CD8 T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy

Abstract

While food allergy oral immunotherapy (OIT) can provide safe and effective desensitization (DS), the immune mechanisms underlying development of sustained unresponsiveness (SU) following a period of avoidance are largely unknown. Here, we compare high dimensional phenotypes of innate and adaptive immune cell subsets of participants in a previously reported, phase 2 randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with allergic reactions upon food challenge after a withdrawal period; n = 21 vs. 30 respectively among total 120 intent-to-treat participants). Lower frequencies of naïve CD8⁺ T cells and terminally differentiated CD57⁺CD8⁺ T cell subsets at baseline (pre-OIT) are associated with SU. Frequency of naïve CD8⁺ T cells shows a significant positive correlation with peanut-specific and Ara h 2-specific IgE levels at baseline. Higher frequencies of IL-4⁺ and IFNγ⁺ CD4⁺ T cells post-OIT are negatively correlated with SU. Our findings provide evidence that an immune signature consisting of certain CD8⁺ T cell subset frequencies is potentially predictive of SU following OIT.

Full text

Article https://doi.org/10.1038/s41467-022-34222-8
CD8+T cell differentiation status correlates
with the feasibility of sustained unrespon-
siveness following oral immunotherapy
Abhinav Kaushik
1
, Diane Dunham
1
,XiaoruiHan
1
,EvanDo
1
, Sandra Andorf
1,2,3
,
Sheena Gupta
4
, Andrea Fernandes
1
,LaurieElizabethKost
1
,
Sayantani B. Sindher
1
, Wong Yu
1,5
,MindyTsai
1,6
, Robert Tibshirani
7
,
Scott D. Boyd
1,6
,ManishaDesai
7
,HoldenT.Maecker
4,8
,StephenJ.Galli
1,6,8
,
R. Sharon Chinthrajah
1
,RosemarieH.DeKruyff
1,9
, Monali Manohar
1,9
&
Kari C. Nadeau
1,9
While food allergy oral immunotherapy (OIT) can provide safe and effective
desensitization (DS), the immune mechanisms underlying development of
sustained unresponsiveness (SU) following a period of avoidance are largely
unknown. Here, we compare high dimensional phenotypes of innate and
adaptive immune cell subsets of participants in a previously reported, phase 2
randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with
allergic reactions upon food challenge after a withdrawal period; n=21vs.30
respectively among total 120 intent-to-treat participants). Lower frequencies
of naïve CD8+T cells and terminally differentiated CD57+CD8+Tcellsubsetsat
baseline (pre-OIT) are associated with SU. Frequency of naïve CD8+Tcells
shows a significant positive correlation with peanut-specificandArah
2-specific IgE levels at baseline. Higher frequencies of IL-4+and IFNγ+CD4+
T cells post-OIT are negatively correlated with SU. Our findings provide evi-
dence that an immune signature consisting of certain CD8+Tcellsubsetfre-
quencies is potentially predictive of SU following OIT.
Oral immunotherapy (OIT), during which food allergens are gradually
introduced at increasing doses until a maintenance dose is reached, has
been successful in desensitizing patients to offending food antigens
and many trials administering peanut OIT have been performed1–3.
While these trials have demonstrated the efficacy of peanut OIT to
desensitize participants to peanut, the proportion of such desensitized
participants achieving sustained unresponsiveness (SU), i.e., a lack of
clinical reactivity following a period of OIT discontinuation is highly
variable1–3. Moreover, while some aspects of the immune mechanisms
resulting in desensitization (i.e., DS, for the purposes of this study,
defined by the recurrence of any allergic reaction upon food challenge
after a period of withdrawal post-successful OIT outcome) have been
largely elucidated, the immune pathways or biomarkers that may dis-
tinguish SU vs. DS are yet to be fully characterized4–7.
To address this issue and to gain insight into mechanisms
underlying development of SU, we performed a comprehensive post-
Received: 2 November 2021
Accepted: 19 October 2022
Check for updates
1
Sean N. Parker Center for Allergy and Asthma Research, Department of Medicine, Stanford University School ofMedicine, Stanford, CA,USA.
2
Department of
Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
3
Divisions of Biomedical Informatics and Allergy & Immunology, Cincinnati
Children’s Hospital Medical Center,Cincinnati, OH,USA.
4
Human Immune Monitoring Center, Institute for Immunity, Transplantation, and Infection, Stanford
University School of Medicine, Stanford, CA, USA.
5
Division of Allergy, Immunology, and Blood and Marrow Transplantation, Department of Pediatrics,
University of California, San Francisco, CA, USA.
6
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
7
Department of
Biomedical Data Science, Stanford University, Stanford, CA, USA.
8
Department of Microbiology and Immunology, Stanford University School of Medicine,
Stanford, CA, USA.
9
These authors contributed equally: Rosemarie H. DeKruyff, Monali Manohar, Kari C. Nadeau. e-mail: knadeau@stanford.edu
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hoc mechanistic analysis of peripheral blood mononuclear cells
(PBMCs) and plasma from participants in our phase 2 OIT POISED
study (Peanut Oral Immunotherapy: Safety, Efficacy, Discovery) (NCT
02103270). The design, objectives, and primary outcomes of this
randomized, double-blind, placebo-controlled (DBPC) clinical trial
have been previously published8. Briefly, in this study, after dosage
build-up over ~52 weeks, peanut-allergic patients were maintained on
4000 mg peanut protein daily for another 52 weeks, while a blinded
placebo group received oat flour. At week 104, 80 (98.77%) of per-
protocol active participants passed the DBPC food challenge
(DBPCFC) and peanut ingestionwas withdrawnin a group of 51 blinded
participants (i.e., peanut avoidance group) for 12 weeks. At week 117, 21
(41.2%) of per-protocol participants in the peanut avoidance group
passed the 4000mg DBPCFC without any allergic reaction, thus
demonstrating SU. Those 21 participants continued to have DBPCFCs
every 3 months and were allowed to continue peanut OIT dis-
continuation if they passed a DBPCFC. At week 156, 12 months after
peanut discontinuation, only 8 participants passed the DBPCFCs (i.e.,
had achieved long-term SU with no allergic reaction upon food
challenge).
We recently reported higher frequencies of peanut-reactive (pr)
CD8+T cells in peanut-allergic individuals and demonstrated interac-
tions between pr CD8+and Th2 cells leading to enhancement of
allergic pathways9,10. Based on these findings, we postulated a pr CD8+
T cell-based mechanism in distinguishing SU vs. DS outcomes. Fur-
thermore, our group and others have demonstrated distinct functions
of innate immune cells in food allergy11,12, and so we further hypothe-
sized that pr CD8+T cells along with innate immune cell subsets likely
impact the possibility achieving SU.
