-binding CD8+ cells in D5 or D8. According to the results of the other centers as well as from the ELISPOT (see below), these stainings were considered as false positive (not shown). One center (Z13) was not able to detect any of the influenza-specific CD8+ T-cell reactivities. Finally, no tetramer+ cells were described in the HLA-A*0201-negative donor (D3).
Table 3Overview of tetramer results from phase II/2006 of the CIMT monitoring panelD1 FluD2 CMVD2 FluD4 FluD5 CMVD6 FluD7 FluD8 CMVMin7,1437710,00010,000603,33386942(I)Medium3,739453,5735,278371,11634719Max1,538281,2502,500305712028Z13,333 453,3335,0003976929410(II)Z24,000303,3335,000301,10025021Z34,00047–5,0004558827020Z41,538712,8576,6663376926342Z56,666542,500–431,42837724Z65,00077–10,000601,66686927Z72,857473,3336,6663883329022Z82,000353,3333,333316662448Z93,3333110,0003,333323,33362520Z103,333282,222–3095220213Z111,666361,2502,5003257121515Z127,41337––357142608Z13–53––34––20Detected by12/1313/139/139/1313/1312/1312/1313/13(III)Detected %9210069691009292100(I) Minimum, mean and maximum frequencies of antigen-specific T-cells(II) Results obtained from the individual centers Z1–Z13. All frequencies are indicated as 1 per x counted CD8+ T-cells(III) Number and percentage of centers that detected each of the eight possible responses
Analysis of CD8+ T-cell responses by ELISPOT following the introduction of a set of four rules
In this second phase, all laboratories performed ELISPOT analysis following local protocols, all of which conformed to the newly introduced minimum requirements. Table 4 shows (I) the minimum, mean and maximum frequencies of antigen-specific cells, (II) the results obtained from the individual centers Z1–Z13, and (III) the number and percentage of centers that detected the response. High frequency T-cell responses against CMV could readily be detected by all 13 centers in donors D5 and D8 and by 12 of 13 in donor D2. Failure of center Z4 to detect the CMV reactivity in donor D2 was due to a very high background of the medium control. The number of spots representing IFNγ-producing cells after influenza-peptide stimulation was generally lower, and consequently, the influenza-specific T-cell responses in subjects D1, D2, D4 and D6 were detected by fewer laboratories (four centers for D1, three centers for D2, two centers for D4 and ten centers for D6). The high numbers of influenza-specific T-cells present in D7 were detected by all 13 laboratories (Table 4).
Table 4Overview of IFNγ ELISPOT results from phase II/2006 of the CIMT monitoring panelD1 FluD2 CMVD2 FluD4 FluD5 CMVD6 FluD7 FluD8 CMVMin44,1181,79133,80358,8241,74548,38714,7201,698(I)Medium28,8231,08816,39549,96099914,2654,6691,023Max10,3453961,23141,0963914,4581,706269Z144,118596––59648,38714,720318(II)Z2–1,732––1,7454,7392,2221,698Z3–1,333––1,117–2,9821,292Z4––––774–2,273447Z5–997––1,15717,6473,8271,209Z610,3451,03114,151–1,006–1,7061,005Z7–1,317––1,06110,0006,0001,661Z832,258396–58,8243914,4583,623269Z928,571966–41,09691214,4238,9551,081Z10–8471,231–8067,4263,052696Z11–1,04433,803–1,08720,3393,6701,273Z12–1,791––1,38710,6193,0571,583Z13–1,008––9494,6154,615770Detected by4/1312/133/132/1313/1310/1313/1313/13(III)Detected %3192231510077100100(I) Minimum, mean and maximum frequencies of antigen-specific cells(II) Results obtained from the individual centers Z1–Z13. All frequencies are indicated as 1 per x seeded PBMC(III) Number and percentage of centers that detected each of the eight possible responses
Comparison of the results obtained in both phases
When the mean frequencies of all T-cell responses in both testing rounds were compared, it became clear that there was a difference in the distribution of reactivities (Fig. 3). In the tetramer assay, the mean T-cell frequency of the six possible positives in the first phase was 1 per 2,083 CD8+ T-cells. This value was 1 per 1,769 CD8+ T-cells for the eight possible positives in the second phase. Similarly, the mean T-cell frequency of the responses detected in IFNγ ELISPOT was 1 per 22,369 PBMC for Phase I/2005 but 1 per 14,653 PBMC for Phase II/2006. To allow a comparison of the overall performance in both phases of the panel, we therefore decided to define theoretical thresholds for high, moderate and low T-cell responses and then to compare data of the participating laboratories within these groups.
