Immunity
Letter Toward Meaningful Definitions of Innate-Lymphoid-Cell Subsets Yannick Simoni1,* and Evan W. Newell1,* 1Agency
for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), 138648 Singapore *Correspondence:
[email protected] (Y.S.),
[email protected] (E.W.N.) http://dx.doi.org/10.1016/j.immuni.2017.04.026
We appreciate comments made by the Spits and Ziegler groups about our article (Simoni et al., 2017). One purpose of our study was to broadly assess innate lymphoid cell (ILC) cellular profiles as a way to help provide some clarity about how best to identify all types of these cells across many different human tissues. We understand that there is concern about our inability to detect a clearly defined helper-type ILC1 cell subset in numerous human tissues. Overall, we think that our results are consistent with previous data from reports showing that ILC1-like cells are heterogeneous and that many share fundamental characteristics of T cells, which seems contradictory with the definition of ILCs. Here we discuss technical considerations and why we think that our t-SNE-based approach was an appropriate way to assess the relevance of an ILC1 subset definition. Lastly, we emphasize results showing that none of the ILC1-like cells we could identify expressed T-bet and why we feel these experiments were adequately controlled. We should also note that our study highlights the presence of cells that nicely fit the definition of intra-epithelial (ie) ILC1 cells in several tissues. Such cells are sometimes referred to loosely as ILC1 cells, except that we think these ieILC1like cells would be better classified as a subset of NK cells. The original paper published by the Spits group identified populations of helper-type ILC1s in human tonsils and intestine as Lin– CRTH2– CD127+ c-Kit– NKp44–. They reported that these cells expressed T-bet and produced IFN-g but that they did not express NK cell markers (Bernink et al., 2013). The Ziegler group studied ILC1 cells obtained from human blood (Lin– CRTH2– CD127+ c-Kit– NKp44– CD56–) and also reported that some expressed T-bet (40% to 80%) and could produce IFN-g. However, many of these same cells expressed CD5, CD4, CD8, and/or intracellular CD3ε
(Roan et al., 2016). In 2013, another study reported an innate-like T cell population in both human blood and human tonsils. As per the Spits and Ziegler groups’ definition, this population exhibited phenotypic characteristics of ILC1s. However, this population produced TNF-a and lymphotoxin-a and b but not IFN-g. This study also showed that these cells expressed intracellular CD3 and T cell receptor (TCR) b (Bekiaris et al., 2013). Lastly, single-cell mRNA sequencing of human ILCs from tonsils revealed that most sorted ILC1 cells expressed genes coding for T-cell surface markers CD4; CD8A and CD8B; CD5; CD28; TCRa; TCRb; and/or the CD3 complex (CD3D, CD3E, and CD3G). Furthermore, the TBX21 gene (coding for T-bet) was not differentially expressed by ILC1 cells as compared with other ILC subsets (Bjo¨rklund et al., 2016). Taken together, these results obtained from independent groups are consistent with our observations and show that the ILC1 cell population is phenotypically heterogeneous, does not necessarily express T-bet or produce IFNg, and is also at least partially composed of T cells that have downregulated surface expression of TCR. This made us wonder about the utility for the definition of such helper-type ILC1s. Because ILCs represent a small population of cells among lymphocytes (less than 0.1% in human blood), the fact that other cells can contaminate ILC gating makes the interpretation of these and other studies of ILCs very difficult. As mentioned, we used magnetic columns to pre-enrich for all types of ILC and make their assessment feasible. We were aware that this pre-enrichment could not fully remove T cell contamination, and so we relied on other markers and combinations of markers to identify these and other contaminating cells. As previously reported, CD5 staining can be an effective way to remove contaminating T cells from the analysis (Burkhard et al.,
