ReportCellular and humoral functional responses after
BNT162b2 mRNA vaccination differ longitudinally
between naive and subjects recovered from COVID-
19Graphical abstractHighlightsd History of SARS-CoV-2 infection affects longitudinal
responses to BNT162b2 vaccine
d Lower humoral but enhanced cellular responses early after
vaccine in naive subjects
d Comparable humoral and cellular responses almost 8months
after vaccination
d Similar S-specific B cells late after vaccine in those naive and
recovered from COVID-19Lozano-Rodrı´guez et al., 2022, Cell Reports 38, 110235
January 11, 2022 ª 2021 The Authors.
https://doi.org/10.1016/j.celrep.2021.110235Authors
Roberto Lozano-Rodrı´guez,
Jaime Valentı´n-Quiroga,
Jose´ Avendan˜o-Ortiz, ..., Luis A. Aguirre,
Carlos del Fresno,
Eduardo Lo´pez-Collazo
Correspondence
carlos.delfresno.sanchez@idipaz.es
(C.d.F.),
elopezc@salud.madrid.org (E.L.-C.)
In brief
Lozano-Rodrı´guez et al. show that naive
subjects have enhanced SARS-CoV-2
spike-specific T reactions but reduced
humoral-specific responses compared
with individuals recovered from COVID-
19. However, almost 8 months after
vaccination, comparable specific
responses are observed with equivalent
levels of SARS-CoV-2-specific B cells
and neutralizing antibodies.ll
OPEN ACCESS
llReport
Cellular and humoral functional responses
after BNT162b2 mRNA vaccination differ longitudinally
between naive and subjects recovered from COVID-19
Roberto Lozano-Rodrı´guez,1,2,11 Jaime Valentı´n-Quiroga,1,2,11 Jose´ Avendan˜o-Ortiz,1,2,11 Alejandro Martı´n-Quiro´s,3
Alejandro Pascual-Iglesias,1,2 Vero´nica Terro´n-Arcos,1,2 Karla Montalba´n-Herna´ndez,1,2
Jose´ Carlos Casalvilla-Duen˜as,1,2 Marta Bergo´n-Gutie´rrez,1,2 Jose´ Alcamı´,4 Javier Garcı´a-Pe´rez,4 Almudena Cascajero,4
Miguel A´ngel Garcı´a-Garrido,3 A´lvaro del Balzo-Castillo,1,3 Marı´a Peinado,3 Laura Go´mez,3 Irene Llorente-Ferna´ndez,5
Gema Martı´n-Miguel,6 Carmen Herrero-Benito,3 Jose´ Miguel Benito,7,8 Norma Rallo´n,7,8 Carmen Vela-Olmo,9
Lissette Lo´pez-Morejo´n,9 Carolina Cubillos-Zapata,1,2,10 Luis A. Aguirre,1,2 Carlos del Fresno,1,2,*
and Eduardo Lo´pez-Collazo1,2,10,12,*
1The Innate Immune Response Group, IdiPAZ, La Paz University Hospital, Madrid, Spain
2Tumor Immunology Laboratory, IdiPAZ, La Paz University Hospital, Madrid, Spain
3Emergency Department and Emergent Pathology Research Group, IdiPAZ La Paz University Hospital, Madrid, Spain
4AIDS Immunopathogenesis Unit, National Microbiology Centre, Instituto de Salud Carlos III, Madrid, Spain
5Intensive Care Unit, Infanta Cristina University Hospital, Parla, Madrid, Spain
6Pediatric Intensive Care Unit, 12 de Octubre University Hospital, Madrid, Spain
7HIV and Viral Hepatitis Research Laboratory, Instituto de Investigacio´n Sanitaria Fundacio´n Jime´nez Dı´az, Universidad Auto´noma deMadrid
(IIS-FJD, UAM), Madrid, Spain
8Hospital Universitario Rey Juan Carlos, Mo´stoles, Spain
9Eurofins-Ingenasa, Madrid, Spain
10CIBER of Respiratory Diseases (CIBERES), Madrid, Spain
11These authors contributed equally
12Lead contact
*Correspondence: carlos.delfresno.sanchez@idipaz.es (C.d.F.), elopezc@salud.madrid.org (E.L.-C.)
https://doi.org/10.1016/j.celrep.2021.110235SUMMARYWe have analyzed BNT162b2 vaccine-induced immune responses in naive subjects and individuals recovered
from coronavirus disease 2019 (COVID-19), both soon after (14 days) and later after (almost 8 months) vacci-
nation. Plasma spike (S)-specific immunoglobulins peak after one vaccine shot in individuals recovered from
COVID-19, while a second dose is needed in naive subjects, although the latter group shows reduced levels
all along the analyzed period. Despite how the neutralization capacity against severe acute respiratory syn-
drome coronavirus 2 (SARS-CoV-2) mirrors this behavior early after vaccination, both groups show compara-
ble neutralizing antibodies andS-specificB cell levels late post-vaccination.When studying cellular responses,
naive individuals exhibit higher SARS-CoV-2-specific cytokine production, CD4+ T cell activation, and prolifer-
ation than do individuals recovered from COVID-19, with patent inverse correlations between humoral and
cellular variables early post-vaccination. However, almost 8 months post-vaccination, SARS-CoV-2-specific
responses are comparable between both groups. Our data indicate that a previous history of COVID-19 differ-
entially determines the functional T and B cell-mediated responses to BNT162b2 vaccination over time.INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
infection and its associated pathology, coronavirus disease 2019
(COVID-19), have had an enormous impact on healthcare sys-
temsworldwide and still constitute a challenge. Several vaccines
have been authorized for emergency use by both the US Food
and Drug Administration (FDA) and the European Medicines
Agency (EMA). Among them, the BNT162b2 messenger RNA
(mRNA) vaccine has been widely used following an accelerated
two-dose vaccination schedule, which has exhibited specific hu-This is an open access article under the CC BY-Nmoral and cellular responses in 95% of individuals (Polack et al.,
2020).
A number of studies have suggested a strong spike-specific
antibodies generation by individuals recovered from COVID-19
after a first vaccine shot and that the second dose appears to
be redundant (Ebinger et al., 2021; Gobbi et al., 2021; Levi et
al., 2021; Prendecki et al., 2021a). In contrast, a second dose
seems to be needed for a strong immunization in naive subjects
(Ebinger et al., 2021; Levi et al., 2021;Mulligan et al., 2020;Walsh
et al., 2020). Besides, the effect of this mRNA vaccine on spike-
specific T cell responses has gained much attention (Ni et al.,Cell Reports 38, 110235, January 11, 2022 ª 2021 The Authors. 1
C-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Report
ll
OPEN ACCESS2020; Prendecki et al., 2021b; Sasikala et al., 2021). In this re-
gard, to understand the cellular responses generated after vacci-
nation, considering previous SARS-CoV-2 exposure is crucial for
future adjustments in vaccination regimes.
Some reports are warning about the waning of the BNT162b2-
vaccine-induced protection a few months after vaccination
despite still showing a robust efficacy against suffering from
COVID-19 (Chemaitelly et al., 2021; Goldberg et al., 2021). This
wane has been linked to a decay in the levels of SARS-CoV-2-
specific neutralizing antibodies (Bayart et al., 2021; Doria-Rose
et al., 2021), although neither the role of SARS-CoV-2 spike-spe-
cific T (Guerrera et al., 2021) and B (Turner et al., 2021) cells is
fully understood nor the relationship between both humoral
and cellular responses triggered by COVID-19 vaccines against
(re)infection.
Herein, we aimed to evaluate the immune responses triggered
by immunization with the BNT162b2 vaccine in a cohort of naive
subjects and individuals recovered fromCOVID-19. Both humor-
al and cellular responses were thoroughly analyzed using blood
samples taken before vaccination, after the first dose, 14 days,
and almost 8 months after the vaccination regime was
completed. Our data indicated that previous SARS-CoV-2 expo-
sure conditioned early responses post-vaccination, as naive
subjects showed enhanced SARS-CoV-2 spike-specific CD4+
T cells but reduced humoral spike-specific responses compared
with individuals recovered from COVID-19. However, almost
8 months after vaccination, comparable humoral and cellular
responses were observed in both groups, importantly, with
equivalent levels of SARS-CoV-2-specific memory B cells and
neutralizing antibodies. Therefore, our findings suggest that pre-
vious exposure to the virus determines early functional T and B
cell-mediated responses to BNT162b2 vaccination. However,
both naive subjects and individuals recovered from COVID-19
show comparable memory SARS-CoV-2-specific immunity
almost 8 months after vaccination.
RESULTS
Humoral responses triggered after vaccination show
specific kinetics in naive subjects and individuals
recovered from COVID-19
Following the BNT162b2 vaccination strategy recommended by
both the FDA and the EMA, a total of 27 individuals were vacci-
nated with a two-dose regime administrated 21 days apart. Of
them, 16 had not been previously exposed to SARS-CoV-2 co-
ronavirus (naive), while 11 were reported as having recovered
from COVID-19 (Table S1). For all participants, four blood sam-
ples were taken: 5 days before the first dose (sample 0),
14 days after the first dose (sample 1), 14 days after the second
dose (sample 2), and a final long-term sample collected 230 days
(almost 8 months) after the second dose (sample 3) (Figure 1A).
We first analyzed the levels of SARS-CoV-2-specific plasma
immunoglobulins. One vaccination dose induced the presence
of both anti-spike S1 immonoglobulin A (IgA) and anti-receptor
binding domain (RBD) IgAs, whose levels were further boosted
by the second immunization dose in naive individuals (Figure 1B).
Although subjects recovered from COVID-19 showed higher
levels of IgA than naive participants after the first dose, the con-2 Cell Reports 38, 110235, January 11, 2022centrations after the second vaccination shot were comparable
between both groups and, again, slightly higher in subjects
recovered from COVID-19 after almost 8 months post-vaccina-
tion (sample 3) (Figure 1B). The analysis of IgGs (anti-spike S1,
anti-RBD, and anti-full spike) in sample 0 confirmed that the sub-
jects recovered from COVID-19 had been previously exposed to
SARS-CoV-2, and these participants showed higher levels of
specific IgGs than naive individuals throughout the observation
period (Figure 1C). It is noteworthy that the titers of all the
analyzed antibodies dropped in sample 3, but individuals recov-
ered fromCOVID-19maintained slightly higher levels (Figures 1B
and 1C).