To test thesehypotheses,we perform high dimensional functional
immunophenotyping of peanut-stimulated, PMA/Ionomycin-stimu-
lated, and unstimulated PBMCs with mass cytometry, and plasma and
peanut-stimulated PBMC supernatants with Luminex technology from
all per-protocol trialparticipants. We quantitate immune changes from
SU (i.e., SU defined as those who had no allergic reaction at the
DBPCFC at week 117 or later) vs. DS (i.e., those who had allergic reac-
tions to the DBPCFC at week 117) at week 117. To identify potential
biomarkers predictive of SU, we examine differences in immune pro-
files of SU vs. DS participants at baseline (BL, i.e., pre-OIT) as well as at
the end of OIT at week 104 (i.e., after successful desensitization). One
of the primary baseline inclusion criterion for study participation was
tolerance to ≤500 mg of cumulative peanut protein dose during the
DBPCFC at the screening stage. The participants who successfully
passed 4000 mg peanut OFC at week 104, but failed at a peanut dose
of ≤500 mg at week 117 represented a subset having relapsed to the
baseline threshold following mere 12 weeks of avoidance. Thus, as a
nested analysis of participants at the ends of SU vs. DS outcome
spectrum, we compare a subset of DS participants, who failed week 117
DBPCFC at a peanut protein dose ≤500 mg (n= 7) with the 8 SU par-
ticipants,whopassedallDBPCFCsuptoweek156.Wealsoexamine
peanut OIT-induced immune changes by comparing active vs. placebo
groups at week 104.
Our results show that lower frequencies of naive CD8+T cells and
terminally differentiated, CD57+pr CD8+T cells at BL are strongly
associated with SU. On the other hand, higher frequencies of IL-4+and
IFNγ+CD4+T cells post-OIT are negatively correlated with SU. In
summary, we discover a distinct CD8+T cell pattern in vivo that is
associated with the impairment of achieving SU during food allergy
OIT. In addition, our data confirm the attenuation of Th2 phenotype
post-OIT.
Results
Peanut OIT leads to attenuation of Type 2 phenotype
Figure 1a depicts the POISED study design (top panel) and the work-
flow for mechanistic assays (bottom panel). High dimensional
intracellular and extracellular proteins in peanut-stimulated PBMCs
from participants on active peanut OIT vs. placebo were quantified by
mass cytometry at week 104.
The frequencies of IL-4+,IL-9
+and IL-10+peanut-reactive (pr) CD4+
T cells (i.e., CD69+CD40L+CD4+T cells) among total CD4+Tcellswere
significantly reduced in participants on active OIT (Fig. 1b; q= 0.002,
0.09 and 0.04, respectively). Peanut-reactive CD4+cells also demon-
strated a reduced frequency and expression of Th2-polarization sur-
rogate marker OX40 in active vs. placebo groups (Fig. 1b; q=0.01and
0.03, respectively), indicating downregulation of the Type 2 pheno-
type in the active peanut OIT-treated, desensitized participants.
Peanut-reactive CD4+T cells also demonstrated increased frequencies
of subpopulations expressing the costimulatory molecule CD27 and
the activation marker CD38 in active vs. placebo (Fig. 1b; q=0.046and
0.092, respectively). Similar differences in OX40 and CD27 expression
were observed inactive vs. placebo comparison of pr CD4+cell clusters
identified through FlowSOM-based unsupervised clustering (Supple-
mentary Fig. 1a, b; q= 0.01 and 0.006, respectively). Expression of
other functional markers evaluated in pr CD4+T cells including IL-17,
LAP (TGF-β)andIFN-γand frequencies of pr CD4+and pr CD8+Tcells
among peanut-stimulated PBMCs were similar in active vs. placebo
comparison at week 104 (Supplementary Fig. 1c; q> 0.1). Also, fre-
quencies of all the manually gated immune subpopulations examined
within the scope of our mass cytometry panel including Treg
(CD25hiCD127-CD4+), NKT (CD56+CD3+αβ T), naive γδ (CD45RA+
CCR7+γδTCR+CD3+) T cells, and plasmablasts (CD27hiCD38hiHLA-
DR+CD19+CD3-) among peanut-stimulated and unstimulated PBMCs
were comparable across active and placebo participants at week 104
(Supplementary Fig. 1d; q>0.1).
Analysis of supernatants of peanut-stimulated PBMCs by Luminex
assay showed that expression of the Type 2 cytokines IL-4, IL-5, IL-9
and IL-13 was significantly decreased at week 104 in active vs. placebo
(q= 0.09, 0.006, 0.005 and 0.006, respectively) (Fig. 1c, d). This
marked decrease in IL-4, IL-5, IL-9 and IL-13 was also evident when
comparing their levels in peanut-stimulated culture supernatants of
active participants’PBMCs at week 104 with those at BL (q= 0.05, 0.09,
0.09 and 0.09, respectively) (Fig. 1e, f), further supporting a shift away
from a Type 2 profile. Apart from Type 2 cytokine changes, peanut-
stimulated PBMC culture supernatant from active participants showed
an increase in Monocyte Chemoattractant Proteins MCP1 and MCP3
(active vs. placebo, q= 0.02 and 0.01, respectively), as well asIL-12 p70
and FLT3 Ligand (q= 0.03 and 0.07, respectively) compared with
placebo (Supplementary Fig. 1e). In contrast, peanut-stimulated PBMC
culture supernatants from active participants showed a marked
increase in the eosinophil chemotactic protein Eotaxin at week 104 vs.
BL (q= 0.05), and a concomitant decrease in proinflammatory cyto-
kines including RANTES, TNF, and IL-22 (Supplementary Fig. 1f;
q= 0.098, 0.065 and 0.059, respectively).
CD8+T cell subset distinctions as a hallmark of SU vs. DS
outcome
While all 51 per-protocol active participants randomized into the
avoidance group were successfully desensitized at week 104, 12 weeks
of peanut avoidance thereafter led to two distinct clinical outcomes,
viz., SU (n=21) vs. DS (n= 30) (no vs. with allergic reactions on the
4000 mg DBPCFC at week 117, respectively). To identify changes in
immune markers or cell type frequencies that might identify SU par-
ticipants, we compared immune changes in peanut-stimulated and
unstimulated PBMCs from SU participants with those from DS at BL,
week 104, and week 117. Interestingly, in both peanut-stimulated and
unstimulated PBMCs, differences distinguishing SU vs. DS were iden-
tified within CD8+T cell subsets but not in CD4+T cells. Peanut-rea ctive
(pr) CD8+T cells (i.e., CD69+CD8+) among peanut-stimulated PBMCs of
SU participants were primarily of effector memory (EM; CD45RA-CCR7-
) phenotype while those from DS participants exhibited a naive
Article https://doi.org/10.1038/s41467-022-34222-8
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(CD45RA+CCR7+) phenotype (Fig. 2a). Also, pr CD8+TcellsfromDS
participants had a significantly higher frequency and expression of
CD57 (q=0.079 and 0.079, respectively), a marker of terminal differ-
entiation (Fig. 2a). Similarly, EM and TEMRA (CD45RA+CCR7) CD8+T
cell subsets of peanut non-reactive (i.e., CD69-)CD8
+T cells among
peanut-stimulated PBMCs from DS participants showed a significantly
higher frequency and expression of CD57 (Fig. 2b; q= 0.019, 0.05, 0.02
and 0.02, respectively). This CD57 expression pattern among EM and
TEMRA CD8+cells was evident in unstimulated PBMCs as well (Fig. 2c;
SU vs. DS q= 0.05, 0.009, 0.03 and 0.01, respectively). Interestingly,
CD56dimCD16+NK cells, a subset involved in host protective immunity
throughcytolysisalongwithCD8
+T cells, from DS participants also
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showed an elevated frequency and expression of CD57 at BL and at
week 117 in peanut-stimulated (Supplementary Fig. 2a; q= 0.059 and
0.068, respectively) and unstimulated PBMCs (Supplementary Fig. 2a;
q= 0.02 and 0.01, respectively).