760 Immunity 46, May 16, 2017 ª 2017 Published by Elsevier Inc.
2014), but we understand the argument that CD5 could be expressed on some T cell-like ILC1 cells. However, we question the utility of including these cells within the ILC1 definition if they are really more like T cells in their expression of various other T-cell markers (see above). t-SNE analysis is a powerful way to delineate and cluster cells in an unbiased way that can take into account all markers being probed as well as all cells under study. Our strategy was to perform t-SNE on live CD45+ Lin– (FcεR1a– CD14– CD19– CD123–) cells to reduce potential bias in the identification ILC populations (see our article). In the Spits group’s comment, an alternate strategy for t-SNE analysis of our publicly available data (https://flowrepository.org/ id/FR-FCM-ZYZX) is provided. By this analysis, t-SNE was performed on a much more restricted population of live CD34 CD94– CD161+ CD45+ Lin + CD127 cells. Note that we believe that this pre-gating strategy will lead to the arbitrary exclusion of CD161– ILC3 cells, which we consider to be phenotypically continuous with CD161+ ILC3 cells, as we described (Figure S1A). Furthermore, this strategy will lead to the identification of ILC1-like cells as a distinct cluster. However, because contaminating cells such as T cells, dendritic cells, and hematopoetic stem cells are excluded by this approach, it is not possible to determine whether ILC1 cells would have co-clustered with any of these populations, as we had observed by our approach. So, we believe that a more restrictive pregating of cells before t-SNE analysis would unnecessarily bias the results and lead to an incorrect interpretation. One limitation we see for this logic comes from the fact that we chose the 29 markers and that this might impart bias into this analysis. However, t-SNE performed on single-cell mRNA sequencing data obtained from ILCs, which is unbiased in terms of the selection of genes
Immunity
Letter being profiled, showed that 18% of ILC1 cells clustered with ILC3, ILC2, or NK cells. And we note that the other predominant ILC1 cluster contained cells expressing high levels of various T cells markers as described above (Bjo¨rklund et al., 2016). To us, these observations strongly suggest that ILC1s are a heterogeneous and indistinct cell population, and they make us wonder about the utility of their current definition. Although we believe it arbitrarily excludes some ILC3 cells (see above), gating on CD127+ CD161+ among Lin– cells does reduce contamination of various ILC populations (e.g., dendritic cells and T cells). Here, to reconcile our findings with the Spits group’s analysis of our publicly deposited data, we used the gating strategy suggested for human tonsils; this strategy includes the use of CD161 (Figure S1B). Using this strategy, we identified a small yet variable population of ILC1-like cells, representing 4.6% ± 6.2% of all ILCs (including NK cells) in human tonsil (Figure S1C). Note that this population represents less than 2% of ILCs in most individuals (12/20) studied. As mentioned in our paper and by the Ziegler and Spits groups, these cells variably expressed CD5 (0% to 96% of ILC1-like cells), showing that at least two subpopulations (CD5+ and CD5–) constitute ILC1like cells (Figure S1D). Furthermore, our data seem clear in showing that ILC1like (either CD5+ or CD5–) cells do not express detectable T-bet, a transcription factor defining these cells (Bernink et al., 2013). Compared to NK cells (internal positive control), our data show that putative ILC1 cells express T-bet at a level similar to that of ILC2 and ILC3 cells (internal negative controls) across all individ-
uals tested (Figure S1E). Similarly, using the gating strategy suggested for the analysis of human colon, we can confirm the analysis shown from an individual having a high frequency of ILC1-like cells (18.5% among all ILCs). However, our analysis shows that, as for the tonsil, many of these cells express CD5 and none express detectable T-bet. We found this gated population of ILC1-like cells to be heavily contaminated with cells that express CD4, CD8, CD56, and/or Granzyme A (Figure S1F). Because our data led us to the conclusion that ILC1s were not a distinct population of innate cells, we did not attempt to assess their functional characteristics. Thus, we did not specifically address the concept of in vitro ILC3-to-ILC1 plasticity. Our data do show that in vitro culture with appropriate cytokines can induce IFN-g production by ILC3s (note that in the process we found that these cells maintained their ILC3 phenotype). To our knowledge, there are no specific reports of in vivo IFN-g+ ILC3s in humans. Thus, we believe that observations made from in vitro long-term culture of ILCs can demonstrate their plasticity, but the in vivo relevance of these observations remains to be determined. In summary, we can agree that by using bi-axial gating, we can observe a heterogeneous and rare cell population of T-bet– ILC1-like cells. These cells have been shown to express various T-cell markers, such as CD5, CD4, CD8, CD56, intracellular CD3, and the TCRb chain (Bekiaris et al., 2013; Bjo¨rklund et al., 2016; Roan et al., 2016), and they cluster mainly with T cells in t-SNE analysis. Thus, we believe that our observations together with previous publications
show that cells fitting previous definitions of ILC1 cells did not constitute a distinct or consistent population at the phenotypic and functional level across any of the tissues we tested. We agree that some cells could be an innate-like T-cell population, as shown previously (Bekiaris et al., 2013). However, the classification of these cells as IFN-g-producing innate cells (helper-type ILC1s) does not seem appropriate. SUPPLEMENTAL INFORMATION Supplemental Information includes one figure and can be found with this article online at http://dx. doi.org/10.1016/j.immuni.2017.04.026.
ACKNOWLEDGMENTS We would like to thank Florent Ginhoux and Marco Colonna for helpful discussions.
REFERENCES Bekiaris, V., Sedy, J.R., Rossetti, M., Spreafico, R., Sharma, S., Rhode-Kurnow, A., Ware, B.C., Huang, N., Macauley, M.G., Norris, P.S., et al. (2013). J. Immunol. 191, 4611–4618. Bernink, J.H., Peters, C.P., Munneke, M., te Velde, A.A., Meijer, S.L., Weijer, K., Hreggvidsdottir, H.S., Heinsbroek, S.E., Legrand, N., Buskens, C.J., et al. (2013). Nat. Immunol. 14, 221–229. Bjo¨rklund, A.K., Forkel, M., Picelli, S., Konya, V., Theorell, J., Friberg, D., Sandberg, R., and Mjo¨sberg, J. (2016). Nat. Immunol. 17, 451–460. Burkhard, S.H., Mair, F., Nussbaum, K., Hasler, S., and Becher, B. (2014). PLoS ONE 9, e94196. Roan, F., Stoklasek, T.A., Whalen, E., Molitor, J.A., Bluestone, J.A., Buckner, J.H., and Ziegler, S.F. (2016). J. Immunol. 196, 2051–2062. Simoni, Y., Fehlings, M., Kløverpris, H.N., McGovern, N., Koo, S.L., Loh, C.Y., Lim, S., Kurioka, A., Fergusson, J.R., Tang, C.L., et al. (2017). Immunity 46, 148–161.
Immunity 46, May 16, 2017 761