Beyond the Ig concentrations, we evaluated the neutralization
capacity of plasma against the spike antigen. The neutralization
capacity was measured using a competitive immunoassay. In
naive individuals, two doses were required to induce neutralizing
antibodies, whereas in recovered individuals, one dose induced
high neutralization titers. Of note, after a second dose, subjects
recovered from COVID-19 further increased their neutralization
activity, which was higher than in naive individuals 14 days after
full vaccination (Figure 1D). Note that, although the neutralization
capacity was still measurable, the neutralizing antibodies drop-
ped dramatically in sample 3. Importantly, this neutralization
ability was similar between naive subjects and individuals recov-
ered from COVID-19 at this long-term post-vaccination time
(Figure 1D). To further characterize the differential neutralization
capacity conditioned by previous exposure to SARS-CoV-2, a
functional assay based on the neutralization of a pseudovirus ex-
pressing the spike protein of SARS-CoV-2 was done. We
accomplished this analysis in sample 2, the first one where
both naive subjects and individuals recovered from COVID-19
showed neutralizing activity. This analysis confirmed that individ-
uals recovered fromCOVID-19 exhibited a better neutralizing ca-
pacity (Figure S1A). Altogether, these data indicated a differential
expression pattern of humoral responses between naive individ-
uals and subjects recovered from COVID-19 over time post-
vaccination (Figure 1E).
Next, we focused on circulating B cell-derived populations
because of their role in humoral responses. Based on a fluores-
cence-activated cell sorting (FACS) panel of 39 extracellular
markers and an unsupervised uniform manifold approximation
and projection (UMAP) dimensional reduction followed by
manual gating, we identified canonical cell subsets in peripheral
blood mononuclear cells (PBMCs) (Figure 1F). Again, we per-
formed this analysis in sample 2, where naive subjects and indi-
viduals recovered from COVID-19 showed neutralizing activity
(Figure 1D). An overall analysis of B cells differentiated up to 6
different subpopulations in naive subjects and patients recov-
ered from COVID-19 (Figure 1G). The UMAP analysis of human
leukocyte antigen (HLA)-DR, IgD, IgM, and IgG expressions
suggested no major changes between these two groups (Fig-
ure 1H), which was confirmed by quantitative assessments (Fig-
ure S1B–S1H). The same multiparametric approach was
applied to study circulating T cells, showing that the popula-
tions’ distribution and activation markers’ expression were
comparable between naive subjects and individuals recovered
from COVID-19 except for a slight increase in CD4+ T regulatory
cells (Figure S2).
Figure 1. SARS-CoV-2 spike-specific humoral response following BNT162b2 mRNA vaccination in naive subjects and individuals recovered
from COVID-19
(A) Experimental design. Blood samples were collected 5 days before BNT162b2 mRNA vaccination (sample 0), 14 days after the first dose (sample 1), and
14 days (sample 2) and 230 days (sample 3) after the second dose.
(B) Concentrations of plasma anti-spike S1 IgA (left panel) and anti-receptor binding domain (RBD) IgA (right panel) antibodies.
(C) Concentrations of plasma anti-spike S1 IgG (left panel), anti-RBD IgG (central panel), and anti-full spike IgG (right panel) antibodies.
(D) Concentration of neutralizing antibodies in plasma by means of a competitive assay; 108/free anti-spike signal is depicted.
(E) Heatmap of Z score of IgA, IgG, and anti-spike neutralizing antibodies.
(F) Uniform manifold approximation and projection (UMAP) of peripheral blood mononuclear cells (PBMCs) followed by manual gating to identify the indicated
populations.
(G) UMAP of B cells followed by manual gating to identify the indicated populations in sample 2.
(H) UMAP clustering expressions of HLA-DR, IgD, IgM, and IgG on B cells.
(I) Frequency of SARS-CoV-2 spike-specific B cells in gated CD19+ cells in sample 3.
(B, C, D, and I) Data shown as mean ± SEM (ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001). Unpaired Student’s t test in samples 0, 1, 2, or 3.
(B, C, and D) Two-way ANOVA analyzing the time course (denoted by vertical bar, |). n = 16 naive, n = 11 recovered from COVID-19.
See also Figures S1, S2, and S3A.
Cell Reports 38, 110235, January 11, 2022 3
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OPEN ACCESSConsidering the lack of differences in the levels of neutralizing
antibodies between naive subjects and individuals recovered
from COVID-19 almost 8 months after complete vaccination
(sample 3; Figure 1D), we decided to analyze the levels of
SARS-CoV-2 spike-specific B cells in this long-term time point
(Figure S3A). Remarkably, all of the participants showed
SARS-CoV-2 spike-specific B cells, with comparable levels be-
tween groups (Figure 1I).
Naive individuals show enhanced SARS-CoV-2-specific
T cell lymphoproliferative responses early after
vaccination but are similar to those recovered from
COVID-19 at later time points
To explore whether previous exposure to SARS-CoV-2 could
modulate specific cellular responses against this coronavirus in
fully vaccinated individuals, PBMCs from both naive and recov-
ered subjects were ex vivo-exposed to a peptide pool covering
the SARS-CoV-2 spike protein, henceforth called the S-peptide
(Figure 2A). First, we analyzed the presence of several chemo-
kines and cytokines in culture supernatants after this ex vivo
stimulation for 5 days. The production of both CCL-2 and
CXCL10 was induced by the SARS-CoV-2 spike peptide pool
except for CCL-2 in individuals recovered fromCOVID-19 almost
8 months post-vaccination (Figure 2B). However, only naive indi-
viduals showed a robust induction of most of the cytokines
analyzed (interleukin [IL]-2, IL-4, IL-6, IL-10, and tumor necrosis
factor alpha [TNFa]), although this S-peptide-specific response
in naive subjects was exclusive to sample 2 (Figure 2C). Interest-
ingly, interferon (IFN)-g expression showed a specific pattern,
mirroring CCL2 production (Figures 2B and 2C).
To further examine this differential outcome, cytokine produc-
tion was analyzed by intracellular FACS staining. The expression
of IL-2, TNFa, IFN-g, and granzyme B were consistently induced
in CD4+ T cells after ex vivo stimulation with the S-peptide pool in
both naive subjects and individuals recovered from COVID-19
early after vaccination (sample 2) (Figures 2D and S4), with less
robust responses in CD8+ T cells. However, more than 7 months
after vaccination (sample 3), this SARS-CoV-2-specific response
was negligible in both groups (Figures 2D and S4).
Interestingly, the analysis of the intracellular cytokine produc-
tion increment induced by SARS-CoV-2 spike antigen ex vivo
stimulation showed a much more intense induction of IL-2 in
both CD4+ and CD8+ T cells from naive subjects than from in-
dividuals recovered from COVID-19 early after vaccination
(sample 2) (Figure 2E). Considering the crucial role of IL-2 in
the lymphoproliferative capacity of CD4+ T cells, we decided
to analyze this function. Proliferation ability was explored based
on carboxyfluorescein succinimidyl ester (CFSE) dilution of total
PBMCs after ex vivo stimulation with the S-peptide pool. Both
CD4+ and CD8+ T cells proliferated in response to the spike an-
tigen in naive subjects and individuals recovered from COVID-
19, although with an apparent stronger effect on CD4+ T cells
from naive subjects (Figure 2F). The increment of proliferation
between SARS-CoV-2 spike-antigen-stimulated and non-stim-
ulated PBMCs confirmed a more powerful CD4+ lymphoproli-
ferative activity in naive subjects than in individuals recovered
from COVID-19, specifically, early after vaccination (sample 2)
(Figure 2G).4 Cell Reports 38, 110235, January 11, 2022These data suggest a strong SARS-CoV-2 spike-specific
T cell response early after vaccination in naive subjects (but
not in COVID-19-recovered individuals) that declines over time.
SARS-CoV-2-specific effector memory T cell enhanced
responses in naive individuals decline along the timeline
Next, we dissected the SARS-CoV-2 spike-specific T cell re-
sponses. First, we analyzed the phenotype of both proliferative
(CFSEdim) and non-proliferative (CFSEbright) CD4+ T cells after
antigen-specific stimulation (Figure S3B). As expected, this
challenge induced the transition from a naive to effector mem-
ory (EM) phenotype in proliferated CD4+ T cells (Figures 3A and
3B). Of note, early after vaccination (sample 2), an increase in
the frequency of EM re-expressing CD45RA (EMRA) cells
was observed, while almost 8 months after vaccination (sample
3), a significant central memory (CM) response was induced
(Figures 3A and 3B). Interestingly, the study of these popula-
tions in terms of proliferative capacity showed that previous
exposure to SARS-CoV-2 had no impact on these transitions
along the timeline (Figure 3C). Although in a less robust way,
similar behaviors were observed for CD8+ T cells (Figures
S5A–S5C).
We next analyzed intracellular cytokine production in CD4+
T cell subpopulations induced by ex vivo SARS-CoV-2 spike
peptide pool stimulation. A consistent IL-2 production was
observed in naive individuals early after vaccination (sample 2)
that was maintained in effector populations (EMRA and EM) in
the long term (sample 3) (Figure 3D). However, CD4+ T cells
from subjects recovered from COVID-19 did not respond to
the SARS-CoV-2 spike peptide pool stimulation at any time (Fig-
ure 3D). In line with previous observations, naive individuals
showed a stronger increment of IL-2 production in the EMRA
and EM population than did subjects recovered from COVID-
19 early after vaccination that declined almost 8 months post-
vaccination (Figure 3E). Again, despite a less robust response