In addition, among peanut non-reactive CD8+T cells in peanut-
stimulated PBMCs, the frequency of naive CD8+cells was substantially
lower in SU vs. DS participants at BL, week 104 and week 117 (Fig. 2d;
q= 0.02, 0.01 and 0.04, respectively). Unstimulated SU vs. DS BL, week
104 and week 117 PBMCs also reflected these differences in naive CD8+
T cell frequency (Fig. 2e; q= 0.02, 0.008 and 0.03, respectively). A
significantly reduced frequency of CD8+naive T cells among total live
cells was also detected in SU vs. DS participants at BL, week 104 and
week 117 using unsupervised clustering of peanut-stimulated PBMCs
(q= 0.09, 0.009 and 0.09, respectively), and unstimulated PBMCs
(q= 0.09, 0.01 and 0.06, respectively) (Supplementary Fig. 2b–d). On
the contrary, SU participants showed a markedly higher frequency of
peanut non-reactive EM CD8+cells among peanut-stimulated and
ustimulated total CD8+cells at week 104, and week 117 PBMCs com-
pared to DS participants (Supplementary Fig. 2e; q= 0.02, 0.03
respectively, and Supplementary Fig. 2f q= 0.025 each). This trend of
higher EM CD8+T cell frequency could be observed at BL as well.
Frequencies of pr CD4+and pr CD8+T cells among peanut-
stimulated PBMCs and expression of the evaluated functional markers
within these subsets were similar between SU and DS participants at
BL, week 104, and week 117 (Supplementary Fig. 3a–h; q>0.1). Parti-
cularly, while there were no significant differences in the respective
frequencies and no significant differences in the expression of IL-4, IL-
9, and IL-10 between SU vs. DS participants at week 117, SU participants
had lower frequencies of IL-4+and IL-9+pr CD4+T cells and lower
expression of IL-4 compared to DS participants at BL, week 104,as well
as week 117 (Supplementary Fig. 3b–d). The frequencies of all the
manually gated immune subpopulations examined within the scope of
our mass cytometry panel including Treg, NKT, naive γδ T cells and
plasmablasts among peanut-stimulated and unstimulated PBMCs were
comparable across SU and DS participants at BL, week 104 and week
117 (Supplementary Fig. 3i–l; q>0.1).
Eligible participants further avoided peanut consumption past
week 117 in a blinded protocol, and eight participants in this group
successfully tolerated a 4000 mg peanut DBPCFC at week 156, thereby
having achieved “long-term”SU (henceforth termed, SU week 156)
following 1 year of peanut avoidance (Supplementary Fig. 4a). Immune
changes in these SU week 156 participants were compared with those
of participants who failed week 117 DBPCFC at a dose lower than
500 mg peanut (termed DS ≤500 mg; n= 7). Such subset selection
afforded immune evaluation of participants representing each end of
the treatment outcome spectrum. The frequency of IL-9+pr CD4+
T cells at week 104 and expression of IL-9 among pr CD4+T cells at
week 117 were significantly lower in the SU week 156 vs. DS ≤500 mg
(Supplementary Fig. 4b; q= 0.03 and 0.05, respectively). While the
CD57 expression and frequency among CD8 subsets did not sig-
nificantly differ in SU week 156 vs . DS ≤500mg, peanut-stimulated and
unstimulated week 117 PBMCs from SU week 156 exhibited a lower
frequency of CD57+CD56dimCD16+NK cells than DS≤500 mg
(Supplementary Fig. 4c; q= 0.05 and 0.06, respectively). The fre-
quency of naive CD8+T cells in total live peanut-stimulated and
unstimulated PBMCs from SU week 156 participants was substantially
lower at week 104 (Supplementary Fig. 4d, e; q= 0.002 and 0.003,
respectively) and week 117 (Supplementary Fig. 4d, e; q= 0.002 and
0.006, respectively) compared with those in the DS ≤500 mg group.
Notably, on comparing the frequencies of naive CD8+TcellsinSU
week 156 (n= 8), DS week 130 to 156 (i.e., per-protocol participants,
who passed at week 117, but failed at week 130/143/156; n=10), DS
week 117 > 500 mg (n= 23), and DS week 117 ≤500 mg (n=7) at BL,
week 104, and week 117, a trend of gradual increase in the frequency of
naive CD8+cells going from SU week 156 to DS week 117 ≤500 mg was
observed (Supplementary Fig. 4f, g).
DifferencesinCD86expressiononAPCsubsetsfromSUvs.DS
participants
Besides CD8+T cell subsets, we found significant differences in CD86
expression on APC subsets in SU vs. DS. The frequencies of CD86
expressing memory B (CD27+CD38+HLA-DR+CD19+CD3-) cells among
peanut-stimulated PBMCs (Fig. 2f; q= 0.039) and that among classical
monocytes (CD14+CD16-HLA-DR+) and myeloid dendritic cells 2
(mDC2; CD11c+CD123-CD14-HLA-DR+) among unstimulated PBMCs
from SU were markedly lower than DS at BL (Fig. 2g; q=0.05and0.03
respectively). Intermediate monocytes (CD14+CD16+HLA-DR+)and
myeloid dendritic cells 1 (mDC1; CD11c+CD123+CD14-HLA-DR+)among
unstimulated PBMCs from SU participants also showed a lower
expression of CD86 (Fig. 2g; q= 0.03 and 0.01, respectively). In addi-
tion, a singular Luminex readout distinctive of SU vs. DS was lower
levels of IL-2 in peanut-stimulated PBMC culture supernatants at BL
(Fig. 2h; q=0.056).
Similarly, reduced CD86+frequency or CD86 expression was seen
in APC subsets among PBMCs from SU week 156 group compared to
the DS ≤500mg group of participants. The frequency of CD86+clas-
sical monocytes and memory B cells among peanut-stimulated week
104 PBMCs, and frequency of CD86+classicalmonocytes, intermediate
monocytes and mDC1s among unstimulated BL PBMCs, was sig-
nificantly lower in SU week 156 vs. DS ≤500 mg (Supplementary
Fig. 4h, i; q= 0.067, 0.080, and 0.007, 0.06 and 0.02, respectively).
Classical monocytes, mDC1, and mDC2 subsets at BL from SU week 156
participants also showed lower expression of CD86 than DS ≤500 mg
participants (Supplementary Fig. 4i; q= 0.03, 0.02 and 0.02,
respectively).
