Jackson S Turner1, Julian Q Zhou2, Julianna Han3, Aaron J Schmitz1, Amena A Rizk1, Wafaa B Alsoussi1, Tingting Lei1, Mostafa Amor1, Katherine M McIntire1, Philip Meade4,5, Shirin Strohmeier4, Rafael I Brent1, Sara T Richey3, Alem Haile6, Yuhe R Yang3, Michael K Klebert6, Teresa Suessen7, Sharlene Teefey7, Rachel M Presti8, Florian Krammer4, Steven H Kleinstein2,9, Andrew B Ward3, Ali H Ellebedy10,11. 1. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. 2. Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. 3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA. 4. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 5. Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 6. Clinical Trials Unit, Washington University School of Medicine, St Louis, MO, USA. 7. Department of Radiology, Washington University School of Medicine, St Louis, MO, USA. 8. Department of Internal Medicine-Infectious Diseases, Washington University School of Medicine, St Louis, MO, USA. 9. Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA. 10. Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. ellebedy@wustl.edu. 11. The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St Louis, MO, USA. ellebedy@wustl.edu.
Abstract
Influenza viruses remain a major public health threat. Seasonal influenza vaccination in humans primarily stimulates pre-existing memory B cells, which differentiate into a transient wave of circulating antibody-secreting plasmablasts1-3. This recall response contributes to 'original antigenic sin'-the selective increase of antibody species elicited by previous exposures to influenza virus antigens4. It remains unclear whether such vaccination can also induce germinal centre reactions in the draining lymph nodes, where diversification and maturation of recruited B cells can occur5. Here we used ultrasound-guided fine needle aspiration to serially sample the draining lymph nodes and investigate the dynamics and specificity of germinal centre B cell responses after influenza vaccination in humans. Germinal centre B cells that bind to influenza vaccine could be detected as early as one week after vaccination. In three out of eight participants, we detected vaccine-binding germinal centre B cells up to nine weeks after vaccination. Between 12% and 88% of the responding germinal centre B cell clones overlapped with B cells detected among early circulating plasmablasts. These shared B cell clones had high frequencies of somatic hypermutation and encoded broadly cross-reactive monoclonal antibodies. By contrast, vaccine-induced B cell clones detected only in the germinal centre compartment exhibited significantly lower frequencies of somatic hypermutation and predominantly encoded strain-specific monoclonal antibodies, which suggests a naive B cell origin. Some of these strain-specific monoclonal antibodies recognized epitopes that were not targeted by the early plasmablast response. Thus, influenza virus vaccination in humans can elicit a germinal centre reaction that recruits B cell clones that can target new epitopes, thereby broadening the spectrum of vaccine-induced protective antibodies.
Influenza viruses remain a major public health threat. Seasonal influenza vaccination in humans primarily stimulates pre-existing memory B cells, which differentiate into a transient wave of circulating antibody-secreting plasmablasts1-3. This recall response contributes to 'original antigenic sin'-the selective increase of antibody species elicited by previous exposures to influenza virus antigens4. It remains unclear whether such vaccination can also induce germinal centre reactions in the draining lymph nodes, where diversification and maturation of recruited B cells can occur5. Here we used ultrasound-guided fine needle aspiration to serially sample the draining lymph nodes and investigate the dynamics and specificity of germinal centre B cell responses after influenza vaccination in humans. Germinal centre B cells that bind to influenza vaccine could be detected as early as one week after vaccination. In three out of eight participants, we detected vaccine-binding germinal centre B cells up to nine weeks after vaccination. Between 12% and 88% of the responding germinal centre B cell clones overlapped with B cells detected among early circulating plasmablasts. These shared B cell clones had high frequencies of somatic hypermutation and encoded broadly cross-reactive monoclonal antibodies. By contrast, vaccine-induced B cell clones detected only in the germinal centre compartment exhibited significantly lower frequencies of somatic hypermutation and predominantly encoded strain-specific monoclonal antibodies, which suggests a naive B cell origin. Some of these strain-specific monoclonal antibodies recognized epitopes that were not targeted by the early plasmablast response. Thus, influenza virus vaccination in humans can elicit a germinal centre reaction that recruits B cell clones that can target new epitopes, thereby broadening the spectrum of vaccine-induced protective antibodies.
Seasonal influenza viruses kill 290,000 to 650,000 people globally every
year[6]. As the virus drifts,
novel antigenic targets emerge, creating a pressing need for the annual vaccine to
engage new B cell clones that recognize such targets. The germinal centre (GC)
reaction is critical for generating high-affinity and durable B cell
responses[5]. It is currently
unknown whether seasonal influenza virus immunization of humans can elicit a GC
response in the draining lymph nodes (LN) where diversification and maturation of
recruited B cells can occur. Studies examining human B cell responses have
traditionally focused on sampling the easily accessible blood compartment, but
ultrasound-guided fine needle aspiration (FNA) has enabled sampling of LNs with good
representation of cell populations recovered by excisional biopsy, including GC B
cells[7-9].
Results
Vaccine-induced B cell responses in blood and lymph nodes
Eight healthy young adults were enrolled in a seasonal influenza
vaccination study. Blood and FNA specimens were collected prior to vaccination
and at 1, 2, approximately 4, and 9 weeks after vaccination with the 2018/2019
quadrivalent inactivated influenza virus vaccine (QIV) (Fig. 1a). QIV-binding antibody-secreting PBs were
measured in blood by enzyme-linked immune absorbent spot (ELISpot). PBs peaked
in blood during the first week after vaccination in all participants, with the
frequency varying from 160 to 3,400 IgG-secreting QIV-binding PBs per mL (Fig. 1b, Extended Data Fig. 1a). Haemagglutinin (HA)-binding PB were also
measured by flow cytometry and peaked 1-week post-vaccination (CD20lo
HA+) and activated B cells (ABC, CD20hi
HA+) peaked during the second week before declining (Fig. 1d, Extended Data
Fig. 1b,f)[10]. Four weeks after vaccination, anti-QIV
IgG plasma antibody titers were elevated compared to those at baseline as
measured by enzyme-linked immunosorbent assay (ELISA), along with
haemagglutination-inhibiting antibody titers against the four constituent
viruses of the vaccine as measured by the haemagglutination inhibition (HAI)
assay (Extended Data Fig. 1g, h).
Figure 1.
Robust peripheral B cell response to influenza virus vaccination.
a) Study design. Eight healthy adults (ages 26–40) who had not
been vaccinated against influenza for 3 years were enrolled and received
quadrivalent inactivated vaccine (QIV) i.m. Blood and fine needle aspirates
(FNAs) of ipsilateral axillary lymph nodes were collected pre-vaccination and at
1, 2, approximately 4, and 9 weeks after vaccination. b) ELISpot quantification
of QIV-binding IgG-secreting plasmablasts (PBs) in blood at baseline, 1, and 2
weeks post-vaccination. c, d) Representative gating (c) and kinetics (d) of
HA-binding activated B cells (ABCs, CD20+ HA+, closed
circles) and PBs (CD20lo HA+, open triangles) in PBMC.
Cells pre-gated IgD− CD19+ CD4−
live singlet lymphocytes. Symbols at each timepoint in b and d represent one
sample (n=8).
Extended Data Figure 1.