after antigen-specific stimulation, a similar IL-2 expression
pattern was observed in CD8+ T cells (Figures S5D and S5E).
These data indicated that previous infections of SARS-CoV-2
dampened T EM cellular responses early after a complete
BNT162b2 vaccination. However, almost 8 months post-vacci-
nation, the SARS-CoV-2 spike-specific T responses were com-
parable between naive subjects and individuals recovered from
COVID-19.
Humoral and cellular activation features are inversely
correlated early after vaccination
Based on the differential behavior of SARS-CoV-2 spike-specific
humoral and cellular responses between naive subjects and in-
dividuals recovered from COVID-19, we explored whether these
features could identify subjects belonging to these two groups,
in an unsupervised manner, in samples 2 and 3. To address
this question, we performed a clustering analysis based on the
different immunological variables studied in this work (Figure 4A).
This algorithm generated a clearer discrimination between naive
subjects and individuals recovered from COVID-19 in sample 2
than in sample 3 (Figure 4A).
Next, we depicted the correlation between the analyzed vari-
ables once theywere classified based on their functionality. Along
Figure 2. SARS-CoV-2 spike-specific cellular ex vivo response following BNT162b2 mRNA vaccination in naive subjects and individuals
recovered from COVID-19
(A) Experimental design of the T cell cellular response ex vivo in PBMCs in samples 2 and 3 after stimulation with SARS-CoV-2 spike peptide pool.
(B) CCL-2 and CXCL10 chemokines production.
(C) IL-2, IL-4, IL-6, IL-10, TNFa, and IFNg production.
(D) Percentage of IL-2+ cells in CD4+ (left panel) and CD8+ (right panel) T cells.
(E) Increment of IL-2+ cells comparing SARS-CoV-2 spike peptide pool-stimulated and non-stimulated CD4+ and CD8+ T cells.
(F) Frequency of proliferative (CFSEdim) CD4+ and CD8+ T cells.
(G) Increment of proliferation comparing SARS-CoV-2 spike peptide pool-stimulated and non-stimulated CD4+ and CD8T cells.
(B–G) Each dot represents an individual. (B–D and F) Paired Student’s t test (ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001). (E and G) Mann
Whitney test (ns, not significant; *p < 0.05; **p < 0.01). n = 16 naive, n = 11 recovered from COVID-19.
See also Figure S4.
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Figure 3. Memory populations in SARS-CoV-2 spike-specific CD4+ T cells following BNT162b2 mRNA vaccination in naive subjects and
individuals recovered from COVID-19
(A and B) PBMCs were labeled with CFSE and stimulated with SARS-CoV-2 spike peptide pool for 5 days. CD4+ T cells were classified according to their
proliferative response in samples 2 and 3. Memory subpopulations were analyzed (naive; CM, central memory; EMRA, effector memory cells re-expressing
CD45RA; EM, effector memory). Frequencies (A) and mean distribution (B) of memory populations in proliferative (B; CFSEdim) and non-proliferative
(,; CFSEbright) CD4+ T cells.
(C) Proliferative (CFSEdim) versus non-proliferative (CFSEbright) ratio of CD4+ T cell memory populations in samples 2 (D) and 3 (>).
(D) Frequency of IL-2+ cells in gated naive, CM, EMRA, and EM CD4+ T cells stimulated or not with SARS-CoV-2 spike peptide pool.
(E) Increment of frequencies of IL-2+ cells comparing SARS-CoV-2 spike pool-stimulated and non-stimulated CD4+ T cell subpopulations.
(A, C–E) Each dot represents an individual. (A, C, and D) Paired Student’s t test (ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001). (E) Mann
Whitney test (ns, not significant; *p < 0.05). n = 16 naive, n = 11 recovered from COVID-19.
See also Figure S3B, S3C, and S5.
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OPEN ACCESSthese lines, humoral parameters quantified in plasma were con-
fronted to the cellular response after ex vivo cellular stimulation
withSARS-CoV-2 spikepeptidepool (Figure 4B). This representa-
tion suggested inverse correlationsearly after vaccination (sample
2) between humoral and cellular responses, particularly IgG pro-
duction andneutralization capacity ofSARS-CoV-2 spike-specific
pro-inflammatory cytokine (CCL2, CXCL10, IFNg, and IL-2) pro-
duction and CD4+ T cell proliferation (Figure 4B). However, these6 Cell Reports 38, 110235, January 11, 2022correlationswereattenuatedmore than7monthsaftervaccination
(sample 3) (Figure 4B). The analysis of these correlations
confirmed the statistically significant inverse association between
Ig-based and cellular responses such as CD4+ T cell proliferation
or IL-2 production in sample 2 (Figure 4C). However, no significant
correlationswere found between these variables in sample 3 (Fig-
ure 4C). Altogether, theseanalyses revealed that early after a com-
plete vaccination regimen with BNT162b2, differential humoral
Figure 4. Differential SARS-CoV-2 spike-specific humoral and cellular responses between naive subjects and individuals recovered from
COVID-19 after vaccination
(A) Heatmap analysis of main humoral and cellular variables of SARS-CoV-2 spike-specific responses. This algorithm showed differential clustering distribution
between naive subjects and individuals recovered from COVID-19.
(B) Spearman correlation matrix heatmap of the main humoral and cellular variables of SARS-CoV-2 spike-specific responses grouped by functionality.
(C) Spearman correlations between different SARS-CoV-2 spike-specific humoral and cellular responses. Top: ratio of CD4+ T cells proliferation versus titers of RBD
IgG antibodies; middle: ratio of CD4+ T cells proliferation versus S1 IgG antibodies; bottom: IL-2 production in supernatants versus titers of neutralizing antibodies.
R, Spearman’s rank correlation coefficient. p value in Spearman correlation test (ns, not significant; *p < 0.05). White dots represent naive individuals; red dots
represent subjects recovered from COVID-19. n = 16 naive, n = 11 recovered from COVID-19.
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OPEN ACCESSandcellular responseswere triggeredbetweennaive subjects and
individuals recovered from COVID-19 almost eight months after
vaccination, both responses were comparable between both
groups.
DISCUSSION
The generation of mRNA COVID-19 vaccines such as mRNA-
1273 (Baden et al., 2021) and BNT162b2 (Polack et al., 2020)
represents a revolution in vaccinology and is one of the key pil-
lars of humanity’s eventual success against the pandemic
caused by the SARS-CoV-2 infection. These vaccines are based
on a lipid-nanoparticle-encapsulated mRNA encoding the full-
length spike protein of the SARS-CoV-2 virus (Corbett et al.,
2020; Walsh et al., 2020). The BNT162b2 vaccine was the first
COVID-19 vaccine approved for emergency use by both theFDA and the EMA. This approval was based on the results of a
clinical trial declaring an efficacy of 95% in preventing COVID-
19 after a two-dose regime 21 days apart (Polack et al., 2020).
Since then, a global vaccination campaign began aiming to
face off the pandemic.
It is worth noting that a medical history of COVID-19 was an
exclusion criterion to be enrolled in the above-mentioned clinical
trial (Polack et al., 2020). Therefore, the potential effect of a pre-
vious infection with SARS-CoV-2 was not anticipated. Here, we
have performed a broad analysis of both humoral and cellular re-
sponses triggered byBNT162b2 vaccination by comparing naive
subjects and individuals recovered from COVID-19 along a time-
line after receiving the complete vaccine regime.Weperformed a
massive phenotypic study of PBMCs after vaccination, but more
importantly, it was accompanied by the analysis of functional
immunological capabilities such as antibody neutralization andCell Reports 38, 110235, January 11, 2022 7
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OPEN ACCESST cell activation and proliferation in response to specific SARS-
CoV-2 spike antigens. Notably, our analysis covers responses
after only one vaccine dose but also early (14 days) and late
(more than 7 months) responses after the complete two-dose
vaccination regime.
Previous studies addressing differential responses between
naive subjects and individuals recovered from COVID-19
have focused on the analysis of specific antibodies as a subro-
gated of vaccine efficacy. In phase 1/2 studies, mRNA immuni-
zation with BNT162b2 showed that two doses were requested
to elicit high titers of neutralizing antibodies in naive individuals;
in contrast, in recovered patients, the first immunization acted
as a booster, thus inducing neutralization titers higher than
those observed after the full immunization of naive patients
(Mulligan et al., 2020; Walsh et al., 2020). Along the same lines,
a pioneer study indicated that individuals with a previous
SARS-CoV-2 infection generated stronger humoral responses
than infection-naive subjects after just a single dose of the
BNT162b2 vaccine (Prendecki et al., 2021a). These findings
were confirmed in a cohort of volunteers that received either
the BNT162b2 or the mRNA-1273 vaccine (Krammer et al.,
2021) but also in individuals receiving only one shot of the vac-
cine based on the SARS-CoV-2 spike protein-expressing
adenovirus (Sasikala et al., 2021; Voysey et al., 2021). Never-
theless, follow-up studies showed that anti-spike SARS-CoV-
2 IgGs titers were comparable between naive subjects and
individuals recovered from COVID-19 after 11 to 21 days of
the complete two-dose regime (Ebinger et al., 2021; Levi et
al., 2021). Of note, our data on early humoral responses sup-
port these findings. We also observed that antibody levels
and the neutralizing capacity of plasma from individuals recov-
ered from COVID-19 were higher than that of naive subjects
early after vaccination (samples 1 and 2), an effect suggested
but not fully analyzed in a previous study (Gobbi et al., 2021).