Correlations between distinctive immune subsets and specific
immunoglobulin readouts
We and others have shown that an increased peanut-specificand
peanut component-specific IgG4/IgE ratio driven by increased
allergen-specific IgG4 is associated with induction of SU8,13,14. Analyses
were thus performed to probe correlations of peanut- or peanut
component-specific IgE, IgG4 and IgG4/IgE antibody levels at BL with
immune cell subset frequencies, which we had observed to be asso-
ciated with SU in this study. We observed a positive correlation of the
frequency of naive CD8+T cells in peanut-stimulated PBMCs with
Fig. 1 | Peanut OIT effects an immune shift away from Th2 phenotype. a Top
panel: schematic representation of POISED study timeline and sample collection
time points. Bottom panel: depiction of approach for mechanistic analyses. Here,
“SU”stands for sustained unresponsive and “DS”stands for desensitized alone
participants. Illustration created with BioRender.com. bImmune cell subsets
among peanut-stimulated week104 PBMCs from participants on active peanut OIT
(n= 80) were compared against those treated with placebo (n=15).Significant
frequency (i.e., percentage) and expression (i.e., MSI) changes among peanut-
reactive (pr) CD4+T cells are shown. c,dSignificant changes in peanut-stimulated
week 104 PBMC culture supernatant of active participants (n= 16) compared to
those treated with placebo (n=7)as a heatmap (c) and as individual boxplots (d).
e,fSignificant changes in peanut-stimulatedweek 104 PBMC culture supernatantof
activeparticipants(n= 13) comparedto BL as a heatmap (e)and individualboxplots
(f). In the boxplots, medians are shown, and the “hinges”represent the first and
third quartile. The whiskers are the smallest and largest values after exclusion of
outliers (greater than the 75th percentile plus 1.5 times the interquartile range
(IQR),or less than 25th percentileminus 1.5 times theIQR). qvalues were comp uted
using the χ2tests in mixed-effects models, adjusted for multiple hypothesistesting
using the Benjamini and Hochberg method. Source data are provided as a Source
Data file.
Article https://doi.org/10.1038/s41467-022-34222-8
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peanut-specific IgE (Fig. 3a; p=0.43, p=0.002)aswellasArah
2-specific IgE (Fig. 3a; p=0.47, p< 0.001) levels at BL. Such positive
correlation with peanut- and Ara h 2-specific IgE was also observed
with naive CD8+T cells among unstimulated PBMCs (Fig. 3a; p=0.48
and 0.53 respectively; p< 0.001 for both). Notably, participants who
demonstrated SU at week 117 had both significantly lower levels of
peanut-specific IgE at BL and significantly lower numbers of naive CD8+
T cells at BL (Fig. 2a) than participants that were DS (Fig. 3a). On the
other hand, the BL frequency of EM CD8+cells among peanut-
stimulated total CD8+cells exhibited a significant negative correlation
with BL peanut-specific IgE (Supplementary Fig. 5a; p=−0.43,
p= 0.0016). Similarly, a significant negative correlation was observed
Fig. 2 | CD8+T celldifferentiation statusdistinguishes SU participants from DS.
Immune cellsubsets among peanut-stimulated (a,b,d,f) and unstimulated (c,e,g)
PBMCs from SU participants were compared with DS participants at BL, week 104,
and week 117. a–cSignificantfrequency and CD57 expressiondifferences amongpr
CD8+Tcells(a), and peanut non-reactive CD8+Tcells(b) among peanut-stimulated
(SU, n=20;DS,n= 29), and unstimulated (c)(SU,n= 20; DS, n= 26), BL PBMCs.
d,eSignificant differences in the naive CD8+T cell frequency in peanut-stimulated
(d) and unstimulated (e) PBMCs at BL (pea stim: SU, n=20,DS,n= 29; unstim: SU,
n=20,DS,n=26), week 104 (pea stim:SU, n= 21, DS, n= 30; unstim: SU,n= 20, DS,
n= 29), and week 117 (pea stim:SU, n= 17, DS, n= 27; unstim: SU, n=17,DS,n=27).
f,gSignificant differences in the frequency or expression of CD86 in memory B
cells among peanut-stimulated PBMCs (f) and APC subsets among unstimulated
PBMCs (g) at BL (pea stim: SU, n=20,DS,n=29;unstim:SU,n=20,DS,n=26).hIL-
2 levels in peanut-stimulated BL PBMC culture supernatants from SU (n=7)vs.DS
(n= 7) participants. Boxplots show medians and “hinges”represent the first and
third quartile. Whiskers denote the smallest and largest values after outlier exclu-
sion. qvalues computed using the χ2tests in mixed-effects models, adjusted for
multiple hypothesis testing using the Benjamini and Hochberg method. Source
data are provided as a Source Data file.
Article https://doi.org/10.1038/s41467-022-34222-8
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between the frequency of EM CD8+cells among peanut-stimulated
total CD8+cells and Ara h 2-specific IgE at BL (Supplementary Fig. 5b;
p=−0.46, p= 0.0008). To further dissect this intriguing link between
relative frequencies of naive vs. EM CD8+T cell subsets with serological
readouts and hence SU as an outcome, we probed a convenience
sample set of week 52 PBMCs from 5 SU vs. DS participants each.
Interestingly, unstimulated and PMA/Ionomycin (PMAi)-stimulated
CD8+cells from SU participants exhibited a significantly higher fre-
quency of the key cytolytic effector molecule, Granzyme B (GzB)-
expressing subset compared to DS participants. A significantly higher
GzB expression was observed in PMAi-stimulated CD8+cells from SU
participants as well compared to DS participants (Supplementary
Fig. 5c; p< 0.01). Overall, EM CD8+followed by TEMRA CD8+Tcells
primarily contributed to GzB production. Nevertheless, DS partici-
pants had a lower mean proportion of GzB+EM CD8+cells (43.28% vs.
54.14%) and a higher mean proportion of GzB+naive CD8+cells (18.46
vs. 9.44%) compared to SU participants (Supplementary Fig. 5d).
As expected, we found that the frequency of IL-4+peanut-
reactive CD4+T cells was positively correlated with the level of Ara h
2-specific IgE (Fig. 3b; p= 0.44, p= 0.002) and negatively correlated
with the ratio of Ara h 2-specific IgG4/IgE at BL (Fig. 3b; p=−0.45,
p= 0.002). Those participants who developed SU displayed lower
values of Ara h 2-specific IgE and higher ratios of Ara h 2-specific
IgG4/IgE1. Also, the frequency of CD57+pr CD8+T cells at BL nega-
tively correlated with peanut and Ara h 2-specific IgG4/IgE levels
(Fig. 3c; p=−0.41, p= 0.005).
Supervised learning-based identification of features predictive
of SU vs. DS outcome
As an independent approach to validate the above-mentioned obser-
vations, we applied supervised learning on the BL CyTOF readouts to
identify features that could potentially predict the outcome as SU vs.
DS (Supplementary Fig. 6a, b). We thus simulated the frequencies and
marker expressions of manually gated immune cell subsets in all the
participantsstudied (300 times data simulation). Each of thesimulated
dataset was then used to build an elastic net machine learning (ML)
model, from which the most important features were elucidated (see
Methods). Here, the data simulation not only enabled probing of the
smallsamplesize(n= 46 at BL), but also facilitated capturing the
important features conserved in the respective group even after con-
stricted random sampling15. The elastic net model could correctly
predict the SU participants with anaverage AUC of 0.94. In agreement
with CyTOF analyses, the most informative features included fre-
quencies of naive CD8+T cells and CD57+TEMRA CD8+Tcellsthus
endorsing the current findings.