Robust peripheral B cell response to influenza virus vaccination.
a) ELISpot quantification of QIV-binding IgG-, IgM-, and
IgA-secreting QIV-binding PBs 1 -week post-vaccination. Each symbol
represents one participant (n=8). b-e) Flow cytometry (b, c) and sorting (d,
e) gating strategies for PBMC (b, d) and FNA (c, e). Population counts per
mL of blood and frequencies are presented in f, below and in Figs. 1d, 2f,
and Extended Data Fig. 2c, m. f) Kinetics of HA-binding PBs
(CD20lo HA+, open triangles) and activated B cells
(ABCs, CD20+ HA+, closed circles) in PBMCs, gated as
in Fig. 1c, pre-gated IgDlo
CD19+ CD4− live singlet lymphocytes as in
(b). Symbols at each timepoint represent one sample (n=8). g, h) IgG plasma
antibody titers against QIV and Tetanus/Diptheria vaccine measured by ELISA
(g) and hemagglutination inhibition titers against QIV constituent viruses
(h) pre- and 4-weeks post-vaccination. Symbols at each timepoint represent
one sample (n = 8). Horizontal lines in a, g, and h
represent means. Dotted lines represent limit of detection.
P-values from paired two-sided Student’s
t -tests.
In humans, QIV is injected into the deltoid muscle of the upper arm. This
area primarily drains into the lateral axillary LNs[11,12]. Both axilla of each participant were examined by
ultrasound before vaccination to identify an accessible LN. Once identified, the
hypoechoic LN cortex, which contains B cell follicles, was sampled by FNA (Fig. 2a), and QIV was administered in the
ipsilateral muscle. The same LN was serially sampled, and its dimensions and
cortical thickness were measured. LN cortical thickening following vaccination
was observed in participants 04, 05, 09, and 11 (Fig. 2b, Extended Data Fig.
2a). The median number of live cells recovered per FNA was
8.9×105 (range, 2.3×104 to
6.9×106; Extended Data Fig.
2b). Flow cytometric analysis of FNA detected significantly higher
frequencies of B cells and CD4+ T cells and lower frequencies of
CD14+ monocytes or granulocytes than those in peripheral blood
mononuclear cells (PBMCs) (Fig. 2c, Extended Data Fig. 1b). These trends were
observed in FNA specimens from all participants except for participant 02 where
the frequencies of CD14+ cells were consistently high, suggesting
significant blood contamination (Extended Data
Fig. 2c). FNA data from specimens in which fewer than
4×104 live cells were recovered or the CD14+
percentage exceeded 10% of CD45+ were excluded from further analyses,
including all FNA specimens from participant 02.
Figure 2.
Defining influenza virus vaccine-induced GC B cell response in
humans.
a) Representative sonogram of FNA; note hyperechoic medulla
“m” and needle sampling hypoechoic cortex “c” from
top left. b) Representative sonograms of axillary LN before each FNA. c)
Percentages of CD19+ CD4− B cells (left),
CD14− CD4+ T cells (middle), and
CD14+ CD4− monocytes or granulocytes (right) of
CD45+ in paired PBMC (red) and FNA (blue) samples measured by
flow cytometry. Lines represent means. Each symbol represents one sample (n=8).
P-values from paired two-sided Student’s
t - tests. d) Clustering via t-distributed stochastic
neighbor embedding (tSNE) of all cells from whole PBMC (left) and FNA (right)
scRNAseq samples pooled from all timepoints from participant 05. Each dot
represents a cell, colored by phenotype as defined by the gene expression
profile. Total numbers of cells are below clusters. e) Representative flow
cytometry gating of CD20hi CD38int population in FNA.
Cells pre-gated IgDlo CD19+ CD4− live
singlet lymphocytes. f) Kinetics of total (closed circles) and HA+
(open circles) GC B cells in FNA, as defined by flow cytometry gates in e and
Extended Data Fig. 2k. Symbols at each
timepoint represent one sample (n=7). Daggers denote samples excluded from
analysis due to low cell recovery or blood contamination.
Extended Data Figure 2.
Defining influenza virus vaccine-induced GC B cell response in
humans.
a) Cortical thickness measurements of axillary LNs before each FNA
collection. b) FNA cell yields for each participant at the indicated
timepoint. Symbols at each timepoint represent one sample (n=7). c)
Participant 02 percentages of CD19+ CD4− B
cells (left), CD14− CD4+ T cells (middle), and
CD14+ CD4− monocytes or granulocytes
(right) of CD45+ in PBMC (red) and FNA (blue) from one set of
paired samples, representative of 6 FNA samples. d) Unsupervised clustering
via tSNE based on scRNAseq gene expression of all cells pooled from all
samples and timepoints from participant 05. Each dot represents a cell,
colored by phenotype as defined by gene expression profile. e) Dot plot
showing the average log-normalized expression of a set of marker genes and
the fraction of cells expressing the genes in each unsupervised cluster. f,
g) Annotated tSNE clusters of all cells from all scRNA-seq samples (f) and
IgDlo enriched B cells from PBMC scRNA-seq samples (g) pooled
from all time points from participant 05. Total number of cells is below
clusters. h) Dot plot for annotated clusters. i) Representative flow
cytometry gating of Bcl6 expression within CD20hi
CD38int in PBMC and FNA. Cells pre-gated IgDlo
CD19+ CD4− live singlet lymphocytes. j)
Representative histographs (upper) and median fluorescence intensity (lower)
of the indicated markers on GC B cells (IgDlo CD20hi
CD38int) compared to PBs (IgDlo
CD20− CD38+), memory B cells
(IgDlo CD27+ CD38−), and
naïve B cells (IgD+ CD27−). All
populations pre-gated CD19+ CD4− live singlet
lymphocytes. MFIs from 2- or 4-week FNA samples from participants 04, 05,
07, 08, 09, and 11. Lines represent medians. k) Representative gating of
HA+ GC B cells. Cells pre-gated CD20hi
CD38int IgDlo CD19+
CD4− live singlet lymphocytes. l) Kinetics of
HA-binding percent of GC B cells measured by flow cytometry in participants
04, 05, and 11. m) Kinetics of HA+ CD38+
CD20lo PBs (open triangles) and HA+
CD38− CD20+ ABCs (closed circles) in FNA,
as gated in Extended Data Fig. 1c.
Symbols at each timepoint represent one sample (n=7). Daggers denote samples
excluded from analysis due to low cell recovery or blood contamination.
Single cell RNA sequencing (scRNAseq) was performed on whole PBMCs and
FNA samples from all timepoints for participant 05 to comprehensively examine
their cellular compositions. Compared to PBMCs, B cells and CD4+ T
cells comprised substantially larger populations in FNA (38.5% vs 5.8% for B
cells and 42.3% vs 34.9% for CD4+ T cells), whereas monocytes and
platelets were substantially lower (Fig.
2d, Extended Data Fig.
2d–h, Extended Data Table 1). Surface staining revealed a
population of CD20hi CD38int IgDlo B cells that
increased in frequency after vaccination in 5 of the 7 participants analysed
(participants 03, 04, 05, 08, and potentially 11, whose baseline FNA specimen
was among those excluded) (Fig. 2e).
Intranuclear staining confirmed that this CD20hi CD38int
IgDlo B cell population expressed Bcl6, indicating that it was
comprised of GC B cells, whereas equivalently gated PBMCs did not (Extended Data Fig. 2i). The expression of
additional markers on the CD20hi CD38int IgDlo
population was consistent with a GC phenotype, including CD24lo,
CD27int, CD71hi, CXCR5int, and
Ki-67+ (Extended Data Fig.
2j). HA+ GC B cells were detected and increased in
frequency after vaccination in participants 04, 05, and 11, along with
HA+ IgDlo CD20− CD38+ PBs
and IgDlo CD20+ CD38− ABCs, which also
were detected in participants 07 and 09 (Fig.
2f, Extended Data Figs. 1c,
2k-m, and Extended Data Table 2).