However, more than 7 months after vaccination, individuals
recovered from COVID-19 still showed higher antibody titers
but comparable neutralizing antibodies to naive subjects.
These data highlight the need to discriminate between anti-
body titers and neutralizing capacity. In addition, the analysis
of this neutralizing capacity against specific SARS-CoV-2 var-
iants of concern (Carren˜o et al., 2021; Noori et al., 2021) would
improve our understanding about the breadth of the vaccine-
conferred protection.
The relevance of cellular responses after SARS-CoV-2 virus
infection have been studied (Grifoni et al., 2020; Ni et al.,
2020), but data regarding how previous exposure to SARS-
CoV-2 impacts these immunogenic responses along the timeline
after vaccination are still scarce. Memory B cell responses one
week after vaccination were boosted in individuals recovered
from COVID-19 after just one shot of the mRNA vaccine, while
naive subjects required two doses to reach comparable memory
B cell levels (Goel et al., 2021). This responsemirrors the produc-
tion of SARS-CoV-2 spike-specific antibodies early after vacci-
nation, as previously discussed.
The analysis of the vaccine-induced humoral responses
7 months after the complete vaccination regime showed a
drop in antibody titers in the long term, in accordance with other
studies (Doria-Rose et al., 2021; Naaber et al., 2021), alongwith a8 Cell Reports 38, 110235, January 11, 2022marked decrease in the neutralizing capacity, reaching compa-
rable low levels in both naive subjects and individuals recovered
from COVID-19. Of note, these data do not necessarily indicate
the lack of specific protection against the SARS-CoV-2 virus
because, in line with other studies (Ciabattini et al., 2021; Turner
et al., 2021), we detected circulating SARS-CoV-2 spike protein-
specific B cells even more than 7 months after full vaccination.
Importantly, the levels of these B cells were comparable be-
tween naive subjects and participants recovered from COVID-
19. Considering that SARS-CoV-2 spike-specificmemory B cells
showed a switch to an anti-RBD neutralizing phenotype (Sokal
et al., 2021), it is tempting to speculate that long-term protection
already documented for the BNT162b2 vaccine (Thomas et al.,
2021) is warranted, based at least in part on the restimulation
of these cells during a SARS-CoV-2 reinfection. Future studies
will shed light on the efficacy of this specific protective
mechanism.
In our study, we analyzed SARS-CoV-2 spike-specific re-
sponses in T cells after restimulation with a peptide pool
covering this antigen. This assay showed a differential response
between naive subjects and individuals recovered from COVID-
19 early after vaccination, with a more pronounced activation of
CD4+ T cells in naive subjects. This was revealed by a higher in-
duction of cytokine production and proliferation after restimula-
tion, particularly in EM cells. The mechanistic implication of
regulatory T cells (Tregs) in this effect (Campbell and Koch,
2011) deserves further studies, as we observed higher levels of
this immunomodulatory population in individuals recovered
from COVID-19 at this early time point after vaccination. Of
note, high levels of SARS-CoV-2 spike-specific CD4+ T cells
correlate with a lower COVID-19 predisposition (Sattler et al.,
2020), stressing the relevance of a robust cellular response. Of
note, previously published data indicated a reduction of the
SARS-CoV-2-specific T cell-mediated responses along the
timeline after vaccination (Guerrera et al., 2021). Along these
lines, we observed that T cell responses dropped to comparable
levels in both naive subjects and individuals recovered from
COVID-19.
Our data point toward boosted T cell responses in naive
individuals early after complete BNT162b2 vaccination in a sce-
nario of reduced humoral reactions such as lower SARS-CoV-2
spike-specific IgGs titers and neutralizing capabilities. This
concerted response allowed an unsupervised clustering of
naive subjects and individuals recovered from COVID-19 that
anticipated inverse correlations between cellular and humoral
immune responses. This is relevant, as it is known that cellular
immunity may contribute to protection against SARS-CoV-2
infection if antibody responses are suboptimal (McMahan
et al., 2021). Therefore, our data suggest that this differential
mechanism could take place early after vaccination in naive in-
dividuals compared with in subjects recovered from COVID-19.
However, more than 7 months after vaccination, humoral and
cellular responses dropped similarly in both naive subjects
and individuals recovered from COVID-19, showing no correla-
tions. Still, memory SARS-CoV-2 spike-specific B cells were
present at comparable levels in both groups, suggestive of an
equivalent long-term protection mechanism (Ciabattini et al.,
2021; Turner et al., 2021).
Report
ll
OPEN ACCESSIn summary, our data indicate that concerted humoral and
cellular responses over time after vaccination should be consid-
ered to define vaccination regimes against COVID-19. This
notion could apply to proposals such as the delay of the second
vaccination dose (Kadire et al., 2021), the administration of just
one shot to a population previously infected with SARS-CoV-2
(Goel et al., 2021), or of a third boosting dose (Mahase, 2021).
Limitations of the study
Sample size is a limitation of this study. Considering the high
number of immune variables analyzed and their complexity, we
decided to perform our study with a not-so-large but well-
controlled cohort of participants. We believe that this approach
allowed us to reach clear conclusions, but a multicentre cohort
with a larger number of patients would be desirable. Further-
more, all stimulations and detections of SARS-CoV-2-specific
responses have been performed against the original S-protein.
The analysis of such responses against SARS-CoV-2 variants
of concern would expand the relevance of our study. Finally,
mechanistic studies would help to explain the divergent re-
sponses observed between naive subjects and individuals
recovered from COVID-19.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d RESOURCE AVAILABILITYB Lead contact
B Material availability
B Data and code availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Healthy health personnel volunteers and longitudinal
samples
B Culture conditions of primary and Vero E6 cells
d METHOD DETAILS
B PBMCs isolation, storage and thawing procedure
B Plasma collection
B Algorithm for dimensionality reduction
B Detection of anti-spike IgA and IgG SARS-CoV-2 anti-
bodies
B Detection of neutralization capacity of plasma against
SARS-CoV-2 Spike antigen
B Antibodies and immunophenotyping by flow cytometry
B SARS-CoV-2 Spike-specific B cells detection
B SARS-CoV-2 Spike-specific T cell proliferation assays
and supernatant collection
B Supernatant soluble cytokine quantification
B PBMCs stimulation and intracellular cytokine staining
d QUANTIFICATION AND STATISTICAL ANALYSIS
B Statistical analysis of biological data
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
celrep.2021.110235.ACKNOWLEDGMENTS
C.d.F., J.G.-P., and J.A. are supported by Instituto de Salud Carlos III (ISCII).
We thank JM Ligos and Cytek Biosciences for their technical support.
Research in E.L.-C.’s lab was supported by Fundacio´n Familia Alonso,
Santander Bank, Real Seguros, Fundacio´n Mutua Madrilen˜a, Fundacio´n
Uria, Fundacio´n La Caixa, and Ayuntamiento de Madrid. This work is dedi-
cated to the memory of Norma Lo´pez-Collazo.
AUTHOR CONTRIBUTIONS
E.L.-C., C.d.F., R.L.-R., J.A.-O., and L.A.A. designed the study. E.L.-C. and
C.d.F. wrote the manuscript. A.M.-Q., M.A.G.-G., A.d.B.-C., M.P., L.G.,
I.L.-F., G.M.-M., and C.H.-B. recruited the participants and collected the sam-
ples. R.L.-R., J.A.-O., J.V.-Q., K.M.-H., J.C.C.-D.,M.B.-G., and V.T. performed
the analysis and the immunological biomarkers quantification. J.M.B. and N.R.
provided the know-howandcritical reagents for the intracellular FACS staining.
J.V.-Q. and C.d.F. performed the UMAP analysis. J.A., J.G.-P., A.C., C.V.-O.,
and L.L.-M. performed design and neutralization testing. A.P.-I. performed
the clustering heatmap analysis. E.L.-C., C.d.F., R.L.-R., J.A.-O., J.V.-Q.,
C.C.-Z., and L.A.A. discussed the results. R.L.-R., J.A.-O., J.V.-Q., and
L.A.A. performed a critical review of the manuscript. All authors read and
agreed to submit the manuscript for publication.
DECLARATION OF INTERESTS
The authors declare no competing interests.
INCLUSION AND DIVERSITY
Weworked to ensure gender balance in the recruitment of human subjects.We
worked to ensure ethnic or other types of diversity in the recruitment of human
subjects. We worked to ensure that the study questionnaires were prepared in
an inclusive way. One or more of the authors of this paper self-identifies as an
underrepresented ethnic minority in science. One or more of the authors of this
paper self-identifies as a member of the LGBTQ+ community. One or more of
the authors of this paper received support from a program designed to in-
creaseminority representation in science. The author list of this paper includes
contributors from the location where the research was conducted who partic-
ipated in the data collection, design, analysis, and/or interpretation of the
work.
Received: June 5, 2021
Revised: September 15, 2021
Accepted: December 16, 2021
Published: December 21, 2021
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OPEN ACCESSSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Anti-human CD45RA BUV395 (clone 5H9) BD Cat# 740315; RRID:AB_2740052
Anti-human CD16 BUV496 (clone 3G8) BD Cat# 612944; RRID:AB_2870224
Anti-human CCR5 BUV563 (clone 3A9) BD Cat# 741401; RRID:AB_2870893
Anti-human CD62L BUV615 (clone SK11) BD Cat# 751364; RRID:AB_2875371