Lower frequencies of certain IFNγ+T cell subsets post-OIT in SU
participants
We further probed immune differences in SU vs. DS participants by
non-specifically stimulating PBMCs with PMAi. Lower frequencies of
IL-4+CD4+memory T effector (CD45RA-CD127+CD25lo)cellswere
observed in SU compared with DS participants at week 104, which was
consistent with a reduced Type 2 response (Fig. 4a; q= 0.02). Inter-
estingly, lower frequencies of IFNγ+CD4+memory T effector cells at
weeks 104 and 117 (Fig. 4a; q=0.07 and 0.04 respectively), and lower
expression levels of IFNγby CD4+memory T effector cells (Fig. 4a;
q=0.06)andmemoryγδ Tcells(Fig.4b; q= 0.044) were observed in
SU vs. DS participants at week 104. Unsupervised clustering analysis
also corroborated lower expression levels of IFNγby CD4+memory T
effector cells of SU participants compared to DS at week 117 (Supple-
mentary Fig. 7a; q= 0.003). Moreover, memory and naive γδ T cells in
unstimulated PBMCs displayed lower expression levels of IFNγ-
inducible chemokine receptor CXCR3 at week 104 from SU vs. DS
participants (Fig. 4b). Concomitantly, significantly lower levels of MIG
(CXCL9), an IFN-γ-induced ligand for CXCR3, were also observed in SU
compared with DS participants’plasma at week 117 (Fig. 4c). Thus,
lower expression of IFN-γin memory γδ T cells was consistent with the
lower expression of CXCR3 and reduced levels of CXCL9.
Since CD8+T cells and IFN-γhave been implicated as contributing
to the development of asthma16, we asked if the age ofwheezing onset,
a prelude to clinical asthma diagnosis by spirometry at the age of 7
years or older, differed in the participants that developed SU com-
pared with DS participants. Notably, most DS participants reported
wheezing onset at a significantly lower age than SU (Fig. 4d; p=0.04).
Age at onset of other atopic comorbidities, such as atopic dermatitis
and allergic rhinitis, if present, as well as age at diagnosis of peanut
allergy were comparable among SU vs. DS participants (Supplemen-
tary Fig. 7b; p= 0.51, 1 and 0.80, respectively).
Fig. 3 | Naive CD8+,IL-4
+pr CD4+,andCD57
+pr CD8+T cell subsets at baseline
significantly correlate with peanut and Ara h 2-specific immunoglobulin
readouts. a Spearman correlations of frequency of naive CD8+T cells among total
live cells from peanut-stimulated(top panels) andunstimulated(bottom panels)BL
PBMCs with peanut-specific, and Ara h 2-specificIgE.bSpearman correlations of
frequency of peanut-reactive IL-4+CD4+cellsamong total pr CD4+T cells in peanut-
stimulated PBMCs with Ara h 2-specific IgE and Ara h 2-specific IgG4/IgEratio at BL.
cSpearman correlation of frequency of peanut-reactive CD57+CD8+cells within
total pr CD8+T cells in peanut-stimulatedPBMCs with Ara h 2-specificIgG4/IgE ratio
at BL (pea stim: SU, n=20, DS, n=29; unstim:SU,n=20,DS, n= 26). Data are
presented as the mean ± SE. pvalues in the figure were determined by an unpaired
two-tailed Student’sttest. Plots display the correlation coefficient (ρ)andlinear
regression line with a 95% confidence interval shading. Sourcedata are provided as
a Source Data file.
Article https://doi.org/10.1038/s41467-022-34222-8
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Discussion
Our overarching aim for this study was to understand those T-cell
changes that might elucidate why SU develops only in a subset of
desensitized participants following a period of allergen withdrawal.
Using agnostic computer-based learning algorithms as well as
hypothesis-based analyses from high dimensionalimmune studies, our
findings suggest a pivotal function of CD8+T cell differentiation status
in defining the likelihood of SU. Our findings suggest that SU is favored
by: (i) a lower frequency of terminally differentiated, CD57+allergen-
reactive, and allergen non-reactive CD8+T cellspossibly coupled with a
higher frequency of cytotoxic EM CD8+T cells, (ii) a lower frequency of
naive CD8+T cells, (iii) a lower expression of CD86 on APCs and/or a
lower frequency of CD86+APCs pre-OIT, and (iv) a lower frequency of
IFN-γ+CD4+memory T effectors post-OIT.
While many clinical studies have confirmed the efficacy of peanut
OIT in conferring desensitization, only a handful have tested the
induction of SU during the post-avoidance period. Moreover, the
immune mechanistic studies performed during a subset of such SU
testing trials primarily have been focused on serological readouts (i.e.,
peanut-specific IgE and IgG4), basophil activation, measurement of
Th2 cytokines, and changes in the frequency of total and peanut-
reactive CD4+Tregs and Teffectors14,17–20. A recent study examining
longitudinal transcriptional changes and clonality of peanut-reactive
Th cell subsets from peanut OIT participants offers further refined
Fig. 4 | Higher frequency and expression of IFNγas an additional distinctive
featureof DS vs. SU participants.a Frequency of IL-4+CD4+memoryT effectorsat
week 104, frequenciesof IFNγ+CD4+memory T effectors at week104 and week 117,
and expression of IFNγin CD4+memory T effectors at week 104 in PMAi-stimulated
PBMCs from SU vs. DS [week 104 (PMAi stim:SU, n= 21, DS, n= 29); week 117 (PMAi
stim: SU, n= 17, DS, n=27)].bExpression ofIFNγin memory γδ TcellsamongPMAi-
stimulated week 104 PBMCs from SU vs. DS; expression of CXCR3 in memory and
naive γδ T cells amongunstimulated PBMCsat week 104 [week 104 (PMAi stim: SU,
n=21,DS, n= 29; unsti m: SU, n= 20, DS, n= 29); week 117 (PMAi stim and unstim:
SU, n= 17, DS, n= 27)]. cPlasma CXCL9 level compared between SU (n=8)andDS
(n= 13) at week 117. In the boxplots, medians are shown,and the “hinges”represent
the first and third quartile. The whiskers are the smallest and largest values after
exclusion of outliers (greater than the 75th percentile plus 1.5 times the inter-
quartile range (IQR), or less than 25th percentile minus 1.5 times the IQR). qvalues
were computed using the χ2tests in mixed-effects models, adjusted for multiple
hypothesis testing using the Benjamini and Hochberg method. dAge of wheezing
onset for SU participants with asthma (n= 11) was compared with that for DS par-
ticipants (n=15).pvalue wascomputed using unpaired,two-sided, non-parametric
Mann–Whitney test. Source data are provided as a Source Data file.
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insights into OIT-induced changes and features predictive of post-OIT
outcome in the context of CD4+T cells21. However, a comprehensive
evaluation of circulating immune cell frequencies and functional
markers, and the cumulative potential impact of any significant
changes distinguishing SU in a given OIT cohort, has not been pre-
viously performed. Our analyses put forth such an evaluation by
employing high dimensional, multi-parametric tools such as mass
cytometry and Luminex. Our study thus provides unique insights into
immune players in the induction of OIT-induced tolerance.
Notably, we observed significant differences beyond Th2 and Treg
subsets while comparing immunophenotypes of SU vs. DS participants
within our CyTOF dataset. In agreement with previous reports,
attenuation of Th2 phenotype was observed exclusively among active
participants post-OIT compared to placebo-treated participants, while
no change was noted in the frequencies of pr CD4+T cells and Tregs
post-OIT17,21. Interestingly, in addition to prototypical Th2 cytokines,
the frequency of a regulatory cytokine- IL-10+pr CD4+T cells was seen
to be diminished in active participants post-OIT. This might be
attributed to an initialautocrine IL-10 induction in pathogenic pr CD4+
T cells in response to peanut dose build-up followed by a marked
decrease by week 104 post-OIT22. However, CD4+T cell subset read-
outs did not significantly distinguish SU vs. DS in our study or those
by others14,17. Instead, higher frequencies of naive CD8+and lower
frequencies of EM CD8+T cells, at BL and throughout the course of
OIT, identified those individuals with less likelihood of becoming SU.