Extended Data Table 1.
Cell counts in overall and B cell clusters based on single-cell gene
expression (Participant 05)
Overall cluster
PBMC
Memory B (PBMC)
FNA
B
2078 (5.8%)
21855 (89.6%)
27223 (38.5%)
CD4+ T
12408 (34.9%)
734 (3.0%)
29914 (42.3%)
CD8+ T
10138 (28.5%)
728 (3.0%)
11693 (16.5%)
NK
4886 (13.7%)
291 (1.2%)
965 (1.4%)
Monocyte
5418 (15.2%)
678 (2.8%)
837 (1.2%)
pDC
178 (0.5%)
29 (0.1%)
159 (0.2%)
Platelet
473 (1.3%)
67 (0.3%)
5 (0.0%)
B cell cluster
GC
0 (0.0%)
0 (0.0%)
689 (2.8%)
PB
136 (7.3%)
47 (0.2%)
358 (1.4%)
PB-like
20 (1.2%)
22 (0.1%)
0 (0.0%)
Naïve
1200 (64.7%)
1709 (7.9%)
8647 (34.6%)
ABC
115 (6.2%)
4950 (22.8%)
539 (1.4%)
RMB
384 (20.7%)
14978 (69.0%)
14941 (59.8%)
Extended Data Table 2.
Cell counts of FNA populations based on flow cytometry
Participant
week
Live cells
GC B cells
HA+ GC B cells
HA+ PB
HA+ ABC
321-03
0
1.14E+06
627
0
0
0
321-03
1
5.25E+06
0
0
20
0
321-03
2
1.84E+06
15929
0
19
0
321-03
4
2.35E+06
493
0
0
0
321-03
9
3.48E+06
515
0
0
0
321-04
0
6.60E+05
7407
0
0
0
321-04
1
5.25E+06
19842
327
20006
4765
321-04
2
1.05E+06
22295
2296
194
375
321-04
3
5.48E+05
1996
10
0
411
321-04
9
1.00E+06
35749
504
18
696
321-05
0
1.02E+06
859
0
0
0
321-05
1
7.60E+05
826
0
98
373
321-05
2
6.88E+06
31453
4954
1995
13787
321-05
4
3.95E+06
49434
9726
2210
3135
321-05
9
1.13E+06
28017
6592
1615
1580
321-07
0
1.55E+06
1862
0
0
0
321-07
1
1.92E+06
9288
0
20857
3224
321-07
2
2.68E+05
91
18
9
209
321-07
4
1.98E+06
9754
19
149
1488
321-07
9
1.93E+05
67
17
17
452
321-08
0
5.33E+05
6
0
0
28
321-08
1
5.67E+05
3883
0
101
124
321-08
2
1.01E+06
8559
0
39
371
321-08
3
2.61E+05
292
0
13
0
321-08
9
1.84E+06
9305
0
0
455
*321-09
0
1.56E+05
321-09
1
5.80E+06
4915
8
9074
3556
*321-09
2
2.69E+04
321-09
4
1.67E+05
70
0
0
44
321-09
9
7.74E+05
1055
0
0
53
*321-11
0
2.29E+04
321-11
1
1.58E+06
4817
458
4522
6215
321-11
2
7.07E+05
1040
870
28
601
321-11
4
3.32E+05
379
13
13
105
321-11
9
1.66E+05
48
0
6
54
Sample excluded due to low cell recovery or blood
contamination
Clonal overlap between PB and GC B cells
We clonally analysed PBs and GC B cells from participants 04, 05, and 11.
We sorted single PBs isolated from 1-week post-vaccination PBMCs and expressed
the corresponding immunoglobulin genes as recombinant monoclonal antibodies
(mAbs, Extended Data Figs. 1d, 3a)[13,14]. We generated
54, 206, and 74 clonally distinct mAbs from PBs, of which 4, 125, and 27 bound
QIV, from participants 04, 05, and 11, respectively (Fig. 3a, Extended Data
Fig. 3b). We also generated mAbs from single GC B cells sorted
irrespective of their binding to HA probes from three timepoints for each of the
three participants. We generated a total of 133, 153, and 87 clonally distinct
mAbs, of which 42, 61, and 35 bound QIV by ELISA from participants 04, 05, and
11, respectively (Fig. 3a, Extended Data Figs. 1e, 3c). Clonal analysis using Vh gene information[15,16] combined from the mAbs and repertoire data from bulk sorted
PBs revealed substantial clonal overlap between B cell clones participating in
the PB and GC responses, with 27%, 58%, and 7% of total GC sequences, and 88%,
78%, and 12% of the QIV-binding GC sequences clonally related to PBs for
participants 04, 05, and 11, respectively (Fig.
3b, Extended Data Fig. 3d). The
proportion of GC sequences clonally related to the early PB response over time
was variable for the three participants; overlap increased, stayed fairly
constant, and decreased for participants 04, 05, and 11, respectively (Extended Data Fig. 3e). Clones recruited to
the early PB response were highly mutated, consistent with derivation from
recalled memory B cells. Clones that participated in both GC and PB responses
had similarly high levels of SHM, whereas GC clones not found in the early blood
PB response carried significantly lower mutational burdens, suggesting a
predominantly naïve B cell origin (Fig.
3c).
Extended Data Figure 3.
GC B cell response to influenza virus vaccine is clonally
diverse.
a) Schematic of single cell mAb cloning and expression. Paired heavy
and light chain genes were amplified from singly sorted PBs or GC B cells.
Variable portions of heavy chains were cloned into a Cγ1 expression
vector and variable portions of κ and λ light chains were
cloned into respective expression vectors. Paired heavy and light chain
expression vectors were co-transfected into 293F cells, and mAbs were
purified from culture supernatant by protein A affinity chromatography, then
screened for QIV specificity by ELISA. b) Minimum positive concentrations of
clonally unique mAbs generated from singly sorted PBs as determined by QIV
ELISA; positive binding defined as greater than 3× background. c)
Distance-to-nearest-neighbor plots for choosing a distance threshold for
inferring clones via hierarchical clustering. After partitioning sequences
based on common V and J genes and junction length, the nucleotide Hamming
distance of a junction to its nearest non-identical neighbor from the same
participant within its partition was calculated and normalized by junction
length (blue histogram). For reference, the distance to the nearest
non-identical neighbor from other participants was calculated (green
histogram). A clustering threshold of 0.1 (dashed black line) was chosen via
manual inspection and kernel density estimate (dashed purple line) to
separate the two modes of the within-participant distance distribution
representing, respectively, sequences that were likely clonally related and
unrelated. d) Clonal overlap of sequences from mAb cloning and bulk
repertoire analysis between PBs sorted from PBMCs 1-week post-vaccination
and GC B cells from the indicated timepoint among total (top) and only
QIV-binding (bottom) sequences. Purple chords link overlapping GC and PB
clones; black chords link GC clones found at multiple timepoints that did
not participate in the early PB response. Chord width corresponds to clonal
population size. Percentages are of GC sequences overlapping with PBs. e)
Clonal rank-abundance distributions of GC B cells from indicated timepoints
(left) and of early blood PBs (right). The number of GC B cells or early
blood PBs in a clone as a percentage of the total GC or early blood PB
repertoire (y-axis) is plotted against the abundance rank of that clone
(x-axis). Solid lines represent the estimated clonal abundance curves, with
shaded bands representing the 95% confidence intervals from 200 bootstraps.
g) tSNE clusters of B cells from FNA scRNAseq samples from participant 05.