Fig. 3Distribution of antigen-specific T-cell frequencies in the two testing phases as obtained by tetramer staining (a) and IFNγ ELISPOT assays (b). The figure shows the six reactivities (filled circle) and the calculated mean of all reactivities from phase I/2005 (filled line) as well as the eight reactivities (open circle) and calculated mean of all reactivities from phase II/2006 (open line). The frequency of antigen-specific T-cells is indicated on the y-axis as 1 per x counted CD8+ T-cells for the tetramer test and as 1 per x seeded PBMC for the ELISPOT assay
In order to define such thresholds for low, medium and high T-cell responses, we first displayed the probability of detecting each of the 14 different reactivities as a value in a coordinate system and inserted a trendline. For both the tetramer assay and the ELISPOT assay, we observed a clear correlation between the frequencies of antigen-specific T-cells and the number of participating centers that were able to detect these populations. We then calculated the theoretical frequencies at which 90% (y = 90) and 50% (y = 50) of all participants could detect a given response (Fig. 4a, b) and used these two thresholds to divide all reactivities into three distinct classes of T-cell responses (“high”, “moderate” and “low”).
Fig. 4Probability of detecting a reactivity by a tetramer staining, or b IFNγ ELISPOT assay. A trendline was inserted on the basis of results from all 14 reactivities from both phases of the panel. The figure shows the six reactivities from phase I/2005 (filled squares) and the eight reactivities from phase II/2006 (open squares). The frequency of antigen-specific T-cells is shown on the x-axis in 1 per x counted CD8+ T-cells for the tetramer assay (a) or 1 per x seeded PBMC for the ELISPOT assay (b). X-values for y = 90% and y = 50% are indicated by the broken lines
For the tetramer assay, T-cell frequencies exceeding 1 per 1,200 CD8+ T-cells were therefore classified as “high”, whereas frequencies of less than 1 per 7,650 CD8+ were classified as “low” (Fig. 4a). Following the same rules for the ELISPOT assay, T-cell responses of at least one IFNγ spot per 2,850 PBMC can be considered as “high” and T-cell responses of less than one spot per 19,000 PBMC as “low” (Fig. 4b).
With these calculated assay-specific thresholds for high, moderate and low T-cell responses, we compared the results obtained in the two phases. For the tetramer assay, the ability to detect high frequency T-cells (>1 per 1,200 CD8+) did not differ in the two phases, and was not influenced by the number of CD8+ analyzed, as previously seen for each of the two phases separately (Fig. 5a). However, for moderate and low T-cell frequencies, we found that they could be successfully detected in only 54% of cases in the first phase but this improved to 77% in the second phase. Moreover, here, the number of cells counted did have an impact on the ability to detect low frequency T-cells. In the first phase, only 14% were detected when less than 30,000 CD8+ were counted, as compared to 71% when more than 30,000 CD8+ T-cells were counted. The same trend was observed in phase II/2006, but in this case 40% of assays with less than 30,000 CD8+ successfully detected the moderate to low T-cell frequencies compared to 83% counting more than 30,000 CD8+ (Fig. 5a).
Fig. 5a Percentage of reactivities actually detected by tetramer staining. The first two groups of bars show the detection rate for the nine high reactivities (>1 per 1,200 CD8+ T-cells) in phase I/2005 and phase II/2006. The next two groups of bars show the detection rate for five moderate to low reactivities (<1 per 1,200 CD8+) in phase I/2005 (third group) or phase II/2006 (fourth group). The open bars represent all tests performed, grey bars represent results obtained in tests that were performed on more than 3 × 104 CD8+ T-cells and filled bars represent results obtained in tests that were performed on less than 3 × 104 CD8+ T-cells. b Percentage of reactivities detected in IFNγ ELISPOT assays. The first two groups of bars show the rate of detection of the four high reactivities (>1 per 2,850 PBMC in phase I/2005 and phase II/2006. The next two groups of columns show the rate of detection for the ten moderate to low reactivities (<1 per 2,850 PBMC) in phase I/2005 and phase II/2006. The open bars represent the performances of all centers in the respective panel phase, grey bars represent results obtained from the five centers that already fulfilled at least three of the four minimum criteria in phase I/2005 and filled bars represent results obtained from centers that fulfilled less than three of the four minimum criteria in phase I/2005
We then analyzed the capacity of the laboratories to measure either high T-cell responses (>1 per 2,850 PBMC) or low to moderate T-cell responses (<1 per 2,850 PBMC) in the ELISPOT assay. This analysis was performed for two defined subgroups of participants. The first subgroup included those five centers (Z3, Z4, Z7, Z8 and Z9) that already fulfilled three or four of the requirements in the first phase of the panel. These five centers did not have to introduce any change or at least no major changes to their protocol for the repetition of the experiments in phase II. The second subgroup included the new center Z13 (led by a colleague that had been in a laboratory that only fulfilled one of four requirements in phase I) and all others that had fulfilled only one or two of the four requirements in the first phase. All laboratories in this second group had to introduce marked changes to their locally established protocols. Similar to the tetramer analysis, the new requirements were not necessary to detect antigen-specific responses among the category of high T-cell frequencies in either the first or second phases (Fig. 5b). However, applying the set of rules defined in phase I markedly improved the capacity of centers to detect the low to moderate T-cell responses. The first subgroup detected a total of 68% of the low to moderate reactivities in phase I, whereas the second subgroup detected only 20% (Fig. 5b). After harmonization of the protocols, both subgroups performed equally well. In addition, the inter-group variability in detecting positive responses was reduced in phase II (percentage of detected responses ranged from 38 to 88% with a mean of 67 ± 16%) as compared to phase I (percentage of detected responses ranged from 0 to 100% with a mean of 55 ± 33%).
