Monitoring regulatory T cells in clinical samples: consensus on an essential marker set and gating strategy for regulatory T cell analysis by flow cytometry

Abstract

Regulatory T cell (Treg)-mediated immunosuppression is considered a major obstacle for successful cancer immunotherapy. The association between clinical outcome and Tregs is being studied extensively in clinical trials, but unfortunately, no consensus has been reached about (a) the markers and (b) the gating strategy required to define human Tregs in this context, making it difficult to draw final conclusions. Therefore, we have organized an international workshop on the detection and functional testing of Tregs with leading experts in the field, and 40 participants discussing different analyses and the importance of different markers and context in which Tregs were analyzed. This resulted in a rationally composed ranking list of "Treg markers". Subsequently, the proposed Treg markers were tested to get insight into the overlap/differences between the most frequently used Treg definitions and their utility for Treg detection in various human tissues. Here, we conclude that the CD3, CD4, CD25, CD127, and FoxP3 markers are the minimally required markers to define human Treg cells. Staining for Ki67 and CD45RA showed to provide additional information on the activation status of Tregs. The use of markers was validated in a series of PBMC from healthy donors and cancer patients, as well as in tumor-draining lymph nodes and freshly isolated tumors. In conclusion, we propose an essential marker set comprising antibodies to CD3, CD4, CD25, CD127, Foxp3, Ki67, and CD45RA and a corresponding robust gating strategy for the context-dependent analysis of Tregs by flow cytometry.

Fig. 1

CD25^posCD127^lowFoxp3^pos def.1, Foxp3^posHelios^pos def.2, and Foxp3^hiCD45RA^neg def.3 aTregs express high levels of Treg-associated markers, suggesting that they are bona fide Tregs. Phenotypic characterization of def.1, def.2, and def.3 Tregs was performed by flow cytometry. Gating of the three different Treg definitions was performed as described in supplementary figs. 2a, 3a, and 5a. Expression of the Treg-associated markers Helios, CD45RA, CTLA4, Ki67, and CD39 is depicted for a representative healthy donor (HD; left) and multiple HD (right; Helios/CD45RA/CTLA4/Ki67 for six and CD39 for two HD) for a CD25^posCD127^lowFoxp3^pos def.1 Tregs, b Foxp3^posHelios^neg and Foxp3^posHelios^pos def.2 Tregs, and c Foxp3^hiCD45RA^neg def.3 aTregs, Foxp3^intCD45RA^pos def.3 nTregs, and Foxp3^intCD45RA^neg def.3 non-Tregs. Percentage Helios/CD45RA/CTLA4/Ki67/CD39 expression is given as percentage of the designated population in the upper right quadrant in the FACS plot for the representative HD (left) and as mean percentage for the six HD (right)

Fig. 2

Treg enumeration based solely on Foxp3 and Helios (def.2) or Foxp3 and CD45RA (def.3) led to an underestimation of CD25^posCD127^lowFoxp3^pos def.1 Tregs through exclusion of def.1 Treg cells in the Foxp3^posHelios^neg (def.2) or Foxp3^intCD45RA^neg non-Treg (def.3) populations. Overlap between the three most commonly used Treg definitions (def.1, def.2, and def.3) is given for a a representative HD and b, c six HDs. a Distribution of def.1 Tregs is shown in def.2 and def.3 populations (left), of def.2 Tregs is shown in def.1 and def.3 populations (middle), and of def.3 Tregs is shown in def.1 and def.2 populations (right). Percentage of Tregs analyzed via def.1, def.2, or a combination thereof b and via def.1, def.3, or a combination thereof c is depicted as percentage of CD4^pos T cells. Overlap between the designated populations is calculated in relation to def.1 Tregs (set at 100) and given in the bar graph for each population

Fig. 3

Treg gating based on Foxp3 and CD45RA (def.3) is subjective in TIL as it is difficult to distinguish between Foxp3^hi versus FoxP3^low cells due to the absence of Foxp3^intCD45RA^pos population. Def.1, def.2, and def.3 Treg analyses were performed by flow cytometry. Treg analysis based on CD25 and CD127 (def.1), FoxP3 and Helios (def.2), and FoxP3 and CD45RA (def.3) is given for PBMC of a representative healthy donor (HD) and an ovarian cancer (OvCA) patient and for a TDLN and TIL sample of representative cervical cancer (CxCa) patient in a and for multiple donors in b. Gates were set as described in supplementary figures 2a, 3a, and 5a. Percentage of CD25^posCD127^low and CD25^posCD127^lowFoxp3^pos def.1; Foxp3^posHelios^neg and Foxp3^posHelios^pos def.2; and the def.3 Foxp3^hiCD45RA^neg aTreg, Foxp3^intCD45RA^pos nTreg, and Foxp3^intCD45RA^neg non-Treg populations is given as percentage of CD3^posCD4^pos T cells. Example of the problem with gating based on Foxp3 and CD45RA in TIL is depicted by the arrow in a

Fig. 4

High pretreatment frequencies of Foxp3^hiCD45RA^neg and Ki67^pos def.1 Tregs (i.e., activated def.1 Tregs) are associated with reduced overall survival in OvCa patients undergoing chemo-immunotherapeutic therapy. The use of Ki67 and CD45RA provides additional information on the activation status of def.1 Tregs. Treg analysis was performed based on CD25, CD127, and Foxp3 (def.1), Foxp3 and Helios (def.2), and Foxp3 and CD45RA (def.3) in PBMC of 21 chemo-immunotherapy-treated ovarian cancer (OvCA) patients (EM Dijkgraaf et al., submitted for publication). Pretreatment values of def.1, def.2, and def.3 Tregs were determined, and overall survival (OS) of these patients following chemo-immunotherapy was plotted in Kaplan–Meier curves for pretreatment values of def.1 (left), def.2 (middle), and def.3 (right) Tregs in a. Activation status of def.1 Tregs was determined by measuring the frequency of Foxp3^hiCD45RA^neg and Ki67^pos cells within the def.1 Tregs. Gating and Kaplan–Meier curves are depicted in b for pretreatment values of Foxp3^hiCD45RA^neg def.1 Tregs and c for pretreatment values of Ki67^pos def.1 Tregs. Gates for Foxp3^hiCD45RA^neg and Ki67^pos were set as shown in the FACS plots. Patients were grouped into two groups based on the median of the total population, i.e., into a group of patients with frequencies that were below the median (dotted line) or with frequencies above the median (solid line) for the indicated parameter, after which survival analysis was performed. Number of patients and corresponding OS for each group is given. Statistical analysis was performed by log-rank testing, and differences were considered significant when p < 0.05