Each dot represents a cell, colored by phenotype as defined by gene
expression profile. Total numbers of cells are given below clusters. GC
percentages are indicated in blue. h) IGHV mutation
frequency of naïve B cells pooled from all timepoints (left) and the
indicated populations at the indicated timepoint (right) from scRNAseq of
whole and memory B cell-enriched PBMC and FNA samples from participant 05.
Horizontal lines represent medians. P-values from two-sided
Dunn’s multiple comparisons test.
Figure 3.
Clonally diverse GC B cell response to influenza virus vaccine.
a) Minimum positive concentrations of clonally unique mAbs generated
from singly sorted PBs from week 1 post-vaccination (left) and GC B cells at the
indicated timepoints (right) from participant 05 as determined by QIV ELISA;
positive binding defined as greater than 3× background. b) Clonal overlap
of sequences from mAb cloning and bulk repertoire analysis between PBs sorted
from PBMCs 1-week post-vaccination and GC B cells from all timepoints among
total (left) and only QIV-binding (right) sequences. Chord width corresponds to
clonal population size; numbers of sequences are in Extended Data Table 3. Percentages are of GC
sequences overlapping with PBs. c) Immunoglobulin heavy chain variable region
(IGHV) gene mutation frequency of sorted QIV-binding PBs
and GC B cells that overlapped clonally (purple) or did not (red and blue).
Vertical lines represent medians. Sequence counts were 14, 149, and 1000
(participant 04); 22, 1129, and 1034 (participant 05); 29, 43, and 57
(participant 11) for GC, shared, and PB, respectively. P-values
from two-sided Dunn’s multiple comparisons test. d, e) Clustering via
tSNE of B cells showing GC (blue), PB, (red), PB-like (brown), naïve
(gold), ABC (green), and RMB (lavender) populations pooled from all timepoints
(d) and QIV-binding clonal kinetics showing clones found in GC (blue), early PB
(red), or both (purple) at the indicated timepoints (e). f, g) Dendrograms of
clonal families of H1 HA–binding day 60 GC mAbs 1A06 (f) and 1C10 (g).
Horizontal branch length represents the expected number of substitutions per
codon in V -region genes, corresponding to the key in the lower left of each
panel. Colored symbols represent sequences from cells isolated at day 5 unless
otherwise specified, corresponding to the indicated phenotype and isotype.
We next tracked the clonal dynamics of vaccine-responding B cell clones
using the scRNAseq data from participant 05. Gene expression–based
clustering of B cells from whole FNA samples and from pooled MBC-enriched and
whole PBMCs identified naïve, ABC, resting MBC (RMB), and PB populations
in FNA and PBMC samples. GC B cells were identified as a distinct cluster only
in the FNA samples (Fig. 3d, Extended Data Figs. 3g, f, 4a–h). Isotype distribution among the clusters was
consistent with ELISpot and flow cytometry analyses; PBs and GC B cells were
predominantly IgG, naïve B cells expressed non-isotype switched
receptors, and RMB expressed IgM, IgG, or IgA (Extended Data Fig. 4i). Sequences clonally related to QIV-binding
mAbs were identified in PB and ABC compartments in 1-week post-vaccination
PBMCs; thereafter, QIV-binding PBs declined rapidly and ABCs predominated. In
FNA samples, QIV-binding GC B cells and PBs were observed after week 2, and
QIV-binding RMB appeared at week 2–4 (Fig.
3e). The SHM frequency of clones recruited to the GC but not early PB
response remained lower at all timepoints than that of clones found in both
compartments (Extended Data Fig. 3h). We
next constructed dendrograms of some responding B cell clonal lineages that were
either highly mutated and extensively expanded (1A06, Fig. 3f) or minimally mutated and less expanded (1C10,
Fig. 3g).
Extended Data Figure 4.
B cell clustering for participant 05.
a) tSNE plot showing unsupervised clusters based on scRNAseq gene
expression of cells in the “B cell” cluster of Extended Data Fig. 2e, pooled from all samples and
timepoints from participant 05. b) Dot plot showing the average
log-normalized expression of a set of marker genes and the fraction of cells
expressing the genes in each unsupervised cluster. c) tSNE plot showing BCR
availability. d) tSNE plot showing interim annotated clusters. e) Dot plot
for interim annotated clusters. f) tSNE plot showing final annotated
clusters. g) Dot plot for final annotated clusters. h) tSNE plot showing
IHGV mutation frequency in BCRs. Total numbers of cells are given below
clusters. i) Bar plots showing isotype usage in annotated B cell clusters.
Numbers of cells per cluster are in Extended
Data Table 1.
Mapping vaccine-induced PB and GC B cells
Influenza A viruses are categorized into two phylogenetic groups based
on the HA sequence[17]. The QIV
contains antigens that are derived from circulating H1N1 (group 1 HA) and H3N2
(group 2 HA) strains, in addition to strains from the two antigenically distinct
influenza B lineages. Antibodies are considered broadly cross-reactive if they
recoginze HAs from H1N1 or N3N2 strains that no longer circulate in humans. We
analyzed the binding breadth of mAbs derived from PB and GC B cell using
influenza virus protein microarrays (IVPMs)[18,19] and found
that both broadly cross-reactive and strain-specific clones were recruited to
the GC and PB responses (Fig. 4a, Extended Data Fig. 5a–c). However, analysis of all QIV binding mAbs combined
from participants 04, 05, and 11 indicated that the proportion of
strain-specific mAbs among H1 HA and influenza B HA binders was significantly
higher in clones recruited to the GC but not early PB response. Similar trends
were observed for the individual participants (Fig. 4b, Extended Data Fig.
5d).
Figure 4.
Functionally diverse GC and PB responses to influenza virus vaccine.
a) Binding of group 1–binding mAbs generated from singly sorted
PBs and GC B cells that overlapped clonally (purple) or did not overlap (red and
blue) for PB and GC B cells, respectively, from participant 05 using an
influenza virus protein microarray (IVPM). Scale bar is median fluorescence
intensity. Vaccine strains in bold type; underlined strains circulated in humans
in participants’ lifetimes. b) Percentages of mAbs that bound two or more
HA strains from participants 04, 05, and 11 from GC clones that did not
participate in the early PB response (blue) and from PB and shared clones (red).
P-values from Fisher’s exact test. The number of
mAbs is indicated in the middle of the charts. c) Polyclonal epitopes of Fabs
from plasma at indicated timepoints from participants 04, 05, and 11 with HA
from A/Michigan/45/2015. Epitopes were determined by 3D reconstructions and/or
2D class averages (images to bottom right of 3D reconstructions). HA proteins
shown in grey; Fabs shown in multiple colors. d) Monoclonal and polyclonal
epitopes of immune complexes with HA from A/Michigan/45/2015 and Fabs generated
from the indicated GC mAbs (blue) and plasma pAbs (red). Fabs with dashed
outlines have predicted epitopes due to limited particle representation. e)
Protection of GC mAbs 1B05 and 2C09 in a mouse influenza virus challenge model.
Mice received 5 mg/kg of the indicated mAb intraperitoneally 1 day before
intranasal challenge with A/California/04/2009 E3 (H1N1), and were weighed
daily; 7 mice were used for 1B05 and 1G01, 6 for 2C09 and isotype control, and 5
for uninfected. Error bars indicate mean ±SEM.
Extended Data Figure 5.
GC and PB responses to influenza virus vaccine are functionally
diverse.
a–c) IVPM binding of H1- (a), H3- (b), and influenza B/HA-
(c) binding mAbs generated from singly sorted PBs and GC B cells that
overlapped clonally (purple) or did not overlap (red and blue) from the
indicated participant. Scale bar represents median fluorescence intensity.