Experience does not equal performance
Among the 13 centers that had participated in phase II, tetramer stainings had been performed for 1–8 years. Similarly, the experience in the ELISPOT technology varied between 1 and 10 years. For both techniques, we could not find any correlation between the years of experience and the ability to detect T-cell responses, not even among the subgroups of moderate or low T-cell responses (not shown).
Discussion
Whenever new techniques are introduced to the scientific community, they are first only available to a small group of expert laboratories. If these assays are robust and applicable for specific research or routine applications, they spread to the international community. In general, the “original” protocol then undergoes several adaptations in order to meet specific needs. On the one hand, changes can be beneficial and result in the improvement of protocols. On the other, this evolutionary process leads to employment of many different protocol variants, limiting comparison of the study results obtained by different laboratories. Thus, standardization approaches should be omitted during the initial development but are absolutely required when assays have become firmly established. In recent years, several activities aiming at the harmonization of techniques used to monitor the presence of antigen-specific T-cells have been initiated for ELISPOT [28–31], tetramer staining [32] and ICS [33–36]. While these studies showed the feasibility, general applicability and the diversity of performance among participants, they were not designed to either systematically investigate the influence of distinct protocol variables nor to test whether changes to these parameters can lead to a global improvement of the group. The CIMT monitoring panel is the first initiative that has now introduced the two-step approach proposing a strategy where technical variables that influence the performance of a defined assay are first systematically identified (“first step”) followed by a new testing phase where resultant protocol changes are validated under controlled conditions within the same group of investigators (“second step”).
As soon as a number of protocol variables that might have influenced the sensitivity and the quality of the tests were identified in the first phase of our study, it was decided to validate this finding in a second phase. Because this two-step approach was not initially foreseen, the second phase was performed with PBMC samples obtained from different donors than those used in the first round. The distribution and the frequencies of detectable T-cell responses directed against the chosen model antigens were different in the first and second group of donors (Fig. 3) precluding a direct comparison of the results obtained in both phases of the panel. To circumvent this problem, two assay-specific frequency thresholds were introduced that allowed us to distinguish classes of T-cell responses (low, moderate and high) (Fig. 4a, b). Clearly, high-frequency T-cell responses were detected irrespective of the protocol used and as such did not allow the identification of factors that exert a strong influence on the sensitivity and variability of the protocols used. Relevant parameters could only be detected when the comparison was focused on the detection of T-cells that are present at low to moderate frequencies in PBMC. This finding should be taken into account when selecting model antigens for use in monitoring panels [37], in particular by laboratories that are interested in the detection of peripheral tumor-specific T-cells, which are often present at low frequencies, even after vaccination.
Although our experiments do not specifically address the question of detection limits for the ELISPOT and tetramer assays, we could detect a high variability in the sensitivity of protocols used by the different participants. The majority of labs (y = 90%) is able to detect responses with a frequency above 1 per 1,200 CD8+ T cells in the tetramer assay or responses with a frequency above 1 per 2,859 PBMC in the ELISPOT. Note that some of the centers could reliably detect a response with a frequency of about 1 per 8,000 CD8+ T cells in the tetramer assay and about 1 per 40,000 PBMC in the ELISPOT assay. These low frequencies are in the range of that is commonly reported as the detection limit for internally validated protocols for both technologies [39, 40, own unpublished observations]. Another important task of standardization efforts should be to decrease the variation of results obtained in a group of several laboratories down to the stable and low values (15–30%) that can be reproducibly found within single labs. In order to quantify the variation of results among laboratories we calculated the coefficient of variation for all 14 reactivities of the two panel phases. The CVs were determined on the base of centers that were able to detect the respective T cell response and the results are shown in supplementary Tables S1a, b. As expected, the CVs we found in our inter-laboratory testing project were higher than those reported from intra-center analysis [39, 40].
In the ELISPOT assay, the background spot numbers obtained by the different participants varied greatly, but we were unable to correlate this finding to a distinct variable. Since the spontaneous cytokine secretion impacts significantly on the sensitivity of this assay, factors that especially influence the non-specific spot production, possibly the medium type or serum source, will need to be systematically analyzed in a separate study.
The main conclusions from our study have been drawn on the basis of subgroup analyses. Although the CIMT panel in general (13 centers in this initial action), and consequently the subgroups formed during the analysis were rather small, we could already identify statistically significant differences in the ability to detect positive responses. We concluded that the number of counted CD8+ T-cells is the most influential crucial factor for the tetramer assay and that the combination of a resting-time and a high number of PBMC leads to increased sensitivity in the ELISPOT assay. This suggests that the impact of the identified technical variables on the quality of the assays is high. In order to identify those protocol variables that lead to more subtle differences, a larger group of participants would be needed.