Anti-CD11c BUV661 (clone B-Ly6) BD Cat# 612967; RRID:AB_2870241
Anti-human CCR7 BUB737 (clone 2-L1-A) BD Cat# 749676; RRID:AB_2873937
Anti-human CD56 BUB737 (clone NCAM
16.2)
BD Cat# 612766; RRID:AB_2813880
Anti-human CD8 BUV805 (clone SK1) BD Cat# 612889; RRID:AB_2833078
Anti-human IgD BV480 (clone IA6-2) BD Cat# 566138; RRID:AB_2739536
Anti-human IgG BV605 (clone G18-145) BD Cat# 563246; RRID:AB_2738092
Anti-human CXCR5 BV750 (clone RF8B2) BD Cat# 747111; RRID:AB_2871862
Anti-human CD141 BB515 (clone 1A4) BD Cat# 566017; RRID:AB_2739462
Anti-human CD127 APC/R700 (clone HIL-
7R-M21)
BD Cat# 565185; RRID:AB_2739099
Anti-human IL-2 APC/R700 (clone MQ1-
17H12)
BD Cat# 565136; RRID:AB_2739079
Anti-human CD163 BB790 (clone GHI/61) BD Cat# 624296; RRID:AB_2214940
Anti-human NKG2C BB700 (clone 134519) BD Cat# 748162; RRID: AB_2872623
Anti-human CD123 SuperBright436 (clone
6H6)
ThermoFisher Scientific Cat# 62-1239-42; RRID:AB_2662727
Anti-human CD161 eFluor 450 (clone HP-
3G10)
ThermoFisher Scientific Cat# 48-1619-42; RRID:AB_10854273
Anti-human CD8 Pacific Orange (clone 3B5) ThermoFisher Scientific Cat# MHCD0830; RRID:AB_10372066
Anti-human CD20 Pacific Orange (clone
2H7)
ThermoFisher Scientific Cat# MHCD2030; RRID:AB_10375578
Anti-human TCRgd PerCP-eFluor 710
(clone B1.1)
ThermoFisher Scientific Cat# 46-9959-42; RRID:AB_2573926
Anti-human CD25 PE-AlexaFluor700 (clone
CD25-3G10)
ThermoFisher Scientific Cat# MHCD2524; RRID:AB_2539740
Anti-human CCR7 BV421 (clone G043H7) Biolegend Cat# 353208; RRID:AB_11203894
Anti-human CD3 BV510 (clone OKT3) Biolegend Cat# 317332; RRID:AB_2561943
Anti-human CD3 BV570 (clone UCHT1) Biolegend Cat# 300436; RRID:AB_2562124
Anti-human CD4 BV570 (clone RPA-T4) Biolegend Cat# 300534; RRID:AB_2563791
Anti-human CD4 cFluor-YG584 (clone K3) Cytek Biosciences Cat# R7-20042; RRID: AB_2885083
Anti-human IgM BV570 (clone MHM-88) Biolegend Cat# 314517; RRID:AB_10913816
Anti-human CD28 BV650 (clone CD28.2) Biolegend Cat# 302946; RRID:AB_2616855
Anti-human TNFa BV650 (clone MAb11) BD Cat# 563418; RRID:AB_2738194
Anti-human CCR6 BV711 (clone G034E3) Biolegend Cat# 353436; RRID:AB_2629608
Anti-human IFNg BV711 (clone 4S.B3) Biolegend Cat# 502540; RRID:AB_2563506)
Anti-human PD-1 BV785 (clone EH12.2H7) Biolegend Cat# 329929; RRID:AB_11218984
Anti-human CD57 FITC (clone HNK-1) Biolegend Cat# 359604; RRID:AB_2562387
Anti-human CD3 SparkBlue550 (clone SK7) Biolegend Cat# 344852; RRID:AB_2819985
(Continued on next page)
Cell Reports 38, 110235, January 11, 2022 e1
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
Anti-human CD14 SparkBlue550 (clone
63D3)
Biolegend Cat# 367148; RRID:AB_2832724
Anti-human CD45 PerCP (clone 2D1) Biolegend Cat# 368506; RRID:AB_2566358
Anti-human CD4 PerCP/Cy5.5 (clone SK3) Biolegend Cat# 344608; RRID:AB_1953236
Anti-human CD11b PerCP/Cy5.5 (clone
ICRF44)
Biolegend Cat# 301328; RRID:AB_10933428
Anti-human PD-L1 PE (clone 10F.9G2) Biolegend Cat# 124308; RRID:AB_2073556
Anti-human CD24 PE/Dazzle594 (clone
ML5)
Biolegend Cat# 311134; RRID:AB_2566349
Anti-human CD95 PE/Cy5 (clone DX2) Biolegend Cat# 305610; RRID:AB_314548
Anti-humanCXCR3PE/Cy7 (cloneG025H7) Biolegend Cat# 353720; RRID:AB_11219383
Anti-human Granzyme B PE/Cy7 (clone
QA16A02)
Biolegend Cat# 372214; RRID:AB_2728381
Anti-human CD27 APC (clone M-T271) Biolegend Cat# 356410; RRID:AB_2561957
Anti-human CD1c AlexaFluor647 (clone
L161)
Biolegend Cat# 331510; RRID:AB_1186032
Anti-human CD19 SparkNIR685 (clone
HIB19)
Biolegend Cat# 302270; RRID:AB_2832581
Anti-human HLA-DR APC/Fire750 (clone
L243)
Biolegend Cat# 307658; RRID:AB_2572101
Anti-humanCD38 APC/Fire810 (clone HIT2) Biolegend Cat# 303550; RRID:AB_2860784
Biological samples
Blood samples of Healthy Health Personnel This paper N/A
Chemicals, peptides, and recombinant proteins
Ficoll-Plus GE Healthcare Cat# 17-1440-03
RPMI 1640 Medium Thermo Fisher Scientific Cat# 11594506
DMEM Medium Thermo Fisher Scientific Cat# 11965092
Phosphate buffer saline (PBS) Sigma Cat# P4417-100TAB
Bovine serum albumin (BSA) Sigma Cat# A9647-1KG
Foetal Bovine Serum (FBS) Thermo Fisher Scientific Cat# 11560636
Carboxyfluorescein succinimidyl ester
(CFSE)
Thermo Fisher Scientific Cat# C34554
PepTivator SARS-CoV-2 Prot_S Miltenyi Biotec Cat# 130-126-701
Protein Transport Inhibitor (Containing
Brefeldin A)
BD Cat# 555029
Protein Transport Inhibitor (Containing
Monensin)
BD Cat# 554724
LIVE/DEAD Fixable Blue Dead Cell Stain Kit Thermo Fisher Scientific Cat# L34962
True –Stain Monocyte Blocker Biolegend Cat# 426103
Brilliant Stain Buffer BD Cat# 566349
Dimethyl sulfoxide (DMSO) Sigma Cat# 67-68-5
Critical commercial assays
Cytofix/Cytoperm Fixation/
Permeabilization Kit
BD Cat# 554714
COVID-19 (SARS-CoV-2) quantitative IgG
ELISA
Demeditec Cat# DECOV1901Q
LEGENDplex SARS-CoV-2 Serological IgA
Panel (2-plex)
Biolegend Cat# 741139
LEGENDplex SARS-CoV-2 Serological IgG
Panel (3-plex)
Biolegend Cat# 741131
(Continued on next page)
e2 Cell Reports 38, 110235, January 11, 2022
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OPEN ACCESS
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
LEGENDplex SARS-CoV-2 Neut. Ab Assay
(1-plex)
Biolegend Cat# 741126
LEGENDplex HU Essential Immune
Response Panel (13-plex)
Biolegend Cat# 740930
SARS-CoV-2 Spike B Cell Analysis Kit,
human
Miltenyi Biotec Cat# 130-128-022
Experimental models: Cell lines
Vero E6 ATCC Cat# CRL-1586, RRID:CVCL_0574
Recombinant DNA
pNL4–3DenvRen This paper N/A
pcDNA3.1-SCoV2D19-D614 This paper N/A
p24Gag This paper N/A
Software and algorithms
LEGENDplex software v.8 Biolegend https://www.biolegend.com/en-us/
legendplex
Prism version 8.3 GraphPad https://www.graphpad.com/
scientific-software/prism/
FlowJo v.10.6.2 TreeStar https://www.flowjo.com/
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ll
OPEN ACCESSRESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact Eduardo
Lo´pez-Collazo (elopezc@salud.madrid.es).
Material availability
This study did not generate new unique reagents.
Data and code availability
d Data reported in this paper will be shared by the lead contact upon request.
d This paper does not report original code.
d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.EXPERIMENTAL MODEL AND SUBJECT DETAILS
Healthy health personnel volunteers and longitudinal samples
A total of 27 healthy health personnel volunteers of the Research Institution of La Paz University Hospital of Madrid (Spain) were
enrolled for this study before vaccination against Spike protein of SARS-CoV-2 (BNT162b2 SARS-CoV-2 mRNA vaccine of Pfizer
& BioNTech). Blood samples were taken at four times: 5 days before the vaccination (sample 0), 14 days after the first dose of vaccine
(sample 1), 14 days after the second dose of vaccine (sample 2) and 230 days after the second dose (sample 3) (Figure 1A). Informed
consent was obtained from all volunteers in accordance with the ethical standards and following the ethical guidelines of the 1975
Declaration of Helsinki. All healthy health personnel data were anonymized before study inclusion and their details are summarized in
Table S1.
Culture conditions of primary and Vero E6 cells
Fresh and thawed Peripheral Blood Mononuclear Cells (PBMCs) were cultured in RPMI 1640 medium containing 10% fetal
bovine serum (FBS), 25 mM HEPES, 2 mM L-glutamine and 1% Penicillin and Streptomycin Mix (Gibco) before some
stimulation to their activation or proliferation. PBMCs were cultured at 37 
C at 5% CO2 in a humidified incubator. Vero E6
were cultured in in DMEM containing 10% fetal bovine serum (FBS), 2 mM L-glutamine and 1% Penicillin and Streptomycin Mix
(Gibco).Cell Reports 38, 110235, January 11, 2022 e3
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OPEN ACCESSMETHOD DETAILS
PBMCs isolation, storage and thawing procedure
Peripheral bloodmononuclear cells (PBMCs) from healthy health personnel vaccinated with BNT162b2 SARS-CoV-2 mRNA vaccine
were isolated from EDTA anticoagulant venous blood using Ficoll-Plus (GE Healthcare Bio-Sciences) solution according to the man-
ufacturer’s instructions. PBMCs were washed twice with phosphate buffer saline (PBS) and counted using Trypan blue staining. A
part of cells was resuspended in two aliquots of 6 3 106 cells in fetal bovine serum (FBS) containing 10% DMSO (Sigma-Aldrich).
Then, aliquoted PBMCs were slowly frozen (1
C/minute) using a controlled-grade freezing device (Mr. Frosty, ThermoFisher Sci-
entific) and stored for 24 hours at80 ⁰Cbefore storage in liquid nitrogen. For use PBMCs, they were rapidly thawed in awater bath at
37
C and washed twice with RPMI 1640 medium containing 10% fetal bovine serum (FBS), 25 mM HEPES, 2 mM L-glutamine and
1% Penicillin and Streptomycin Mix (PenStrep, Gibco).
Plasma collection
Plasma samples from healthy health personnel vaccinated with the Pfizer vaccine against SARS-CoV-2 were obtained from EDTA
anticoagulant venous blood using Ficoll-Plus (GE Healthcare Bio-Sciences) solution according to standard density gradient centri-
fugation method. Then, they were aliquoted and stored at 80
C until use.
Algorithm for dimensionality reduction
UniformManifold Approximation andProjection (UMAP) analysiswas carried out using allmarkers listed in Table S2.Dataweremanu-
ally gated to remove aggregates, dead cells, debris, and CD45 negative events, and then they were sub-sampled to include 60% of
CD45+ live singlets from each sample. Subsequently, the UMAP analysis was performed to visualize the different subpopulations in
groups. CD3+ and CD19+ subpopulations were defined as CD3+/TCRgd/CD56-/CD14-/CD4+ or CD8+, and CD3-/TCRgd/CD56-/
CD14-/CD19+/CD20+/, respectively, prior to the UMAP analysis. UMAP settings for CD3+ subpopulation used CD45RA, CD28,
CCR7, PD-1,CD27,CD57,CD127,CD25,CD95,CD38andHLA-DRfluorescent parameters. UMAPsettings forCD19+ subpopulation