Our preliminary data demonstrate that CD8+T cells from SU parti-
cipants have higher GzB-mediated cytolytic potential. Of note, GzB
has been shown to be critical for TCR-induced death of Th2 cells23,24
thus raising a possibility of EM CD8+cell sourced-GzB conferring SU
participants a selective advantage by regulating pathogenic peanut-
reactive Th2 proliferation, and thus peanut-specific IgE during
avoidance. The significant negative correlation observed between
the BL frequency of EM CD8+cells and peanut-specific as well as Ara h
2-specific IgE reinforces this possibility. Investigations are underway
to evaluate how GzB from EM CD8+cells may preferentially target
immune cell subsets in the IgE pathway, and influence of CD57
expression on GzB function in this context. The higher frequency of
naive CD8+T cells, despite higher expression of CD86 on APC subsets
and a higher level of IL-2 in peanut-stimulated PBMC culture super-
natants, observed in DS compared to SU participants at BL likely
implies a higher threshold of activation. Of note, our group has
recently reported hypomethylation of the IL-2 locus, and conse-
quently higher levels of IL-2, in peanut-stimulated PBMC culture
supernatants as one epigenetic readout distinguishing peanut-
allergic twins from their non-allergic counterparts25.
Concomitantly, a higher frequency and expression of CD57 on EM
and TEMRA CD8+T cells in DS participants suggests terminal differ-
entiation and immunosenescence. Early childhood viral exposure,
chronic in vivo inflammation, and natural aging may drive such term-
inal differentiation. The speculation regarding early childhood viral
exposure also isconsistent with the significantly lower age of wheezing
onset we diagnosed for DS participants (Fig. 4d).
CD57 and CD27 have reciprocal expression patterns in T cell
ontology26. Interestingly, terminally differentiated CD27-
CRTH2+CD161+memory allergen-reactive CD4+Tcells,termedTh2A
cells, have been strongly associated with allergies27.Despitebeing
frequently associated with senescence, CD57+CD8+T cells subsets
have been reported to be highly proinflammatory26. We also observed
a higher frequency of IFNγ+CD4+T effectors amon g post-OIT PBMCs of
DS participants on PMA/Ionomycin stimulation. Thus, taken together,
preexistence of terminally differentiated, proinflammatory CD8+and
CD4+subsets compounded with a high-activation-threshold, “reluc-
tant-to-differentiate”CD8+T cells in DS participants, likely pose
immune plasticity challenges. These challenges might be mitigated
during active dosing of OIT leading to successful desensitization,
although they can resurface post-avoidance, and are reflected through
clinical relapse/reactivity to allergens.
There are limitations to our study. Firstly, not all immune subsets
of interest such as Tfh subsets21,28,Th2a
27 cells could be monitored
through our CyTOF panel. Secondly, a thorough longitudinal analysis
inclusive of PBMCs and plasma from additional time points over the
course of OIT was not feasible due to limited sample availability. In
addition, unlike manual gating, FlowSOM-based unsupervised clus-
tering analysis could not identify certain rare immune cell subsets as
clusters, particularly pr CD8+T cells. Semi-supervised cell type anno-
tation algorithm- CyAnno29 developed by our group will likely yield
a better output in this regard. In addition, the ML models used to
identify features for classification of SU vs. DS groups were based on
small sample size of training and testing datasets, which may have led
to model over-fitting, and would be a major limitation of models
proposed in this study and features identified. To address this issue, we
simulated the real dataset 300 times and built elastic net models for
each of them. Here, the elastic netmodel was appliedbecause it uses L1
and L2 regularization penalties for model building and the algorithm
has been proven to be useful for similar cases where the number of
features are larger than the number of samples. To further minimize
the effect of model over-fitting, we only prioritized the SU vs. DS
classification features that are conserved across 300 elastic net model.
The current ML model based findings nonetheless warrant validation
using a similar, independent dataset of food OIT study participants
tested for SU.
Higher levels of allergen- or allergen-component-specificIgEpre-
OIT have been reported in the past to predict a negative outcome
towards SU13,14. Frequency of naive CD8+T cells positively correlated
with peanut and Ara h 2-specific IgE, while abundance of CD57+pr CD8+
T cells negatively correlated with the Ara h 2-specific IgG/IgE ratio at
BL. Thus, the CD8+T cell differentiation status, along with the fre-
quency of IL-4+and IL-9+pr CD4+T cells, may be considered as
potential biomarkers predictive of SU following OIT. Prospective
peanut-allergic participants presenting with such skewed CD8+Tcell
differentiation may benefit from continued peanut dosing. In addition,
adjunctive treatment with anti-IgE, which has been shown to reduce
the frequency of CXCR3+CD8+Tcells
30 could be helpful in certain
patients. Moreover, future research to examine the function of CD8+T
cell subsets in the context of food allergy pathogenesis and SU needs
to be extended to include regulatory CD8+Tcells
31 and
Tc2 subpopulations32,theidentification of which was beyond the
scope of our CyTOF panel.
The CyTOF and Luminex data presented here confirmed
attenuation of Th2 phenotype as well as reduction in proinflammatory
cytokines in active participants at week 104 post-OIT. In addition, the
Luminex data also showed significant changes in monocyte and DC
differentiation-related mediators such as GCSF, GMCSF and FLT3L, as
well as a reduction in tissue homeostasis factors viz. IL-22 and VEGF,
which warrant further investigation.
Taken together, this study demonstrates that frequencies of non-
Th2 immune cells, primarily CD8+T cell subsets, may dictate the like-
lihood of achieving sustained unresponsiveness following food
allergy OIT.
Methods
Study participants
The clinical research protocol for POISED study was approved by the
Division of Allergy, Immunology, and Transplantation (DAIT) and the
National Institute of Allergy and Infectious Diseases (NIAID) Allergy
and Asthma Data Safety Management Board, the DAIT/NIAID Clinical
Review Committee, the Stanford Institutional Review Board, and the
US Food and Drug Administration (FDA). Written informed consent
was obtained from adult participants or parents/ guardians of minor
participants along with assent from minor participants of age 7 years
Article https://doi.org/10.1038/s41467-022-34222-8
Nature Communications | (2022) 13:6646 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved

and older. All compliant participants were given $30 gift cards as a
compensation for each study visit as consistent with Stanford IRB-
approved protocol. The study protocol, patient demographics, and
treatment outcomes have been described in detail previously8.Briefly
120 peanut-allergic participants (age: 7–55 years) were randomized
into active vs. placebo group. Participants in the active arm (n=95)
were built up and maintained on 4000mg peanut protein through
week 104. Fifty-one of the total per-protocol active arm participants
were randomized to discontinue peanut consumption post-week 104
(peanut-0 group). DBPCFCs to 4000 mg peanut protein were done at
BL and weeks 104, 117, 130, 143, and 156. Participants in peanut-0
group,who passed oral food challenge (OFC) at week 117 (i.e., Peanut-0
Successes) were denoted as “Sustained unresponsive”, while those,
who failed week 117 OFC (i.e., Peanut-0 Failures) were termed as
“Desensitized”. Baseline demographics for all the participants in the
study have been previously published8. Supplementary Table 1 shows
baseline demographic characteristics for the participants per rando-
mization (i.e., active vs. placebo) and outcome (i.e., SU vs. DS), and for
the subset, whose samples were analyzed through Luminex.