In addition to the systematic identification of variables that correlate with sensitivity/insensitivity of various assays, inter-laboratory testing projects also allow the rapid evaluation of individual performance among a group. Interestingly, the finding that experienced laboratories did not perform better than laboratories which recently applied these techniques strongly suggests that non-optimal protocols, once established in a lab, can commonly be maintained for several years. Periodic comparison of local protocols with those of other centers is recommended. Even if a new staff member uses an established protocol, it is recommended to have them participate in inter-laboratory testing/teaching exercise. Regular participation in multi-center comparisons could thereby help to optimize and validate participants’ performance over time and to maintain sensitive protocols or minimal standards. This is of great importance when material from expensive clinical trials has to be analyzed.
All data from the CMV-serology, from the pre-testing experiments and from the results generated by the participating laboratories in ELISPOT and tetramer staining were taken together for each donor in order to qualitatively validate the presence of CMV- and influenza-specific T-cells. To estimate the quantity, i.e. the frequency of specific T-cells in each donor, we calculated the average of all qualitatively positive results, as well as the standard deviations. This procedure constitutes only an approximation of the real number of antigen-specific cells present in a given sample, and cannot be taken as a method for determining absolute T-cell frequencies. Cell samples that contain pre-defined numbers of antigen-specific T-cells (e.g. spiked T-cell clones), especially tumor-reactive T-cells, are not easily available for use in multi-center comparisons, although such standard samples are urgently needed. We see this as one major bottle-neck for the optimization and standardization of immunomonitoring techniques. Methods to generate such standard samples for broader use will therefore be elucidated with high priority in the near future for the next phases of this international collaboration. Another big challenge will be to define accepted rules for the settings of the equipment used in these analyses (flow cytometer or ELISPOT reader) in order to uniformly process and analyze the raw data. Ten from eleven laboratories that performed the ELISPOT assay in the first phase used an ELISPOT reader for spot counting. It is known that spot counts between centers can differ significantly and this may be explained by the use of different reading machines, different settings for the same type of machine or by the experience of the operator. Within this group, four different commercially available reading systems were used (supplementary Table S2). We were not able to identify differences between the types of ELISPOT readers. A new ELISPOT panel phase is currently in preparation, that will specifically focus on the performances of different ELISPOT readers and try to introduce tools to control inter center variation. In addition, none of the participant reported on the use of live/dead cell discrimination on thawed PBMC samples for the FACS-based experiments. Whether the combination of staining with Ab/HLA-tetramers and vital dyes or with a resting phase is beneficial for increasing the sensitivity of the tetramer staining assay could be addressed in future testing actions.
Results from a proficiency panel of 36 laboratories from nine different countries in which the ELISPOT assay was validated are now also being reported [41]. This initiative, conducted under the aegis of the Cancer Vaccine Consortium (CVC), was mainly designed to offer an external validation to the participating laboratories but the in depth analysis of the obtained data sets lead to similar findings and recommendations as the CIMT monitoring panel. It confirmed that a resting phase of cells prior to addition to the ELISPOT plates is advantageous and should therefore be generally recommended. Furthermore, a long year experience in a technology did not guarantee for a sensitive test and failure to detect specific T cell responses concentrated on the weak responses. The fact that two independent initiatives come to similar findings is surely notable and shows the necessity to carry on running proficiency panels.
Last but not least, we would like to stress that even the best guidelines and protocols alone cannot guarantee good performance. Monitoring of antigen-specific T-cell responses requires skills as well as experience. Participation in immunomonitoring panels cannot compensate for the need to constantly educate and train staff and to develop specific expertise for covering individual needs. Nevertheless, we strongly believe that by organizing further two-step inter-laboratory testing projects, the CIMT monitoring panel will be able to improve the sensitivity of the assays used for immunomonitoring as well as to actively participate in the harmonization of these assays, which is required to enable the comparison of immunotherapeutic trials performed in different centers.
Electronic supplementary material
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Physiological importance of RNA and protein mobility in the cell nucleus

Trafficking of proteins and RNAs is essential for cellular function and homeostasis. While it has long been appreciated that proteins and RNAs move within cells, only recently has it become possible to visualize trafficking events in vivo. Analysis of protein and RNA motion within the cell nucleus have been particularly intriguing as they have revealed an unanticipated degree of dynamics within the organelle. These methods have revealed that the intranuclear trafficking occurs largely by energy-independent mechanisms and is driven by diffusion. RNA molecules and non-DNA binding proteins undergo constrained diffusion, largely limited by the spatial constraint imposed by chromatin, and chromatin binding proteins move by a stop-and-go mechanism where their free diffusion is interrupted by random association with the chromatin fiber. The ability and mode of motion of proteins and RNAs has implications for how they find nuclear targets on chromatin and in nuclear subcompartments and how macromolecular complexes are assembled in vivo. Most importantly, the dynamic nature of proteins and RNAs is emerging as a means to control physiological cellular responses and pathways.