usedCD38, CD27, CD19, CD24, IgD, IgM, IgG andCD20 fluorescent parameters. All fluorescent parameters were used besides lived
and CD45+ cells. UMAP was run using 15 nearest neighbors, a minimal distance of 0.5, in 2-dimensions and Euclidean distance and
spectral initialization mode (McInnes et al., 2020). Data were analyzed using FlowJo (TreeStar) v10.6.2 software.
Detection of anti-spike IgA and IgG SARS-CoV-2 antibodies
For detection of specific antibodies IgA and IgG against the Spike protein of SARS-CoV-2, reserved plasma samples from healthy
health personnel vaccinated with the Pfizer vaccine against SARS-CoV-2 stored at80
Cwere thawed and centrifuged at 1000 rela-
tive centrifugal force for 30 minutes to remove particulates prior to use. The title of IgA antibodies in plasma samples were performed
by the bead-based multiplex assay, LEGENDplex SARS-CoV-2 Serological IgA Panel (2-plex, Spike (S1) and receptor binding
domain (RBD) of Spike protein) (Biolegend) according to the manufacturer’s instructions. The title of IgG antibodies in plasma
samples were performed by the bead-based multiplex assay, LEGENDplex SARS-CoV-2 Serological IgG Panel (3-plex, Spike
(S1), receptor binding domain (RBD) of Spike protein and nucleocapsid (N)) (Biolegend) according to themanufacturer’s instructions.
Samples were acquired on a FACSCalibur flow cytometer (BD Biosciences) and data were analyzed using LEGENDplex (Biolegend)
v.8 software. For quantification of IgG antibodies against full Spike protein of SARS-CoV-2, COVID-19 quantitative IgG ELISA kit from
Demeditec Diagnostics GmbH (Ref.: DECOV1901Q) was used. Data obtained were corroborated by Eurofins-Ingenasa kits: INGE-
ZIM-NP-COVID 19 DR and INGEZIM-RBD-COVID 19 DR.
Detection of neutralization capacity of plasma against SARS-CoV-2 Spike antigen
The neutralizing antibodies in plasma samples were performed by a competitive immunoassay of ACE2-conjugated beads,
LEGENDplex SARS-CoV-2 Neut. Ab Assay (1-plex) according to the manufacturer’s instructions.
Tomeasure neutralising antibodies titres bymeans of viral pseudoparticles, diluted plasma samples were preincubated with pseu-
doviruses generated by co-transfection of the plasmid pNL4–3DenvRen and an expression vector for the viral SARS-CoV-2 Spike
(pcDNA3.1-SCoV2D19-D614) and added at a concentration of 10 ng p24Gag per well to Vero E6 cells in 96-well plates. At 48 h
post infection, viral infectivity was assessed by measuring luciferase activity (Renilla Luciferase Assay (Promega, Madison, WI,
USA) using a 96-well plate luminometer LB 960 Centro XS (Berthold Technologies, Oak Ridge, TN, USA). The titre of neutralising an-
tibodies was calculated as 50% inhibitory dose (neutralising titre 50, NT50), expressed as reciprocal of four-fold serial dilution of heat-
inactivated sera (range 1:32–1:8192), resulting in a 50% reduction of pseudovirus infection compared with control without serum.
Samples below the detection threshold (1:32 serum dilution) were given 1:16 value. Positive and negative controls were included
in the assay and non-specific neutralisation was assessed using a nonrelated pseudovirus expressing the vesicular stomatitis virus
envelope.
Antibodies and immunophenotyping by flow cytometry
Stored PBMCs were thawed as we have described above and they were rested in RPMI 1640 medium containing 10% fetal bovine
serum (FBS), 25 mM HEPES, 2 mM L-glutamine and 1% Penicillin and Streptomycin Mix (Gibco) for 1 hour previous the staininge4 Cell Reports 38, 110235, January 11, 2022
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OPEN ACCESSprotocol. Then, PBMCs were stained with fluorochrome-conjugated antibodies to a multi-colour panel of surface markers listed in
Table S2. Dead cells were excluded using LIVE/DEADBlue fluorescent reactive dye purchased from Invitrogen and True-StainMono-
cyte Blocker (BioLegend) reagent was added prior to the label protocol to block the nonspecific binding of some fluorochromes on
monocytes. Labeled cells were acquired on a Cytek Aurora Spectral Cytometer (Cytek Biosciences). Data were analyzed using
FlowJo (TreeStar) v10.6.2 software.
SARS-CoV-2 Spike-specific B cells detection
SARS-CoV-2 Spike-specific B cells were detected in sample 3 by means of the SARS-CoV-2 Spike B cell analysis kit provided by
Miltenyi Biotec, following the manufacturer’s instructions. Labeled cells were acquired on a Cytek Aurora Spectral Cytometer (Cytek
Biosciences). Data were analyzed using FlowJo (TreeStar) v10.6.2 software.
SARS-CoV-2 Spike-specific T cell proliferation assays and supernatant collection
Fresh PBMCs from healthy health personnel 14 (sample 2) and 230 (sample 3) days after the second dose of BNT162b2 SARS-CoV-2
mRNA vaccine isolated from EDTA anticoagulant venous blood using Ficoll-Plus (GE Healthcare Bio-Sciences) were washed twice
with phosphate buffer saline (PBS) and counted using Trypan blue staining. Carboxyfluorescein succinimidyl ester (CFSE) was pur-
chased from Thermo Fisher Scientific and used following themanufacturer’s protocol to assess T lymphocyte proliferation. After that,
living CFSE-labeled PBMCs were plated in RPMI 1640 medium containing 10% fetal bovine serum (FBS), 25 mM HEPES, 2 mM
L-glutamine and 1% Penicillin and Streptomycin Mix (Gibco) in a 96-wells plate flat bottom (1,5 3 106 cells/well) and stimulated
or not with Peptivator SARS-CoV-2 Prot_S (Miltenyi Biotec) for 5 days at 37
C at 5% CO2. After proliferation assay, supernatants
were collected, aliquoted and stored at 80
C until use. Then, PBMCs were washed and stained with fluorochrome-conjugated
antibodies to surface markers listed in Table S3.
Supernatant soluble cytokine quantification
Reserved and stored supernatants of PBMCs from healthy health personnel 14 (sample 2) and 230 (sample 3) days after the second
dose of BNT162b2 SARS-CoV-2 mRNA vaccine, stimulated with Peptivator SARS-CoV-2 Prot_S (Miltenyi Biotec) for 5 days, were
thawed. The concentration measurements of cytokines in supernatant samples was performed by the bead-based multiplex assay,
LEGENDplex Human Essential Immune Response Panel (13-plex: IL-1b, IL-2, IL-4, IFN-g, TNF-a, MCP-1 (CCL2), CXCL10, IL-6, IL-8
(CXCL8), IL-10, IL-12p70, IL-17A and Free Active TGF-b1), according to themanufacturer’s instructions. Samples were acquired on a
FACSCalibur flow cytometer (BD Biosciences) and data were analyzed using LEGENDplex (BioLegend) v.8 software.
PBMCs stimulation and intracellular cytokine staining
Thawed PBMCswere stimulated with Peptivator SARS-CoV-2 Prot_S (Miltenyi Biotec) consisting in a pool of 15-mer sequences with
11 amino acids overlap covering the immunodominant sequence domains of the Spike glycoprotein of SARS-CoV-2. Incubation was
performed for 6 hours at 37
C 5% CO2 in presence of Golgi-Plug containing Brefeldin A (BD) and Golgi-Stop containing Monensin
(BD) added after 1 hour of the stimulation according to the manufacturer’s instructions. After that, PBMCs were washed and stained
with the surface markers (listed in Table S4) for 30 minutes at room temperature, twice washed, fixed and permeabilized using the
Cytofix/Cytoperm Fixation/Permeabilization Kit (BD) according the manufacturer’s instructions. Subsequently, the fixed and per-
meabilized PBMCs were staining using fluorochrome-conjugated antibodies against intracellular makers listed in Table S4. Labeled
cells were acquired on a Cytek Aurora Spectral Cytometer (Cytek Biosciences). Data were analyzed using FlowJo (TreeStar) v10.6.2
software.
QUANTIFICATION AND STATISTICAL ANALYSIS
Statistical analysis of biological data
Data are expressed as violin and box plots with mean and interquartile ranges, mean ± SEM, and single dots representing an indi-
vidual subject each. D’Agostino & Pearson Normality test was performed to all the studied variables. Student’s t-test for two groups
comparison of quantitative variables, either unpaired (t-test or Mann-Whitney) or paired (t-test or Wilcoxon), and ANOVA or Kruskal-
Wallis for multiple groups comparisons of quantitative variables were performed. Correlation between quantitative variables
were evaluated by Spearman’s analysis. All along figures, p-values (P) are denoted as ns: non-significant, *P < 0.05, **P < 0.01,
***P < 0.001, ****P < 0.0001.
In order to perform a visual correlation analysis between the expression of different immune factors, and COVID-19 history of the
subjects, the raw data of each one was normalized using the Z-Score strategy ((value-m)/s). The hierarchical clustering analysis was
developed by heatmap, geom tile and ggplot2 packages (version 1.16.0) in R language (version 4.0.2). This package is available at
https://www.r-graph-gallery.com/heatmap. The clustering was analyzed and distributed by average linkage method, in which the
distance between two clusters is defined as the mean of distances between all pairs of objects, where each pair is made up of
one object from each group. Measurement method between rows and columns was performed by Manhattan method.Cell Reports 38, 110235, January 11, 2022 e5
Cell Reports, Volume 38Supplemental informationCellular and humoral functional responses
after BNT162b2 mRNA vaccination differ longitudinally
between naive and subjects recovered from COVID-19
Roberto Lozano-Rodríguez, Jaime Valentín-Quiroga, José Avendaño-Ortiz, Alejandro
Martín-Quirós, Alejandro Pascual-Iglesias, Verónica Terrón-Arcos, Karla Montalbán-
Hernández, José Carlos Casalvilla-Dueñas, Marta Bergón-Gutiérrez, José Alcamí, Javier
García-Pérez, Almudena Cascajero, Miguel Ángel García-Garrido, Álvaro del Balzo-
Castillo, María Peinado, Laura Gómez, Irene Llorente-Fernández, Gema Martín-
Miguel, Carmen Herrero-Benito, José Miguel Benito, Norma Rallón, Carmen Vela-
Olmo, Lissette López-Morejón, Carolina Cubillos-Zapata, Luis A. Aguirre, Carlos del
Fresno, and Eduardo López-Collazo
1 
 