Blood draws and processing
From each study participant, ~40 mL blood was drawn byvenipuncture
and collected in EDTA tubes at baseline (BL, i.e., during the initial
screening phase or at week 0 post-enrollment), and every 3 months
over the course of treatment. PBMCs and plasma isolated by Ficoll-
based density gradient centrifugation were frozen in aliquots, and
stored in liquid nitrogen at −80 °C, until thawing for respective assays.
In these analyses, we used PBMCs and plasma stored at BL, week 104,
and week 117.
Mass cytometry
Mass cytometry studies were performed on PBMC samples at BL, week
104, and week 117 as listed in Supplementary Table 2. PBMCs frozen
per time point per participant were thawed and rested overnight at
37 °C with 5% CO
2
in RPMI + 10% FBS, and Pen-Strep. Cells were
counted, and plated in 3 round-bottom wells of a 96-well plate at the
density of 3 × 106cells in culture with 300 μL RPMI, 5% FBS, Pen-Strep
per well. To evaluate the allergen (peanut)-induced response, PBMCs
in one of the wellswere stimulated with 200 μg/mL peanut solution for
24 h with the addition of brefeldin A (5μg/mL; BioLegend, San Diego,
CA) for the last 4 h. PBMCs in the second well were left unstimulated,
treated with brefeldin A for 4 h before harvesting and served as con-
trols. PBMCs in the third well were stimulated with 20 ng/mL PMA +1
μg/mL Ionomycin (Sigma-Aldrich) for4 h in the presenceof brefeldin A
to measure cytokine expression. The downstream steps comprising
cell harvesting, staining, were followed as previously described12.
Briefly, the cells were harvested at the end of the incubation period,
washed with CyFACS buffer (0.1% bovine serum albumin, 0.1%
sodium azide, 2 mM EDTA in PBS), and stained with surface antibody
cocktail (Supplementary Data 1) for 30 min at room temperature in
dark followed by staining with live/dead 115-DOTA maleimide. The
cells were then washed with CyFACS buffer and barcoded using Cell-
ID 20-Plex Pd Barcoding Kit (Fluidigm Co., Product# 201060)
according to the manufacturer’s instructions. Barcoded cells were
fixed with 2% paraformaldehyde overnight at 4 °C. Following a
CyFACS wash the next day, equal number of cells per sample were
pooled, permeabilized, and stained with intracellular antibody
cocktail (Supplementary Data 1) for 30 min at room temperature. The
cells were then washed twice with permeabilization buffer followed
by two washes with CyFACS buffer. Finally, the pooled samples were
stained with Ir interchelator, washed twice in CyFACS buffer, fol-
lowed by one wash in CyPBS and two further washes in MilliQ water.
Approximately 300,000 cells per sample in each pool were acquired
on a Helios mass cytometer (Fluidigm Co.). Data normalization,
concatenation and debarcoding were performed using CyTOF
software (Fluidigm Co.). Data were analyzed by manual gating as well
as FlowSOM-based unsupervised clustering analysis.
Manual gating on normalized, de-barcoded data files was carried
out using FlowJo v10 (FlowJo LLC., Ashland, OR). Gating scheme used
to identify and analyze CD3+and CD3-immune cell subsets has been
previously illustrated30 as well as depicted in Supplementary Fig. 8a, b.
Workflow illustrations were generated using BioRender application.
The FlowSOM-based unsupervised computational analysis was
performed separately for peanut-stimulated, unstimulated, and PMA/
ionomycin-stimulated samples at independent time points (i.e., week
0, week 104, and week 117)33. CD127 was excluded to minimize batch
effects, and the remaining lineage markers were transformed using
arcsinh function (inverse hyperbolic sine; cofactor of 5), followed by
their further normalization with landmark alignment procedure using
the warpSet function from the flowStats R package (version 4.4.0)34.In
each analysis 50,000 live cells were randomly selected and subjected
to FlowSOM-based clustering with default parameters and with a fixed
number of 30 clusters. A defined set of lineage markers were used for
cell clustering (Supplementary Table 3). Distinct clusters represented
as a heatmap (R package pheatmap, version 1.0.12)35 were manually
annotated by visual inspection of median expression of lineage mar-
kers per cluster. In addition, 100,000 cells were randomly selected
from the predicted clusters for further visualization (R package
ggplot2 version 3.3.3)36 by applying the non-linear dimensionality
reduction technique- Uniform Manifold Approximation and Projection
(UMAP) at the lineage marker levels using the R package UMAP (ver-
sion 0.2. 7.0, default parameters except min_dist = 0.25)37.
Significant differences in frequency (i.e.,proportionor abundance
within a given parent population) of cell subsets and median intensity
(i.e., expression) of functional markers within a cell subset were eval-
uated by manual gating as well as unsupervised clustering analysis. For
both these approaches, pvalues were computed using the χ2tests in
mixed-effects models, wherein defined groups were used as fixed-
effects and batch number as random-effects using lmerTest R
package38. The computed pvalues were adjusted to control the FDR
during multiple hypothesis testing using the Benjamini and Hochberg
method. Readouts with pvalue < 0.05 and adjusted pvalue (i.e., Q
value) <0.1 have been reported as significant in this analysis. Any
extreme outlier data point(s) were identified and removed using
identify_outliers() function (coef = 3.0) from rstatix R package (Ver-
sion 0.7.0)39.
Luminex assay
Luminex analysis was performed on a convenience sample subset
(Supplementary Table 1). PBMCs were thawed, rested for 6 h, and
plated in 2 round-bottom wells of a 96-well plate at the density of
1.4 × 106cells in culture with 200 μL RPMI, 5% FBS, Pen-Strep per well.
PBMCs in one of the wells were stimulated with 200μg/mL peanut
solution for 18 h, while the ones in the other well were left unstimu-
lated for that duration. A total of 130 μL culture supernatant was har-
vested per well in prelabeled microfuge tubes and stored at −80 °C
until performing the assay. Culture supernatants were probed with
Human 48-plex Cytokine/Chemokine magnetic bead panel, while
plasma samples were tested using a combination of 48-plex and 23-
plex Cytokine/Chemokine magnetic bead panel (MilliporeSigma, Bur-
lington, MA). While comparing the readouts (MFIs), pvalues were
computed using Wilcoxon signed-rank sum test (unpaired and paired
where Time is used as group), and adjusted for multiple hypothesis
testing using Benjamini and Hochberg method. The rstatix R package
was used to identify and remove any extreme outlier data point(s)39.