Introduction
It is an irony of science that the discovery of fundamental principles is often delayed. A case in point is the cell biological investigation of the cell nucleus. The nucleus is arguably the functionally most prominent cellular organelle as it houses the vast majority of the genetic information and is the exclusive site of essential processes including transcription, RNA processing, replication and DNA repair. Yet, our understanding of how the nucleus is organized and how nuclear architecture contributes to cellular function is only now emerging and still lags behind that of most other cellular compartments. Fortunately, pioneering studies over the past decade have uncovered several of the fundamental principles which determine nuclear architecture and function (Lanctot et al. 2007; Misteli 2001; Spector 2003). These studies have revealed the presence of distinct structural elements, particularly lamin proteins and possibly short actin filaments, within the nucleus and they have led to the identification of a large number of distinct nuclear subcompartments in which specific nuclear functions, such as the synthesis and processing of ribosomal RNA in the nucleolus, occur (Burke and Stewart 2006; Gruenbaum et al. 2005; Handwerger and Gall 2006; Hernandez-Verdun 2006; Lamond and Sleeman 2003) (Fig. 1a). In addition, it has become clear that the genome is non-randomly organized within the spatial confines of the nucleus with specific chromosomes and genes preferentially localizing to specific sites (Lanctot et al. 2007; Meaburn and Misteli 2007) (Fig. 1b, c). These observations paint a picture of a highly complex and heterogeneous cell nucleus. They also indicate that some properties of the nucleus are counterintuitive. For example, despite the requirement to accommodate ∼2 m of DNA within the cell nucleus of a diameter of typically ∼10 μm, chromatin is estimated to only occupy about 15% of the nuclear volume.
Fig. 1Nuclear architecture and function. The mammalian cell nucleus contains a large number of non-random and structural features. a Nuclear lamins (green) form a network of architectural elements at the periphery. This nuclear lamina is implicated in mechanical support of the nucleus, organization of chromatin (red/blue) in the nuclear interior, as a platform for signaling events and in the sequestration of nuclear proteins. b, c Genomes are non-randomly organized within the nucleus. b Each chromosome occupies a distinct chromosome territory (red chromosome 14; green chromosome 17 in human breast epithelial cells). c Pericentromeric heterochromatin regions (white) from multiple chromosomes often cluster into chromocenters. Bar 6.3 μm. Image courtesy: a Gianluca Pegoraro, b Karen Meaburn, c Manjari Mazumdar, all National Cancer Institute, NIH
A key question in understanding nuclear architecture is how protein and RNAs move within the nucleus. This is a key issue if we are to gain a full appreciation of what the nuclear environment is like. More importantly, the mode of nuclear motion of molecules has physiological implications as it affects how proteins find their target sequences in the genome, how RNAs are exported from the nucleus upon transcription and how proteins are sequestered within the nucleus for regulatory purposes. Some initial insights into these issues are summarized in broad terms here.
The motion of RNA and proteins in the cell nucleus
We have long known that proteins and RNAs must be able to move within the cell nucleus. Messenger RNAs traverse the nucleus and are promptly exported from the nucleus after their synthesis and complete processing. Newly synthesized proteins on the other hand are imported into the nucleus and rapidly disperse throughout its volume, implying effective intranuclear trafficking. Furthermore, proteins rapidly relocalize within the nucleus upon experimental or physiological changes in conditions. For example, inhibition of protein synthesis leads to rapid and dramatic relocalization of a large number of nucleolar proteins (Andersen et al. 2005), and pre-mRNA splicing factors swiftly respond to transcriptional activation and inhibition (Jiménez-García and Spector 1993; Misteli et al. 1997). How proteins and RNAs move within the nucleus and how they find their targets, however, was unknown for long. It was not clear whether movements are energy dependent, whether they occur by directed transport or what the speed of intranuclear trafficking is. These fundamental questions have recently been answered (Misteli 2001).
The investigation of intranuclear protein and RNA dynamics was made possible by the development of techniques to visualize and measure the motion of these molecules by time-lapse microscopy. Tracking of proteins by photobleaching methods revealed a surprising degree and speed of intranuclear trafficking. The analysis of tracers such as GFP or dextran which do not undergo specific interactions with DNA or other proteins indicated that the nuclear environment is permissive for rapid diffusional motion (Görisch et al. 2005; Misteli 2001; Seksek et al. 1997). The diffusion of molecules within the nucleus is only limited by the steric constraints imposed by chromatin and nuclear bodies (Görisch et al. 2005; Misteli 2001; Seksek et al. 1997). The typically measured diffusion coefficients of 10–100 μm2 s−1 are similar to those observed in the cytoplasm and are only ∼5 times less than that in solution. As expected for diffusion, this rapid motion is energy-independent and non-directional.