Asterisks denote HAI+ mAbs. Vaccine strains in bold type;
underlined strains circulated in humans in participants’ lifetimes.
d) Percentages of mAbs that bound two or more HA strains from participants
04, 05, and 11 from GC clones that did not participate in the early PB
response (blue), clones that participated in both GC and early PB responses
(purple), and from PB clones not found in GCs (red). Numbers of mAbs are
indicated in the middle of the charts. e) Polyclonal epitopes of Fabs from
plasma at indicated timepoints from participants 04, 05, and 11 with HA from
A/Singapore/INFIMH-16-0019/2016. Epitopes were determined by 3D
reconstructions and/or 2D class averages (images to bottom right of 3D
reconstructions). HA proteins shown in grey; Fabs shown in multiple colors;
Fabs with dashed outlines have predicted epitopes due to limited particle
representation. f, g) Example 2D class averages of immune complexes from
participants 04, 05, and 11 plasma with HA from A/Michigan/45/2015 (f) and
A/Singapore/INFIMH-16-0019/2016 (g). h, i) Monoclonal and polyclonal
epitopes of immune complexes with HA from A/Michigan/45/2015 (h) or
A/Singapore/INFIMH-16-0019/2016 (i) and Fabs generated from indicated GC
mAbs in blue or purple mesh and plasma pAbs in red. Fabs with dashed
outlines have predicted epitopes due to limited particle representation. j,
k) Example 2D class averages of immune complexes from the indicated mAb with
HA from A/Michigan/45/2015 (j) and A/Singapore/INFIMH-16-0019/2016 (k).
We next examined the epitopes targeted by plasma anti-HA antibodies in
participants 04, 05, and 11. We used electron microscopy polyclonal epitope
mapping (EMPEM)[20,21] of antigen binding fragments (Fabs)
purified from the plasma of these participants to identify the binding landscape
to the H1 and H3 HAs included in QIV. The polyclonal antibody (pAb) response
diversified over time in participants 04, 05, and 11; new pAbs that appeared in
the first four weeks after vaccination likely represented contributions from the
early PB response, whereas later additions were likely also contributed by
GC-derived longer-lived plasma cells (Fig.
4c, Extended Data Fig.
5e–g). We then mapped the
epitopes targeted by some GC clones that did not participate in the early PB
response by negative stain electron microscopy of Fab/HA complexes and found
that the binding footprint of some Fabs overlapped with those found in plasma,
whereas others targeted unique epitopes (Fig.
4d, Extended Data Fig.
5h–k). We identified two
mAbs that targeted unique H1 HA epitopes and were protective in mice against
lethal influenza challenge: 1B05 and 2C09 from participants 04 and 05,
respectively (Fig. 4e). Notably, these and
another mAb not detected in plasma, 1B08 from participant 05, were
strain-specific and had low Vh mutation frequencies of 0.033, 0.026, and 0.026,
respectively.
Discussion
This study sought to define the role of the GC reaction in human B cell
responses to influenza virus vaccination. We found that a substantial proportion of
vaccine-binding GC B cell clones were part of the early circulating PB response.
These shared clones exhibited the two cardinal features of a recall B cell response
to influenza: they possessed high levels of SHM, and they were broadly
cross-reactive. By contrast, a subset of vaccine-induced GC B cells that were not
detected among early circulating PBs had low SHM levels and were more likely to be
strain-specific, indicating a probable naïve B cell origin. Studies of
secondary GC responses in mice have found that naïve B cells and less
somatically mutated MBCs are more efficiently recruited to GCs than MBCs that have
undergone extensive GC selection[22-25]. One key
difference is that in the current study, the vaccine antigens were likely different
from the influenza antigens the participants had previously encountered, as none had
been vaccinated for three years, whereas in the mouse studies, animals were boosted
with the same antigen. Compared to homologous reimmunization, heterologous boosting
may recruit MBCs to GC responses more efficiently due to decreased competition from
preexisting antibodies.To our knowledge, the current study provides the first direct evidence of
vaccine-induced GC responses in humans, but it does have some limitations. We
detected HA-binding GC B cells in only three participants despite readily detectable
peripheral B cell responses in all eight participants. It is possible that the LNs
sampled in the other five participants were not the primary draining LNs. It is also
possible that vaccination did not elicit a GC response in these participants. A
second limitation pertains to our clonal analyses of vaccine-responding B cells, in
which we excluded those encoding mAbs that did not show detectable binding by QIV
ELISA. Studies in mice[26,27] and humans[28] indicate that low-affinity B cells can be
recruited to GCs despite encoding BCRs that, when expressed as mAbs, are below the
ELISA detection threshold. This suggests that some very low-affinity clones that are
in fact specific for QIV could have been excluded from our analyses, resulting in
overestimation of the proportion of higher-affinity clones in the response. In a
similar vein, the epitope analysis by EMPEM may have failed to detect some
low-affinity and/or -frequency circulating pAbs, overestimating the contribution
from more abundant, higher-affinity PB- and plasma cell-derived antibodies.A significant fraction of vaccine-induced GC- and PB-derived mAbs were
cross-reactive with previously circulating influenza virus strains. This response is
reminiscent of the original antigenic sin (OAS) phenomenon, where antibody responses
are preferentially directed against previously encountered influenza strains when a
person is exposed to a contemporary strain[4]. We propose that suboptimal GC B cell responses after
influenza vaccination result in a more pronounced OAS effect due to inefficient
engagement of naïve B cells targeting novel epitopes. High levels of baseline
antibodies against conserved influenza epitopes may interfere with the formation or
continued maintenance of vaccination-induced GC responses in humans. If so, new
vaccine formulations that promote robust GC reactions are more likely to induce a
more diverse antibody response against circulating and emerging influenza virus
strains.
Methods
Sample collection, preparation, and storage.
All studies were approved by the Institutional Review Board of
Washington University in St. Louis. Written consent was obtained from all
participants. Eight participants who had not been vaccinated against influenza
for at least three years were enrolled, including 1 female and 7 males, aged
26–40 years old. Peripheral blood mononuclear cells (PBMCs) were isolated
using Vacutainer CPT tubes (BD), the remaining RBCs were lysed with ammonium
chloride lysis buffer (Lonza), and cells were immediately used or cryopreserved
in 10% dimethylsulfoxide in FBS. Ultrasound guided fine-needle aspiration (FNA)
of axillary lymph nodes was performed by a qualified physician’s
assistant under the supervision of a radiologist. Lymph node dimensions and
cortical thickness were measured before each FNA. For each FNA sample, 6 passes
were made using 25 -gauge needles, each of which was flushed with 3 mL of RPMI
1640 supplemented with 10% FBS and 100 U/mL penicillin/streptomycin, followed by
three 1 -mL rinses. Red blood cells were lysed with ammonium chloride buffer
(Lonza), washed twice with PBS supplemented with 2% FBS and 2 mM EDTA, and
immediately used or cryopreserved in 10% DMSO in FBS. Participants reported no
adverse effects of phlebotomy, serial FNA, or vaccination.
Vaccine.
Flucelvax QIV influenza vaccine (2018/2019 season) was purchased from
Seqirus.
Antigens.
