www.sciencetranslationalmedicine.org/cgi/content/full/7/297/297ra115/DC1 Supplementary Materials for Human NK cell repertoire diversity reflects immune experience and correlates with viral susceptibility Dara M. Strauss-Albee, Julia Fukuyama, Emily C. Liang, Yi Yao, Justin A. Jarrell, Alison L. Drake, John Kinuthia, Ruth R. Montgomery, Grace John-Stewart, Susan Holmes, Catherine A. Blish* *Corresponding author. E-mail: [email protected]Published 22 July 2015, Sci. Transl. Med. 7, 297ra115 (2015) DOI: 10.1126/scitranslmed.aac5722 This PDF file includes: Fig. S1. Serial negative gating strategy used to define NK, CD4 + T, and CD8 + T cells from PBMCs. Fig. S2. Representative gates to evaluate marker expression on NK cells. Fig. S3. Stability analysis of CD56 bright versus CD56 dim NK cells. Fig. S4. Human NK cell repertoire and function are stable for 6 months in an individual. Fig. S5. Subpopulation analysis for the 10 most frequently detected NK subpopulations. Fig. S6. Donor age does not correlate with NK cell diversity. Fig. S7. CMV serostatus does not correlate with NK cell diversity or the frequency of NKG2A or CD57. Fig. S8. NK diversity does not correlate with viral suppression in vitro. Fig. S9. No correlative features distinguish NK cells responding to HIV-1– infected CD4 + T cells. Fig. S10. Cytokine-producing NK cells are more diverse than non–cytokine- producing NK cells. Fig. S11. Proportion of variance explained in correspondence analysis. Table S1. Mass cytometry antibody panels used in each experiment. Table S2. HIP and SBB cohort information. Table S3. Mama Salama Study cohort information. Reference (48) Other Supplementary Material for this manuscript includes the following:
Published 22 July 2015, Sci. Transl. Med. 7, 297ra115 (2015) DOI: 10.1126/scitranslmed.aac5722
This PDF file includes:
Fig. S1. Serial negative gating strategy used to define NK, CD4+ T, and CD8+ T cells from PBMCs. Fig. S2. Representative gates to evaluate marker expression on NK cells. Fig. S3. Stability analysis of CD56bright versus CD56dim NK cells. Fig. S4. Human NK cell repertoire and function are stable for 6 months in an individual. Fig. S5. Subpopulation analysis for the 10 most frequently detected NK subpopulations. Fig. S6. Donor age does not correlate with NK cell diversity. Fig. S7. CMV serostatus does not correlate with NK cell diversity or the frequency of NKG2A or CD57. Fig. S8. NK diversity does not correlate with viral suppression in vitro. Fig. S9. No correlative features distinguish NK cells responding to HIV-1–infected CD4+ T cells. Fig. S10. Cytokine-producing NK cells are more diverse than non–cytokine-producing NK cells. Fig. S11. Proportion of variance explained in correspondence analysis. Table S1. Mass cytometry antibody panels used in each experiment. Table S2. HIP and SBB cohort information. Table S3. Mama Salama Study cohort information. Reference (48)
Other Supplementary Material for this manuscript includes the following:
(available at www.sciencetranslationalmedicine.org/cgi/content/full/7/297/297ra115/DC1)
Table S4 (Microsoft Excel format). Frequencies of NK markers used in repertoire stability analysis. Table S5 (Microsoft Excel format). Frequencies of NK markers after 72-hour culture with IL-15 or IL-2, or without stimulation, for the SBB cohort. Table S6 (Microsoft Excel format). Frequencies of functional markers used in stability analysis. Table S7 (Microsoft Excel format). CD57 and NKG2A frequencies and NK diversity scores for HIP and cord blood cohorts. Table S8 (Microsoft Excel format). Proportion of phenotypes expressing CD57 and NKG2A. Table S9 (Microsoft Excel format). Functional frequencies of CD57+ and CD57− NK cells. Table S10 (Microsoft Excel format). Diversity of NK cells in the presence or absence of HIV-1–infected CD4+ T cells. Table S11 (Microsoft Excel format). Diversity of NK cells in the presence or absence of WNV-infected cells.
Figure S1. Serial negative gating strategy used to define NK, CD4+ T, and CD8+ T cells from PBMCs. NK cells are defined as CD19-, CD3-, CD33-, HLA-DR-
/CD56+, LILRB1low, CD56/CD16+. CD4+ T cells are CD19-, CD3+, CD4+, CD8-. CD8+ T cells are CD19-, CD3+, CD4-, CD8+. For experiments using purified NK and CD4+ T cells, residual monocytes and B cells are excluded by a CD14/CD19 dump gate.
Figure S2. Representative gates to evaluate marker expression on NK cells. All markers used for the calculation of diversity are shown. Donor SBB1.
Figure S3. Stability analysis of CD56bright versus CD56dim NK cells. NK cells from HIP donors were gated into CD56brightCD16- (A, B) and CD56dimCD16+ (C, D) subpopulations as shown. As described for Figure 1B and 1C, they were analyzed in a Bayesian hierarchical model for the stability of each receptor within each subpopulation. These subpopulations showed similar levels of stability as in the total NK population (95% credible intervals for mean SD for CD56bright <0.1; CD56dim <0.12).