RNA motion within the nucleus is similarly rapid and non-directional. Several methodological approaches involving fluorescently labeled, microinjected, engineered or endogenous RNAs demonstrate that ribosomal RNAs as well as polyA-RNAs move freely in a non-directional manner with a diffusion coefficient of 0.03–0.1 μm2 s−1 within the nucleus (Ritland-Politz et al. 2006; Shav-Tal et al. 2004). A diffusion coefficient of this magnitude is sufficient to ensure transport of an RNA particle from deep within the nucleus to the cytoplasm within a few minutes, consistent with biochemical observations on kinetics of RNA maturation and transport. Thus energy-independent, diffusion-based movement of RNA particles alone can account for the observed kinetics of RNA export, and no active mechanisms are required to ensure rapid export. An impressive demonstration of the non-directional motion of mRNA comes from studies in which a nascent RNA is visualized at its site of transcription and its export to the cytoplasm measured in living cells (Ritland-Politz et al. 2006; Shav-Tal et al. 2004). These studies show that RNAs synthesized from genes positioned in proximity to the nuclear envelope diffuse away from their site of synthesis in all directions rather than follow a direct path to the nearest nuclear pore. This observation powerfully demonstrates the non-directed, diffusion-based motion of RNAs in the nucleus.
Trafficking as a means of targeting
One of the conceptually most challenging problems in cell biology is the question of how molecules find their specific targets within a cell or within an organelle. This problem is particularly complex in the cell nucleus where transcriptional regulators need to find their specific target genes amongst the myriad of potential binding sites within the genome. Somewhat counter-intuitively, the non-directional, but rapid, motion of proteins within the cell nucleus provides a means to ensure targeting of proteins to specific genome locations (Misteli 2001). The power of targeting by random diffusion within the nucleus is best illustrated when considering how a transcription factor finds its target genes but it also applies to targeting of proteins to nuclear compartments or any other nuclear site.
Proteins a priori do not know where their targets are and we do know of any directed transport systems, such as a molecular motor-based mechanism, that would bring a factor to a specific gene or a specific location within the nucleus. Thus, the only way for a transcription factor to find its target is to scan the genome. The intrinsic ability of proteins to rapidly move within the nucleus by diffusion-based mechanisms permits such genome scanning (Misteli 2001). This occurs by a transcription factor freely diffusing within the nucleoplasm until it interacts by chance with chromatin. The molecule will now probe whether the sequence it has encountered is a specific binding site such as in the promoter of one of its target genes. If it is, the transcription factor will be captured and stably associated with its specific target site. If the sequence is not a binding site, the molecule, after a short interaction, will dissociate from chromatin and continue its diffusional journey through the nucleus.
It might appear at first glance that such a stop-and-go model for genome scanning would be insufficiently effective. However, the observed dynamic properties of transcription factors are entirely consistent with this model. We know that the residence time of most transcription factors even on specific DNA binding sites is in the order of a few seconds and that their interaction with non-specific sites is even faster, most likely in the order of tens of milliseconds (Gorski et al. 2006; Houtsmuller et al. 1999; McNally et al. 2000; Phair et al. 2004; Sprague et al. 2006). Assuming these time scales for binding, one can calculate that it takes a single transcription factor molecule only a few minutes to search the entire genome space. Considering that most transcription factors exist in several thousand copies and have multiple target genes, their random diffusional motion is entirely sufficient to ensure a steady supply at their target genes. This assumption is further supported by direct measurement of transcription factor flux on an endogenous rRNA promoter demonstrating the collision of several hundred molecules per second (Dundr et al. 2002). Since proteins similarly move by diffusional motion within the cytoplasm, it stands to reason that the random scanning is also a key mechanism of protein targeting in the cytoplasm and represents a universal mechanism for how proteins find their targets.
A key feature, and a requirement, in a genome-scanning model of targeting is that the interactions of proteins with chromatin are transient. This has been confirmed by photobleaching methods on a large number of DNA binding proteins (Gorski et al. 2006; Phair et al. 2004; Sprague et al. 2006). The transient nature of protein–chromatin interactions is important for three reasons. First, it allows proteins to maintain a high rate of motion and thus allows faster scanning. Were protein–chromatin interactions static, they would get stuck at non-specific or incorrect binding sites which would slow down their overall motion. Second, the short-life of protein–chromatin interactions continuously makes available binding sites which can then be scanned by diffusing transcription factors. If proteins interacted for extended periods of time on chromatin, non-specific or improper binding would block access of the correct factors. Third, the dynamic dissociation allows for change. Were protein complexes permanently bound to their target sites, changes in transcriptional activity such as in response to physiological stimuli could only occur after active removal of the bound complex, presumably by dedicated and specialized molecular machinery. In contrast, in a dynamic binding model the natural flux of proteins provides a window of opportunity for association of a distinct regulator each time a bound protein or complex dissociates as part of its normal binding cycle.