For ELISpot, plates were coated with QIV, tetanus/diphtheria vaccine
(Grifols), or recombinant hemagglutinin (HA) proteins derived from the pandemic
H1N1 (A/Michigan/45/2015), H3N2 (A/Singapore/INFIMH-16-0019/2016),
B/Yamagata/16/88-like lineage (B/Phuket/3073/2013), or B/Victoria/2/87-like
lineage (B/Brisbane/60/2008) influenza viruses. HA proteins were expressed in a
baculovirus expression system as described previously[29]. For flow cytometry staining,
recombinant HA from A/Michigan/45/2015 (a.a.18–529),
A/Singapore/INFIMH-16-0019/2016 (a.a.17–529), and B/Colorado/06/2017
(a.a. 18–546) expressed in 293F cells were purchased from Immune
Technology and biotinylated using the EZ-Link Micro NHS-PEG4-Biotinylation Kit
(Thermo Fisher); excess biotin was removed using 7-kDa Zeba desalting columns
(Pierce).
ELISpot.
Direct ex-vivo ELISpot was performed to determine the
number of total, vaccine-binding, or recombinant HA-binding IgM-secreting cells
present in PBMC samples. ELISpot plates (Millipore) were coated overnight at
4°C with QIV (diluted 1:100), tetanus/diphtheria vaccine (diluted 1:50),
and 10 µg/mL anti-human Ig (Jackson ImmunoResearch) and 3 µg/mL
recombinant HA proteins. Plates were blocked the following morning for 90 min at
37°C with RPMI 1640 supplemented with 10% FBS. Dilutions of washed PBMCs
were incubated for 18 h in RPMI supplemented with 10% FBS. After washing the
plates, secreted antibodies were detected with anti-human IgM-biotin
(Invitrogen, 1:2000) and streptavidin-HRP (Jackson ImmunoResearch), and plates
were developed with AEC substrate (Sigma). Total, vaccine-binding, or
recombinant HA-binding IgG and IgA-secreting cells were detected using IgG/IgA
double -color ELISpot Kits (Cellular Technologies, Ltd.) according to the
manufacturer’s instructions. ELISpot plates were analyzed using an
ELISpot counter (Cellular Technologies Ltd.).
ELISA.
Assays were performed in 96-well plates (MaxiSorp; Thermo). Each well
was coated with 100 µL of QIV (diluted 1:100), tetanus/diphtheria vaccine
(diluted 1:50), or with 0.5 μg/mL of the recombinant HA antigens in PBS,
and plates were incubated at 4 °C overnight. Plates were then blocked
with 0.05% Tween20 and 10% FBS in PBS. Plasma or mAbs were serially diluted in
blocking buffer and added to the plates. Plates were incubated for 90 min at
room temperature and then washed 3 times with 0.05% Tween-20 in PBS. Goat
anti-human IgG-HRP (Jackson ImmunoResearch, 1:2,500) was diluted in blocking
buffer before adding to wells and incubating for 90 min at room temperature.
Plates were washed 3 times with 0.05% Tween20 in PBS, and then washed 3 times
with PBS before the addition of peroxidase substrate (SigmaFAST
o-Phenylenediamine dihydrochloride, Sigma-Aldrich). Reactions were stopped by
the addition of 1 M HCl. Optical density measurements were taken at 490 nm. The
half-maximal binding dilution for plasma was calculated using nonlinear
regression (Graphpad Prism v7). The minimum positive concentration for mAbs was
defined as that with optical density at least 3-fold above background.
Hemagglutination inhibition (HAI).
Plasma and monoclonal antibody HAI titers were determined for the four
constituent viruses of 18–19 QIV (A/Michigan/45/2015 [pH1N1],
A/Singapore/INFIMH-16-0019/2016 [H3N2], B/Phuket/3073/2013 [Yamagata lineage],
and B/Colorado/06/2017 [Victoria lineage] as described previously[1]. Briefly, plasma samples were
treated with receptor -destroying enzyme (RDE; Denka Seiken) by adding 1 part
plasma to 3 parts RDE and incubating at 37° C overnight. The following
morning, RDE was inactivated by incubating the samples at 56° C for 1 h.
Then, plasma samples or purified monoclonal antibodies were serially diluted
with PBS in 96 -well u-bottom polystyrene plates, and 4 agglutinating doses (as
determined by incubation with 1% turkey RBCs in the absence of plasma) of the
appropriate inactivated virus were added to each well. After 30 min at room
temperature, 50 μL of 0.5% turkey RBCs (Lampire) suspended in PBS was
added to each well, and the plates were gently agitated. After an additional 30
min at room temperature, the titers were computed as the reciprocal of the final
dilution for which non-agglutination was observed.
Influenza virus protein microarray (IVPM).
IVPMs were generated as described previously[18,19]. Briefly, recombinant influenza virus HAs were spotted onto
expoxysilane-coated glass sides (Schott) using a Versa 100 automated liquid
handler (Aurora Biomed). Each array included 13 HAs (Extended Data Table 5) diluted in 0.1% milk in PBS,
and spotted in triplicate. Each slide contained 24 identical arrays. HAs were
printed at a volume of 30 nL per spot with a concentration of 100 µg/mL.
Slides were vacuum-packed and stored at –80°C until use. At the
start of each assay, slides were removed from the –80°C freezer
and allowed to warm to room temperature, then incubated in a humidity chamber at
95–98% relative humidity for 2 h. The slides were then inserted into
96-well microarray gaskets (Arrayit), dividing the slide into 24 separate
arrays, which were blocked with 220 µL 3% milk in PBS containing 0.1%
Tween 20 (PBS-T) for 2 h. The blocking solution was then removed, and mAbs
diluted in 1% milk in PBS-T were incubated with arrays at a volume of 100
µL and a concentration of 5 µg/mL for 1 h. The arrays were washed
3 times with 220 µL PBS-T, and secondary antibody solution containing
Cy5-labeled anti-human IgG secondary antibody (Abcam) diluted 1:3,000 in 1% milk
in PBS-T was added to each well at a volume of 50 µL and incubated for 1
h. After removing the secondary antibody solution, arrays were washed 3 times
with PBS-T and removed from their gaskets. The slides were rinsed with PBS-T,
followed by a rinse with deionized water, and then dried with an air compressor.
Arrays were imaged and analyzed with a Vidia microarray scanner (Indevr) using
an exposure time of 1000 ms to measure spot median fluorescence.
Extended Data Table 5.
HA Strains for IVPM
Strain Name (Subtype)
Abbreviation
A/Michigan/45/2015
(H1)
Mich15
H1
A/New Caledonia/20/1999
(H1)
NC99 H1
A/Texas/36/1991
(H1)
TX91 H1
A/Puerto Rico/8/1934 (H1)
PR8 H1
A/Japan/305/1957 (H2)
H2
A/Indonesia/05/2005 (H5)
Indo H5
A/mallard/Sweden/81/2002 (H6)
H6
A/shoveler/Netherlands/18/1999 (H11)
H11
A/black headed gull/Sweden/1/1999
(H13)
H13
A/black headed gull/Sweden/5/1999
(H16)
H16
A/mallard/Sweden/24/2002 (H8)
H8
A/mallard/Interior Alaska/7MP0167/2007
(H12)
H12
A/guinea fowl/Hong Kong/WF10/1999
(H9)
H9
A/Singapore/INFIMH-16-0019/2016
(H3)
Sing16H3
A/Wisconsin/67/2005
(H3)
Wisc05 H3
A/Panama/2007/1999
(H3)
Pan99 H3
A/Hong Kong/1/1968 (H3)
HK68 H3
A/red knot/Delaware/541/1988 (H4)
H4
A/mallard/Gurjev/263/1982 (H14)
H14
A/chicken/BC/CN-6/2004 (H7)
H7
A/mallard/Interior Alaska/10BM01929/2010
(H10)
H10
A/shearwater/West Australia/2576/1979
(H15)
H15
B/Lee/1940 (B-HA)
B/Lee HA
B/Colorado/06/2017 (B-HA
Victoria lineage)
B/Colorado
(V)
B/Phuket/3073/2013 (B-HA
Yamagata lineage)
B/Phuket
(Y)
Vaccine strains in bold type; underlined strains circulated in
humans in participants’ lifetimes.