Figure S4. Human NK cell repertoire and function are stable for 6 months in an individual. CD107a (A), IFN-γ (B), and TNF (C) in donor HIP12 at timepoints T1-T6. (D) Stability of NK function based on frequency of TNF, IFN-γ, and CD107a at T1-T6. Columns represent six blood draws for a single donor; circle size, frequency of functional cells. n=3.
Figure S5. Subpopulation analysis for 10 most frequently detected NK subpopulations. (A) Columns represent six blood draws for a single donor; circle size, frequency of NK population expressing listed markers. (B) Mean standard deviations of subpopulation frequency over the six-month period given by Bayesian hierarchical model (see Methods for details). Black bars, 95% credible intervals for mean standard deviation of each subpopulation frequency for T1-T6, HIP donors, n=12.
Figure S6. Donor age does not correlate with NK cell diversity. NK cell diversity at individual time points (circles) and the mean of all time points (triangles) compared to donor age at first blood draw. HIP donors. Linear regression, black, and 95% CI, gray. Generalized Estimating Equation p=0.31, AR-1 correlation structure.
Figure S7. CMV serostatus does not correlate with NK cell diversity or the frequency of NKG2A or CD57. Mean diversity (A) CD57 frequency (B) or NKG2A frequency (C) stratified by CMV serostatus for HIP donors. While donors with CMV seropositivity trend toward higher diversity and lower total NKG2A expression, none of these associations is significant (Diversity p = 0.1; CD57 p = 0.9; NKG2A p = 0.3, Student’s t-test).
Figure S8. NK diversity does not correlate with viral suppression in vitro. NK cells were allowed to suppress autologous HIV-1-infected CD4+ T cells in vitro for 48h (see Methods for assay details). Linear model coefficients p=0.61, 0.51, and 0.85 for MOI = 40, 100, and 250, respectively. n=6.
−50
0
50
100
150
200 300 400 500 600 700Diversity
Per
cent
Sup
pres
sion
MOI: 40 100 250
Figure S9. No correlative features distinguish NK cells responding to HIVinfected CD4+ T cells. The Citruwith NK cells responding to HIVto uninfected CD4+ T cells. NK cells clustered as expected, with marker plots for CD56 (A) and CD16 (B) shown. However, the L1build a subset of correlative features based on abundances (medians (D) with greater than 40% accuracy. This unbiased approach shows that no subset of NK cells preferentially responds to HIVgroup.
features distinguish NK cells responding to HIVThe Citrus method (48) was used to detect features associated
with NK cells responding to HIV-1-infected CD4+ T cells, compared to those responding T cells. NK cells clustered as expected, with marker plots for CD56
) shown. However, the L1-regularized regression model was unable to features based on abundances (C) or functional feature
with greater than 40% accuracy. This unbiased approach shows that no ferentially responds to HIV-1-infected CD4+ T cells. n=9 per
features distinguish NK cells responding to HIV-1–was used to detect features associated
T cells, compared to those responding T cells. NK cells clustered as expected, with marker plots for CD56
regularized regression model was unable to r functional feature
with greater than 40% accuracy. This unbiased approach shows that no T cells. n=9 per
Figure S10. Cytokine-producing NK cells are more diverse than non–cytokine-producing NK cells. Diversity of IFN-γ- and TNF-producing NK cells following stimulation with IL-2 and HIV-1-infected target cells. Wilcoxon signed-rank tests: IFN-γ p=9.8*10-4, TNF p=4.9 x 10-3. SBB donors, n=11.
�
��
�
�
�
�
��
�
�
�
�
�
�
�
�
��
��
�
p = 9.8 × 10−4
�
�
�
��
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
p = 4.9 × 10−3
IFN−gamma TNF
75
100
125
150
175
Non−Functional
Functional Non−Functional
Functional
Div
ersi
ty
Figure S11. Proportion of variance explained in correspondence analysis. The first three axes, considered in Figure 6A-B and D-E, explain a cumulative 34% of the total variance for HIV-1 (A), and 24% for WNV (B), indicating the extreme diversity of the NK repertoire. Even while accounting for 1/4 - 1/3 of the total variance, we were still able to detect a significant increase in the sum of squared distances from the centroid following addition of virus-infected CD4+ T cells (Figure 6C, F), showing the high degree of divergence detected.
Table S1. Mass cytometry antibody panels used in each experiment.
Table S2. HIP and SBB cohort information. Age, sex, CMV serostatus, and KIR and MHC-I genotyping are shown.
Table S3. Mama Salama Study cohort information. Samples were selected from a cohort of 1304 women with a total of 25 incident HIV-1 infections, of which 13 had a PBMC sample available. Risk-score for HIV-1 acquisition includes the woman’s age (continuous), knowledge of partner’s receipt of an HIV-1 test (dichotomous), marriage status (dichotomous), and history of trading sex for money or goods (dichotomous).