Protein dynamics as the key for formation of nuclear compartments
A hallmark of the mammalian cell nucleus is the presence of distinct subnuclear compartments and domains in which particular functions occur (Handwerger and Gall 2006; Hernandez-Verdun 2006) (Fig. 2). The prototypical nuclear compartment is the nucleolus, a distinct intranuclear compartment in which ribosomal RNAs are synthesized and partially processed. Other prominent nuclear domains include splicing factor compartments which serve as storage and assembly sites for spliceosomal components, and the Cajal bodies which are possibly involved in maturation of small nuclear RNPs. The structure of intranuclear bodies is not determined by a membrane, and the principles underlying their biogenesis are extremely poorly understood. The recently revealed dynamic properties of proteins in nuclear compartments give a hint as to the principles involved in subnuclear compartment assembly.
Fig. 2Intranuclear compartments. The mammalian cell nucleus contains a larger number of distinct intranuclear compartments. The nucleolus is the site of ribosomal RNA synthesis and is a prototypical nuclear body. The complex organization of the nucleolus is revealed by multi-color staining of distinct nucleolar components. RPA43 RNA polymerase I transcription factor, Fibrillarin snRNP component, B23 rRNA processing factor. Image courtesy of Miroslav Dundr, Rosalind Franklin University of Medicine and Science, Chicago
The key property in understanding the formation of nuclear compartment is the remarkable fact that the association of proteins with their compartments is highly dynamic (Dundr et al. 2004; Kruhlak et al. 2000; Phair and Misteli 2000). Typical residence times of proteins within a compartment are in the seconds range. In practical terms the dynamic behavior of nuclear body proteins means that they undergo repeated rapid cycles of association and dissociation between the nuclear body and the nucleoplasm. As a consequence, a nuclear body is in perpetual flux and its structure is determined by the ratio of on-rate versus off-rate of its proteins.
Given these observations, the simplest scenario to explain the formation of nuclear subdomains is a self-organization model in which the collective sum of all interactions amongst proteins in a body establishes and determines its structure (Carrero et al. 2006; Misteli 2007). In this model, nuclear bodies are essentially dynamic protein aggregates which form as a consequence of multiple, transient interactions amongst a large number of proteins. This is consistent with the lack of defining intranuclear membranes and with the absence of any known dedicated structural elements within intranuclear bodies. While dynamic observations on nucleoli, splicing factor compartments and Cajal bodies support this view, this model has not been rigorously tested experimentally. A key prediction, and a way to experimentally address this fundamental question, is that it should be possible to create nuclear bodies de novo and that any nuclear body component should be able to nucleate the formation of a body. Experimental systems to test these predictions are now available, and we are eagerly awaiting the results of these key experiments.
The importance of protein and RNA dynamics in regulatory events
The observation of dynamic properties of proteins in the cell nucleus of living cells has suggested that dynamic trafficking is an intrinsic property of proteins and RNAs. The dynamic behavior of proteins and RNAs clearly contributes to their proper function. However, is dynamic trafficking important for physiological regulation? Several observations demonstrate that dynamic trafficking of both proteins and RNA indeed can have regulatory function by several means.
Of particular importance as a regulatory mechanism is intranuclear sequestration (Fig. 3). Accumulation of a protein at a particular nuclear site or within a nuclear compartment can serve to either increase the local concentration of a factor at that site or to reduce its abundance in the rest of the nucleus. Several proteins are now known to accumulate, in particular nuclear locations in response to physiological cues and their sequestration is thought to contribute to their cellular function.
Fig. 3Intranuclear retention and sequestration as a regulatory means. a Retention of proteins within intranuclear subcompartments regulates their nuclear concentration and their rate of nuclear export. b Sequestration of proteins at the nuclear periphery, particularly via interaction with the lamina (blue) controls their nucleoplasmic concentration and their availability at target genes. Sequestration can act both as a negative or a positive regulatory mechanism. Sequestration of an activator leads to repression, sequestration of a repression leads to activation. c Retention of partially or fully processed RNA within intranuclear compartments modulates RNA export rate
Prototypical example of sequestration as a regulatory mechanism is the accumulation of the ubiquitin ligases MDM2 and the von Hippel-Lindau tumor suppressor protein VHL in the nucleolus (Mekhail et al. 2005, 2007). These two proteins are responsible for the ubiquitination of the p53 tumor suppressor and the hypoxia-induced factor HIF, respectively. Modification of these two targets leads to their nuclear export and degradation in the cytoplasm. Since ubiquitination is a major regulatory mechanism in their function, the concentration of the ubiquitin ligase in the nucleus significantly determines their fate. Both MDM2 and VHL diffuse freely within the nucleus. However, in response to physiological cues, both MDM2 and VHL can become sequestered in the nucleolus, thus reducing their nucleoplasmic concentration and the extent of ubiquitination of their targets (Mekhail et al. 2005, 2007) (Fig. 1a). Sequestration of these proteins appears to occur by increased retention of diffusing molecules within the nucleolus. How this retention is regulated is currently unclear (Mekhail et al. 2005, 2007). The possibility that retention is a highly controlled mechanism comes from observations on nucleostemin, a nucleolar protein implicated in stem cell maintenance and cancer proliferation (Meng et al. 2007; Tsai and McKay 2005). The retention of nucleostemin in the nucleolus is controlled by a GTP–GDP binding cycle which might be directly or indirectly linked to signaling pathways. In addition to nucleostemin the localization of several additional GTP-binding domain containing nucleolar proteins seems to be controlled in a similar fashion (Meng et al. 2007; Tsai and McKay 2005).