SUPPLEMENTAL INFORMATION 
 
Cellular and humoral functional responses after BNT162b2 mRNA vaccination 
differ longitudinally between naïve and COVID-19-recovered individuals 
 
Roberto Lozano-Rodríguez, Jaime Valentín-Quiroga, José Avendaño-Ortiz, Alejandro Martín-
Quirós, Alejandro Pascual-Iglesias, Verónica Terrón, Karla Montalbán-Hernández, José Carlos 
Casalvilla-Dueñas, Marta Bergón-Gutierrez, José Alcamí, Javier García-Pérez, Almudena 
Cascajero, Miguel Ángel García-Garrido, Álvaro del Balzo-Castillo, María Peinado, Laura 
Gómez, Irene Llorente-Fernández, Gema Martín-Miguel, Carmen Herrero-Benito, José Miguel 
Benito, Norma Rallón, Carmen Vela-Olmo, Lissette López-Morejón, Carolina Cubillos-Zapata, 
Luis A. Aguirre, Carlos del Fresno and Eduardo López-Collazo 
 
  
2 
 
SUPPLEMENTARY FIGURES 
Supplementary Figure 1 
 
Figure S1. Neutralizing capacity and B cell subpopulation frequencies after 
vaccination in naïve and COVID-19-recovered individuals in sample 2. (A) Functional 
neutralizing antibody titles on plasma of naïve and COVID-19-recovered individuals against 
Spike expressing-pseudovirus. Results are expressed as the dilution of sera that inhibited infection 
by 50% (NT50). (B) Frequency of B cells (CD19+CD20+) gated on live leucocytes (CD45+) in 
naïve and COVID-19-recovered individuals. Frequency of CD24+CD38+ B regs (C), CD27+IgD-
B cells (D), naïve CD27-IgD+ B cells (E), switched CD27+IgD- B cells (F), not switched 
CD27+IgD+ B cells (G) and CD38+CD27+ plasma cells (H) gated on CD19+CD20+ B cells in naïve 
and COVID-19-recovered individuals. (A-H) Data shown as mean ± SEM. (A-H) Unpaired 
Student’s t-test (ns, not significant; ****, P<0.0001). 
 
  
3 
 
Supplementary Figure 2 
 
Figure S2. T cell phenotype and subpopulation frequencies after vaccination in naïve 
and COVID-19-recovered individuals in sample 2. (A) Uniform Manifold Approximation 
and Projection (UMAP) of CD4+ T cells from naïve and COVID-19-recovered individuals 
followed by manual gating to identify naïve (CD45RA+CCR7+), central memory (CM) (CD45RA-
CCR7+), effector memory cells re-expressing CD45RA (EMRA) (CD45RA+CCR7-), effector 
memory (EM) (CD45RA-CCR7-), regulatory (Tregs) (CD25+CD127-) and follicular helper (Tfh) 
(CXCR5+CD45RA-) T cells. (B) UMAP clustering expressions of CD95, CD25 and CCR7 on 
CD4+ T cells from naïve and COVID-19-recovered individuals. (C) Frequency of total and 
different CD4+ T cell subsets in naïve and COVID-19-recovered individuals. (D) UMAP of CD8+ 
T cells from naïve and COVID-19-recovered individuals followed by manual gating to identify 
naïve (CD45RA+CCR7+), central memory (CM) (CD45RA-CCR7+), effector memory cells re-
expressing CD45RA (EMRA) (CD45RA+CCR7-) and effector memory (EM) (CD45RA-CCR7-). 
(E) UMAP clustering expressions of CD95, CD25 and CCR7 on CD8+ T cells from naïve and 
COVID-19-recovered individuals. (F) Frequency of total and different CD8+ T cell subsets in 
naïve and COVID-19-recovered individuals. Expression levels of CD28, CD57, CD95 and PD-1 
in CD4+ (G) and CD8+ (H) T cells from naïve and COVID-19-recovered individuals. (C, F-H) 
Data shown as mean ± SEM. (C, F-H) Unpaired Student’s t-test (*, P<0.05). 
4 
 
Supplementary Figure 3 
 
Figure S3. Gating strategies. (A) Representative gating to identify SARS-CoV-2 S-protein-
specific B cells (CD19+) in PBMCs from sample 3 (230 days after the second dose of the 
BNT162b2 vaccine). PBMCs were labelled with CFSE and stimulated with SARS-CoV-2 spike 
peptide pool for 5 days. CD4+ T cells were classified according to their proliferative response as 
“Proliferative” (CFSEdim) or “Non-proliferative” (CFSEbright). Memory subpopulations were 
analyzed (Naïve; central memory, CM; effector memory cells re-expressing CD45RA, EMRA; 
effector memory, EM). (B) Representative gating strategy to identify memory subpopulations in 
proliferative (CFSEdim) and non-proliferative (CFSEbright) SARS-CoV-2 Spike-specific CD4+ T 
cells in sample 2 (left, 14 days after the second dose) and sample 3 (right, 230 days after the 
second dose). (C) Representative gating strategy to identify memory subpopulations in 
proliferative (CFSEdim) and non-proliferative (CFSEbright) SARS-CoV-2 Spike-specific CD8+ T 
cells in sample 2 (left, 14 days after the second dose) and sample 3 (right, 230 days after the 
second dose).   
5 
 