Cell sorting and flow cytometry.
Staining for analysis and sorting was performed using fresh or
cryo-preserved PBMCs or FNA single cell suspensions in 2% FBS and 2 mM EDTA in
PBS (P2). For sorting, cells were stained for 30 min on ice with IgD-PerCP-Cy5.5
(IA6–2, 1:200), CD4-Alexa 700 (SK3, 1:400), CD20-APC-Fire750 (2H7,
1:100), and Zombie Aqua along with CD38-BV605 (HIT2, 1:100), CD71-FITC (CY1G4,
1:200), and CD19-PE (HIB19, 1:200) for PBs or CD19-BV421 (HIB19, 1:100), CD71-PE
(CY1G4, 1:400), CXCR5-PE-Dazzle 594 (J252D4, 1:40), and CD38-PE-Cy7 (HIT2,
1:200) for GC B cells (all BioLegend). Cells were washed twice, and single PBs
(live singlet CD19+ CD4− IgDlo
CD38+ CD20− CD71+) and GC B cells
(live singlet CD19+ CD4− IgDlo
CD71+CD38int CD20+ CXCR5+) were sorted
using a FACSAria II into 96-well plates containing 2 µL Lysis Buffer
(Clontech) supplemented with 1 U/µL RNase inhibitor (NEB), or bulk sorted
into buffer RLT Plus (Qiagen) and immediately frozen on dry ice.For analysis, cells were stained for 30 min on ice with biotinylated
recombinant HAs and PD-1-BB515 (EH12.1, BD Horizon, 1:100) diluted in P2, washed
twice, then stained for 30 min on ice with IgA-FITC (M24A, Millipore, 1:500),
CD45-PerCP (2D1, BD Bioscience, 1:25), IgG-BV480 (goat polyclonal, Jackson
ImmunoResearch, 1:100), IgD-SB702 (IA6–2, Thermo, 1:50), CD38-BV421
(HIT2, 1:100), CD20-Pacific Blue (2H7, 1:400), CD27-BV510 (O323, 1:50),
CD4-BV570 (OKT4, 1:50), CD24-BV605 (ML5, 1:100), streptavidin-BV650, CD19-BV750
(HIB19, 1:100), CXCR5-PE-Dazzle 594 (J252D4, 1:50), CD71-APC (CY1G4, 1:100),
CD14-A700 (HCD14, 1:200), and IgM-APC-Cy7 (MHM-88, 1:400) (all BioLegend)
diluted in Brilliant Staining buffer (BD Horizon). Cells were washed twice, then
fixed and permeabilized for intranuclear staining for 1 h at 25°C with
True Nuclear fixation buffer (BioLegend), washed twice with
permeabilization/wash buffer, and stained for 30 min at 25°C with Bcl6-PE
(7D1, 1:50) and Ki-67-PE-Cy7 (Ki-67, 1:400) (both BioLegend). Cells were washed
twice with permeabilization/wash buffer and resuspended in P2 for acquisition on
an Aurora using SpectroFlo v2.2 (Cytek). Flow cytometry data were analyzed using
FlowJo v10 (Treestar).
Monoclonal antibody (mAb) generation.
Antibodies were cloned as described previously[13]. Briefly, VH, Vκ, and Vλ
genes were amplified by reverse transcription-PCR and nested PCR reactions from
singly sorted GC B cells and PBs using primer combinations specific for IgG,
IgM/A, Igκ, and Igλ from previously described primer
sets[30] and then
sequenced. To generate recombinant antibodies, restriction sites were
incorporated via PCR with primers to the corresponding heavy and light chain V
and J genes. The amplified VH, Vκ, and Vλ genes were cloned into
IgG1 and Igκ expression vectors, respectively, as described
previously[1,30,31]. Heavy and light chain plasmids were co-transfected into
Expi293F cells (Gibco) for expression, and antibody was purified with protein A
agarose (Invitrogen).
Bulk B cell receptor sequencing.
RNA was purified from whole PBMCs and sorted PBs from participants 04,
05, and 11 (Extended Data Table 3) using
the RNeasy Micro Kit (Qiagen). Reverse transcription, unique molecular
identifier (UMI) barcoding, cDNA amplification, and Illumina linker addition to
B cell heavy chain transcripts were performed using the human NEBNext Immune
Sequencing Kit (NEB) according to the manufacturer’s instructions.
High-throughput 2×300 bp paired-end sequencing was performed on the
Illumina MiSeq platform with a 30% PhiX spike-in according to the
manufacturer’s recommendations, except that 325 and 275 cycles were
performed for read 1 and 2, respectively.
Extended Data Table 3.
Processing of bulk sequencing BCR reads and number of nested PCR
sequences and clonally distinct monoclonal antibody sequences
Participant
Sample
Timepoint
Compartment
Cell count
Sequence count
Input
Preprocessed
Post-QC
Unique VDJ
321-04
7
d5
Plasmablast
101555
2433663
73442
70096
31219
321-04
10
d0
PBMC
515000
2605448
99076
80997
33651
321-05
8
d5
Plasmablast
8007
2495273
63677
50292
9898
321-05
11
d0
PBMC
299000
2126862
75363
68028
33853
321-11
9
d6
Plasmablast
2007
2771332
11526
8928
2076
321-11
12
d0
PBMC
481000
2719946
114850
102198
83059
Processing B cell receptor bulk sequencing reads.
Demultiplexed pair-end reads were preprocessed using
pRESTO v0.5.10 [ref. [32]] as follows. 1) Reads with a mean
Phred quality score less than 20 were filtered. 2) Reads
were aligned against template switch sequences and constant region primers, with
a maximum mismatch rate of 0.5 and 0.2, respectively. 3) Reads were grouped
based on unique molecular identifiers (UMIs) determined by the 17 nucleotides
preceding the template switch site. 4) Separate consensus sequences were
constructed for the forward and reverse reads within each UMI group, with a
maximum error score of 0.1 and minimum constant region primer frequency of 0.6.
If multiple constant region primers were associated with a particular UMI group,
the majority primer was used. 5) Forward and reverse consensus sequence pairs
were assembled by first attempting de novo assembly with a
minimum overlap of 8 nucleotides and a maximum mismatch rate of 0.3. If
unsuccessful, this was followed by reference-guided assembly using
blastn v2.7.1 [ref [33]] with a minimum identity of 0.5 and an E-value
threshold of 1×10−5. 6) Isotypes were assigned by local
alignment of the 3′ end of each consensus sequence to isotype-specific
internal constant region sequences with a maximum mismatch rate of 0.3.
Sequences with inconsistent isotype assignment and constant region primer
alignment were removed. 7) Duplicate reads were collapsed into unique sequences,
except for those spanning multiple biological samples and/or with different
isotype assignments. Only sequences with at least two reads contributing to the
UMI consensus sequence were used for further analyses. The template switch
sequences, constant region primers, and isotype-specific internal constant
region sequences that were used in these studies are available at: https://bitbucket.org/kleinstein/immcantation/src/master/protocols/AbSeq/.Initial germline V(D)J gene annotation was performed using IgBLAST
v1.14.0 [ref [34]] with
IMGT/GENE-DB release 201931–4 [ref [35]]. IgBLAST output was processed using Change-O v0.4.3
[ref [36]]. Additional quality
control of these sequences was performed with the following requirements:
aligned exclusively to heavy chain V and J genes; had Ns at fewer than 10% of V
segment positions; had a minimum V segment coverage from nucleotide position 1
to 310 under the IMGT unique numbering scheme[37]; and had a junction length that was a
multiple of 3, where junction was defined as from IMGT codon 104 encoding the
conserved cysteine to codon 118 encoding phenylalanine or tryptophan.