Intranuclear sequestration can also be a means of negative regulation. The basic helix–loop–helix transcription factor Hand1 is expressed in trophoblast stem cells and is required for their differentiation along several lineages (Martindill et al. 2007). Hand1 is sequestered in the nucleolus and upon stimulation during differentiation is rapidly released and moves into the nucleoplasm where it presumably acts on target genes. Hand1 is retained in the nucleolus by its interaction with I-mfa, and this interaction is sensitive to phosphorylation of Hand1. Upon phosphorylation Hand1 dissociates from I-mfa and is released from the nucleolus. Interestingly, both the sequestration of Hand1 in the nucleolus and its release are functionally important. Premature release of Hand1 triggers differentiation, whereas the presence of Hand1 is also required to maintain the stem-cell potential of trophoblast stem cells. Thus nucleolar sequestration of this key cell fate regulator serves a dual regulatory role (Martindill et al. 2007).
Most examples of intranuclear sequestration involve the nucleolus. However, the nuclear lamina has recently also emerged as a major site for transcription factor sequestration (Heessen and Fornerod 2007) (Fig. 1b). Prominent transcription factors which associate with the nuclear periphery either via interaction with the lamins or with the inner nuclear membrane proteins include c-fos, Oct-1 and Rb (Imai et al. 1997; Ivorra et al. 2006; Johnson et al. 2004). It is tempting to speculate that the association of these factors with the periphery reduces the intranuclear concentration and thus their availability at target genes (Heessen and Fornerod 2007). A physiological role for such peripheral sequestration is most strongly suggested by observations on the pro-proliferation factor c-fos which associates with the lamina in quiescent cells but is released upon entry of cells into the cell cycle correlating with activation of some of its target genes (Ivorra et al. 2006). Similarly, release of Oct-1 from the lamina correlates with activation of collagenase, one of its prime target genes (Imai et al. 1997). The sequestration of transcription factors at the nuclear periphery is a potentially powerful and simple mechanism of transcriptional regulation. It will be important to determine how widespread this mechanism is and how precisely the association of transcription factors with the lamina is controlled.
Dynamic trafficking of RNA is similarly used as a regulatory mechanism (Prasanth and Spector 2007). In particular, it appears that RNA retention can act as a quality control mechanism for RNA export. For one, incompletely spliced or processed RNAs are retained at their sites of transcription and not released into the nucleoplasm for export. In addition, pools of stable polyadenylated RNAs are enriched in the nuclear splicing factor compartments, although their function remains unclear (Huang et al. 1994). But retention is also emerging as a mechanism for regulation of specific RNAs. A non-coding RNA transcribed from the mouse Cat2 gene encoding a cationic amino acid transporter is retained in the nucleus via a dedicated 3′ end containing extensive repeat sequences (Prasanth et al. 2005). These sequences are responsible for accumulation of the RNA in a nuclear compartment identified as paraspeckles (Fig. 1c). This retention serves a physiological function because upon stress the Cat2 RNA is cleaved, released from paraspeckles and rapidly exported into the cytoplasm for translation, thus ensuring a rapid physiological stress response. Although the Cat2 RNA is to-date the best characterized example of such retention, a similar mechanism is likely at work for the migration-stimulating factor (MSF) mRNA whose 3′ end resembles that of Cat2 and is retained within the nucleus (Kay et al. 2005). Under conditions of MSF secretion the RNA appears to be released from the nucleus and rapidly translated. A further candidate for control via nuclear retention is the neuron-specific gomafu RNA which is retained in intranuclear compartments. Interestingly, gomafu does not seem to encode for any protein and might thus be a non-coding regulatory RNA which is retained in the nucleus (Sone et al. 2007).
Conclusions
The past few years have seen a dramatic change in how we view the cell nucleus. We have come to appreciate the presence of distinct structural elements within the nucleus, the presence of a multitude of intranuclear bodies and the fact that genomes are non-randomly organized within the nuclear space. One of the most consequential findings has been the realization that just about every aspect of nuclear organization is highly dynamic. Both proteins and RNAs move rapidly within the nucleus, and they only transiently interact with chromatin and nuclear bodies. It is now clear that the dynamic nature of nuclear components is a fundamental property and has implications for how molecules are targeted to their final destinations and for how intranuclear compartments form. Most importantly, it is becoming clear that the dynamic properties of nuclear proteins are critical for various mechanisms of physiological regulation, particularly via sequestration and retention of proteins and RNA. It is likely that these recently discovered examples of regulation by modulation of dynamic interactions within the cell nucleus are only the proverbial tip of the iceberg, and it stands to reason that we should consider the contribution of dynamic interactions and trafficking in any nuclear event we investigate in the future.

rna
dynamics
diffusion
nuclear architecture