Supplementary Figure 4 
 
Figure S4. SARS-CoV-2 Spike protein-specific intracellular cytokines production 
by T cells from naïve and COVID-19-recovered individuals. Frequency of TNFα+ (A), 
IFNγ+ (B) and Granzyme-B+ (C) CD4+ (left panel) and CD8+ (middle panel) T cells from naïve 
and COVID-19-recovered individuals, stimulated or not with a SARS-CoV-2 Spike peptide pool 
for 6 hours of sample 2 (14 days after second dose) and sample 3 (more than seven months after 
second dose). Increment of TNFα+ (A), IFNγ+ (B) and Granzyme-B+ (C) cells (right panels) 
comparing SARS-CoV-2 Spike pool-stimulated and non-stimulated CD4+ and CD8+ T cells from 
naïve and COVID-19-recovered individuals of sample 2 and sample 3. Each dot represents an 
individual. (Left and middle panels) Paired Student’s test (ns, not significant; * P<0.05; **, 
P<0.01; ***, P<0.001; ****, P<0.0001). (Right panels) Mann Whitney test  (ns, not significant; 
*, P<0.05) 
 
 
 
  
6 
 
Supplementary Figure 5 
 
Figure S5. Memory populations in SARS-CoV-2 spike-specific CD8+ T cells after 
BNT162b2 mRNA vaccination in naïve and COVID-19-recovered individuals. 
PBMCs were labelled with CFSE and stimulated with SARS-CoV-2 spike peptide pool for 5 days. 
CD8+ T cells were classified according to their proliferative response as “Proliferative” (CFSEdim) 
or “Non-proliferative” (CFSEbright). Memory subpopulations were analyzed (Naïve; central 
memory, CM; effector memory cells re-expressing CD45RA, EMRA; effector memory, EM). 
Frequencies (A) and mean distribution (B) of memory populations in “Proliferative” (○, CFSEdim) 
and non-proliferative (□, CFSEbright) CD8+ T cells from naïve and COVID-19-recovered 
individuals in sample 2 (14 days after second dose) and sample 3 (more than seven months after 
second dose). (C) “Proliferative” (CFSEdim) versus “Non-proliferative” (CFSEbright) ratio of CD8+ 
T cell memory populations from naïve and COVID-19-recovered individuals in sample 2 (Δ ) and 
sample 3 (◊). (D) Frequency of IL-2+ cells in gated naïve, CM, EMRA and EM CD8+ T cells 
stimulated or not with SARS-CoV-2 spike peptide pool for 6 hours in sample 2 and sample 3. (E) 
Increment of frequencies of IL-2+ cells comparing SARS-CoV-2 spike pool-stimulated and non-
stimulated CD8+ T cell subpopulations, naïve and CM (upper panel), EMRA and EM (lower 
panel), from naïve and COVID-19-recovered individuals in sample 2 and sample 3. (A, C-E) Each 
dot represents an individual. (A, C-D) Paired Student’s t-test (ns, not significant; *, P<0.05; **, 
P<0.01; ****, P<0.0001). (E) Mann Whitney t test (ns, not significant; **, P<0.01). 
7 
 
SUPPLEMENTARY TABLES 
 
Table S1. Demographics and laboratory findings from naïve and COVID-19-
recovered individuals  
 All HVs 
(n=27) 
Naïve 
(n=16) 
Recovered 
(n=11) 
P-value 
Age – years 42.37±14.74 46.44±16.02 36.45±10.72 0.069 
Sex, male – n (%) 10 (58.82) 5 (50.00) 5 (71.43) 0.484 
Laboratory findings – n, (%)     
Red blood cells – Counts/cm3 5260±690 5380±780 5080±510 0.5037 
Haemoglobin – g/dL 15.68±2.13 15.78±2.34 15.53±1.89 0.8748 
Haematocrit – (%) 48.87±6..67 49.66±7.42 47.72±5.54 0.9318 
White-cells  – Counts/cm3 6.73±1.71 6.32±1.27 7.33±2.12 0.087 
ALC – Counts/cm3 2.29±0.68 2.18±0.77 2.44±0.52 0.107 
ANC – Counts/cm3 3.71±1.42 3.25±0.93 4.37±1.77 0.069 
AMC – Counts/cm3 0.38±0.10 0.37±0.10 0.40±0.11 0.761 
AEC – Counts/cm3 0.27±0.21 0.22±0.14 0.35±0.27 0.288 
ABS – Counts/cm3 0.07±0.02 0.06±0.02 0.08±0.02 0.056 
Platelet counts 244.33±57.92 228.75±55.03 267.00±56.81 0.068 
Ratio N/L 1.73±0.87 1.60±0.63 1.92.±1.14 0.8177 
 
Data are expressed as mean±SD or number (percentage) 
  
8 
 
Table S2. List of all fluorochrome-conjugated monoclonal antibodies for flow 
cytometry analysis. 
 
 
 
Marker Fluorochrome Source Clone Reference  
CD45RA BUV395 BD 5H9 Cat# 740315 
CD16  BUV496 BD 3G8 Cat# 612944 
CCR5 BUV563 BD 3A9 Cat# 741401 
CD62L  BUV615 BD SK11 Cat# 751364 
CD11c BUV661 BD B-Ly6 Cat# 612967 
CD56 BUV737 BD NCAM 16.2 Cat# 612766 
CD8 BUV805 BD SK1 Cat# 612889 
IgD  BV480 BD IA6-2 Cat# 566138 
IgG BV605 BD G18-145 Cat# 563246 
CXCR5 BV750 BD RF8B2 Cat# 747111 
CD141 BB515 BD 1A4 Cat# 566017 
CD127 APC-R700 BD HIL-7R-M21 Cat# 565185 
CD163 BB790 BD GHI/61 Cat# 624296 
NKG2C BB700 BD 134591 BD OptiBuild 
CD123 Super Bright 436 ThermoFisher Scientific 6H6 Cat# 62-1239-42 
CD161 eFluor 450 ThermoFisher Scientific HP-3G10 Cat# 48-1619-42 
CD20 Pacific Orange ThermoFisher Scientific 2H7 Cat# MHCD2030 
TCRγδ PerCP-eFluor 710 ThermoFisher Scientific B1.1 Cat# 46-9959-42 
CD25  PE-Alexa Fluor 700 ThermoFisher Scientific CD25-3G10 Cat# MHCD2524 
CCR7 BV421 Biolegend G043H7 Cat# 353208 
CD3  BV510 Biolegend OKT3 Cat# 317332 
IgM  BV570 Biolegend MHM-88 Cat# 314517 
CD28 BV650 Biolegend CD28.2 Cat# 302946 
CCR6 BV711 Biolegend G034E3 Cat# 353436 
PD-1 BV785 Biolegend EH12.2H7 Cat# 329929 
CD57  FITC Biolegend HNK-1 Cat# 359604 
CD14 Spark Blue™ 550 Biolegend 63D3 Cat# 367148 
CD45  PerCP Biolegend 2D1 Cat# 368506 
CD11b PerCP/Cyanine5.5 Biolegend ICRF44 Cat# 301328 
PD-L1 PE Biolegend 10F.9G2 Cat# 124308 
CD24 PE/Dazzle™ 594 Biolegend ML5 Cat# 311134 
CD95 PE/Cy5 Biolegend DX2 Cat# 305610 
CXCR3 PE/Cy7 Biolegend G025H7 Cat# 353720 
CD27  APC Biolegend M-T271 Cat# 356410 
CD1c  Alexa Fluor® 647 Biolegend L161 Cat# 331510 
CD19  Spark NIR™ 685 Biolegend HIB19 Cat# 302270 
HLA-DR  APC/Fire™ 750 Biolegend L243 Cat# 307658 
CD38 APC-Fire810 Biolegend HIT2 Cat# 303550 
CD4 cFluor-YG584 Cytek Biosciences SK3 Cat# R7-20042 
9 
 
Table S3. List of fluorochrome-conjugated monoclonal antibodies for T cell 
proliferation assay by flow cytometry analysis  
 
Marker Fluorochrome Source Clone Reference 
CD3 BV510 Biolegend OKT3 Cat# 317332 
CD4 cFluor-YG584 Cytek Biosciences SK3 Cat# R7-20042 
CD8 BUV805 BD SK1 Cat# 612889 
CD45 PerCP Biolegend 2D1 Cat# 368506 
CD45RA BUV395 BD 5H9 Cat# 740315 
CD62L BV615 BD SK11 Cat# 751364 
CD28 BV650 Biolegend CD28.2 Cat# 302946 
CCR7 BV421 Biolegend G043H7 Car# 353208 
 
 
Table S4. List of fluorochrome-conjugated monoclonal antibodies for extracellular 
and intracellular staining for T cell stimulation by flow cytometry analysis  
 
Extracellular Marker Fluorochrome Source Clone Reference 
CD3 BV570 Biolegend UCHT1 Cat# 300436 
CD4 PerCP/Cy5.5 Biolegend SK3 Cat# 344608 
CD8 BUV805 BD SK1 Cat# 612889 
CD45RA BUV395 BD 5H9 Cat# 740315 
CCR7 BUV737 BD 2-L1-A Cat# 749676 
 
Intracellular Marker Fluorochrome Source Clone Reference 
Granzyme B PE/Cy7 Biolegend QA16A02 Cat# 372214 
IFNγ BV711 Biolegend 4S.B3 Cat# 502540 
IL-2 APC-R700 BD MQ1-17H12 Cat# 565136 
TNFα BV650 BD MAb11 Cat# 563418