Evaluation of Granzyme B expression by flow cytometry
Week 52 PBMCs from 5 SU and DS participants each were thawed and
rested overnight. Approximately 0.3 million PBMCs were stimulated
with 20 ng/mL PMA and 1 μg/mL Ionomycin for 4 h, and equivalent
Article https://doi.org/10.1038/s41467-022-34222-8
Nature Communications | (2022) 13:6646 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved

number of PBMCs per participant was left unstimulated. Brefeldin A
(5 μg/mL) and Monensin (2 μM) was added to PBMCs during PMAi
stimulation as well as to unstimulated PBMCs for 4 h prior to har-
vesting. On harvesting, cells were stained with antibodies listed in
Supplementary Data 1. Data were acquired on CyTek Dx10 flow cyt-
ometer and analyzed using FlowJo v10 (FlowJo LLC., Ashland, OR).
Spearman correlations
Spearman correlations between mass cytometry and serological
parameters were performed for SU and DS participants. The correla-
tion coefficient (Rho; p) and associated pvalue significance was com-
puted using cor.test() R function. Correlations with ǀpǀ> 0.4 and
p< 0.001 were considered as significant and were shortlisted for sub-
sequent evaluation. The resulting correlations were plotted using
ggplot R function and linear regression line with 95% confidence
interval shading was computed using geom_smooth() R function
(method = lm).
Machine learning analysis
To identify the BL mass cytometry (CyTOF) parameters that distin-
guish SU participants from DS, we applied a Machine learning (ML)
approach on BL simulated datasets (n= 300; see Supplementary
Fig. 6a, b). Data simulation was used to compensate for the small
sample size (n= 46 at BL) available for model training as well as to
obtain confidence intervals for model performance evaluation, using
the simstudy R package40. Here, the data were simulated (300 times)
from manually gated frequencies and marker expression of cell types
from BL CyTOF samples with a set of constraints, including sample
size, mean, and variance per participant p,i.e.,X
p
~N(μ,σ2). Each of the
simulated dataset had the same sample size as the original BL dataset
with overall same mean and variance of CyTOF parameters in the
respective participant (i.e., 46 samples and 123 CyTOF parameters).
For ML prediction, we used elastic net algorithm whichis a regularized
logistic regression method that uses a linear and weighted combina-
tion of L1 and L2 regularization of the lasso and ridge methods41.This
ML algorithm is considered as suitable for the datasets wherein there
are more features (123 features) than instances (i.e., 46 participants).
Here, the glmnet R package (version 4.1-2) was used for executing
elastic net, wherein the model parameters were tuned using k-fold
cross-validation42. Finally, each of the given simulated dataset was used
for building elastic net model and the feature importance matrix were
computed for respective dataset. The model accuracy was evaluated
by comparing the predicted outcome with expected outcome, and
after 300 runs, the performance statistics, including specificity, sen-
sitivity, and area under the curve (AUC) were also evaluated. Robust
CyTOF parameters in each elastic net model were then defined as
those features with score greater than 80 (arbitrary selected threshold;
max(score) = 100 and min(score) = 0). The cumulative importance
scoreforeachfeaturewasdefined as:
Cumulative importance= Pi=iterations
i=1 δ
iterations ×100 ð1Þ
Where, iteration is the number of times (=300) simulated datasets
were modeled, and δis the feature score computed for each model,
and its value in each iteration iis defined as:
δ=1, if δi,>80
1, if δi0, ≤80
ð2Þ
Reporting summary
Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The raw unlabeled POISED Mass Cytometry dataset files generated in
this study in FCS 3.0 format, associated metadata, and the gating
schema have been deposited in the FlowRepository database under
accession codes FR-FCM-Z4MA and FR-FCM-Z2V9. The processed CSV
files are provided in the Source Data file, which is available on the
dryad digital repository under the DOI accession 10.5061/
dryad.95x69p8p6. Source Data are provided with this paper.
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Acknowledgements
This work was supported through the Sean N. Parker Center for Allergy
and Asthma Research at Stanford University and NIAID AADCRC grant
U19AI10420. We thank our patients and their families for their partici-
pation and the staff members of the Sean N. Parker Center for their
support, Drs Yael Rosenberg-Hasson, Ramona Hoh, and Anita Kant for
their advice, and Dr Vanitha Sampath for her help with reviewing and
compiling this manuscript.
Author contributions
K.C.N., S.J.G., S.D.B., and H.T.M. designed the study. R.S.C., S.B.S., and
L.E.K. steered and oversaw trial recruitment, progress, and clinical data
management. S.G. optimized mass cytometry panel. D.D. performed
mass cytometry on all the PBMC samples. M.M. devised manual gating
scheme and analytical strategy. E.D. and M.M. performed manual gating.
X.H. performed all the experimental steps and panel selection for
Luminex analysis. A.F. and M.M. evaluated Granzyme B output from
CD8+T cell subsets using flow cytometry. A.K. analyzed and plotted all
the data in the manuscript including machine learning analysis. S.A.,
M.D., and R.T. guided data analysis, visualizations, and the statistical
evaluations listed in the manuscript. A.K., M.M., R.D.K., and K.C.N. wrote
the manuscript. D.D. and X.H. contributed equally to this work as second
authors. S.J.G. was the overall PI of the NIH-funded U19 study. S.J.G.,
S.D.B., H.T.M., M.T., S.A., M.D., R.T., and W.Y. critically reviewed the
manuscript.
Competing interests
K.C.N. reports grants from National Institute of Allergy and Infec-
tious Diseases (NIAID), National Heart, Lung, and Blood Institute
(NHLBI), National Institute of Environmental Health Sciences
(NIEHS), and Food Allergy Research & Education (FARE); stock
options from IgGenix, Seed Health, ClostraBio, and ImmuneID; is a
Director of World Allergy Organization (WAO), Advisor at Cour
Pharma, Consultant for Excellergy, Red tree ventures, Eli Lilly, and
Phylaxis, Co-founder of Before Brands, Alladapt, Latitude, and
IgGenix; and National Scientific Committee member at Immune
Tolerance Network (ITN), and National Institutes of Health (NIH)
clinical research centers, outside the submitted work; patents
include, “Mixed allergen composition and methods for using the
same”,“Granulocyte-based methods for detecting and monitoring
immune system disorders”,“Methods and Assays for Detecting and
Quantifying Pure Subpopulations of White Blood Cells in Immune
System Disorders,”and “Methods of isolating allergen-specific
antibodies from humans and uses thereof”. R.S.C. reports grants
from NIAID, CoFAR, Aimmune, DBV Technologies, Astellas,
Regeneron, Stanford Maternal and Child Health Research Institute
(MCHRI), and FARE. She is an Advisory Board Member at Alladapt
Therapeutics, Novartis, Genentech, Sanofi, Allergenis, and Nutricia.
S.B.S. reports grants from NIH, Regeneron, DBV, AIMMUNE,
Novartis, CoFAR, and FARE. She receives personal fees from Astra
Zeneca, DBV, and honoraria from FARE. S.J.G. reports grants from
the NIH. S.D.B. has consulted for Regeneron, Sanofiand Novartis
on topics unrelated to this study, owns shares in AbCellera, has
patents awarded or submitted related to immunoglobulin gene and
protein analysis, and reports grants from the NIH. S.A. reports
grants from the NIH. All others authors declare no competing
interests.
Article https://doi.org/10.1038/s41467-022-34222-8
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Additional information
Supplementary information The online version contains
supplementary material available at
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Correspondence and requests for materials should be addressed to
Kari C. Nadeau.
Peer review information Nature Communications thanks the anon-
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