Single cell RNAseq library preparation and sequencing.
Activated and memory B cells were enriched from PBMCs by first staining
with IgD-PE and MojoSort anti-PE Nanobeads (BioLegend), and then processing with
the EasySep Human B Cell Isolation Kit using the EasyEights magnet (Stemcell) to
negatively enrich IgDlo B cells. Enriched IgDlo B cells,
whole PBMCs, and whole FNA from each timepoint for participant 05 were processed
using the following 10× Genomics kits: Chromium Single Cell 5′
Library and Gel Bead Kit v2 (PN-1000006); Chromium Single Cell A Chip Kit
(PN-120236); Chromium Single Cell V(D)J Enrichment Kit; and Human, B cell
(96rxns) (PN-1000016), and Chromium i7 Multiplex Kit (PN-120262). The cDNAs were
prepared after GEM generation and barcoding, followed by GEM RT reaction and
bead cleanup steps. Purified cDNA was amplified for 10–14 cycles before
cleaning with SPRIselect beads. Then, samples were evaluated on a bioanalyser to
determine cDNA concentration. BCR target enrichments were performed on full
-length cDNA. GEX and enriched BCR libraries were prepared as recommended by the
10× Genomics Chromium Single Cell V(D)J Reagent Kit (v1 Chemistry) user
guide, with appropriate modifications to the PCR cycles based on the calculated
cDNA concentration. The cDNA libraries were sequenced on Novaseq S4 (Illumina),
targeting a median sequencing depth of 50,000 and 5,000 read pairs per cell for
gene expression and BCR libraries, respectively.
Processing 10× Genomics single-cell B cell receptor reads.
Demultiplexed pair-end FASTQ reads from participant 05 were preprocessed
using the “cellranger vdj” command from 10×
Genomics’ Cell Ranger v3.1.0 for alignment against the
GRCh38 human reference v3.1.0
(refdata-cellranger-vdj-GRCh38-alts-ensembl-3.1.0). Initial
germline V(D)J gene annotation was performed as described for bulk sequencing
BCR reads. Additional quality control was performed, requiring sequences to be
productively rearranged and have valid V and J gene annotations, consistent
chain annotation (excluding sequences annotated with heavy chain V gene and
light chain J gene), and a junction length that was a multiple of 3. The 429
heavy chain BCRs from the sample day 0 PBMC 2 were removed after they were found
to have identical cell barcodes and sequences as BCRs from the sample day 5 PBMC
2. Only cells with exactly one heavy chain sequence paired with at least one
light chain sequence were retained.
B cell receptor genotyping.
Processed BCR sequences from bulk sequencing (Extended Data Table 3) and, if available,
single-cell sequencing (Extended Data Table
4), were combined with nested PCR and mAb sequences (Extended Data Table 3) for genotyping using
TIgGER v0.3.1 [ref [38]]. Individual genotypes, including novel V gene alleles
missing from IMGT/GENE-DB, were computationally inferred and
used to finalize V(D)J annotations. Non-productively rearranged sequences
annotated as “non-functional” by IgBLAST were
removed from further analysis.
Extended Data Table 4.
Processing of 10x Genomics single-cell BCR and 5’ gene
expression data (Participant 05)
Sample
BCR
5’ gene
expression
Pre-QC number of cells
Post-QC number of cells
Pre-QC number of cells
Post-QC number of cells
Median number of UMIs per cell
Median number of genes per cell
d0 PBMC 2
794
296
8165
7961
4102
1357
d5 PBMC 2
971
896
7242
7106
4309
1378
d12 PBMC 2
437
402
6795
6712
4182
1407
d28 PBMC 2
423
406
6435
6317
4416
1493
d60 PBMC
605
556
7594
7483
4110
1392
d0 memory B (PBMC)
3552
3261
3998
3899
4548
1322
d5 memory B (PBMC)
3141
2768
3365
3265
4328
1231
d12 memory B (PBMC)
5859
5244
6587
6406
4317
1266
d28 memory B (PBMC)
7780
6909
7865
7755
4426
1315
d60 memory B (PBMC)
2817
2592
3143
3057
4587
1397
d0 FNA
2138
1951
6702
6429
3738
1414
d0 FNA 2
2223
2052
6167
5882
3670
1407
d5 FNA
3399
3122
8840
8426
4075
1557
d5 FNA 2
2659
2439
6959
6657
4039
1557
d12 FNA
3408
3128
7491
7178
4066
1514
d12 FNA 2
2537
2315
5932
5661
3969
1486
d12 FNA 3
3157
2902
7322
6992
4004
1496
d28 FNA 2
2005
1809
6604
6274
3878
1541
d28 FNA 3
2071
1869
6491
6173
3854
1539
d60 FNA
3033
2790
5799
5540
3893
1436
d60 FNA 2
3146
2832
5865
5584
3900
1440
Clonal lineage inference.
B cell clonal lineages were inferred based on productively rearranged
heavy chain sequences using hierarchical clustering with single
linkage[15]. First,
sequences were partitioned based on common V and J gene annotations and junction
region lengths. Within each partition, sequences whose junction regions were
within 0.1 normalized Hamming distance from each other were clustered as clones.
This distance threshold was determined by manual inspection in conjunction with
kernel density estimates to identify the local minimum between the two modes of
the within-participant bimodal distance-to-nearest distribution (Extended Data Fig. 3c). Following clonal clustering,
full-length clonal consensus germline sequences were reconstructed for each
clone with D-segment and N/P regions masked with N’s, resolving any
ambiguous gene assignments by majority rule. Within each clone, duplicate
IMGT-aligned V(D)J sequences from bulk sequencing were
collapsed with the exception of duplicates derived from different B cell
compartments or isotypes. Clonal rank abundance distributions for GC and early
PB (Extended Data Fig. 3e) were produced
using the “estimateAbundance” function from Alakazam v0.3.0 [ref
[36]].
Clonal overlap analysis.
Clonal overlap between week 1 PBs from blood and the GC B cells from
later weeks was determined by the presence of sequences from both compartments
in the same B cell clone. Visualization was achieved using the
circlize package v0.4.8 [ref [39]].
Calculation of SHM frequency.
Mutation frequency was calculated by counting the number of nucleotide
mismatches from the germline sequence in the heavy chain variable segment
leading up to the CDR3, while excluding the first 18 positions that could be
error-prone due to the primers used for generating the mAb sequences. This
calculation was performed using the calcObservedMutations
function from SHazaM v0.1.11 [ref [36]].
Construction of B cell lineage trees.
Phylogenetic trees for responding B cell clones were constructed using
IgPhyML v1.1.0 [ref.[40]] with
the HLP19 model[41].
Processing of 10× Genomics single-cell 5′ gene expression
data.
Demultiplexed pair-end FASTQ reads from participant 05 were first
preprocessed on a by-sample basis using the “cellranger count”
command from 10× Genomics’ Cell Ranger v3.1.0 for
alignment against the GRCh38 human reference v3.0.0
(refdata-cellranger-GRCh38–3.0.0). To avoid a batch
effect introduced by sequencing depth, the “cellranger aggr”
command was used to subsample from each sample so that all samples had the same
effective sequencing depth, which was measured in terms of the number of reads
confidently mapped to the transcriptome or assigned to the feature IDs per cell.
The feature biotypes were retrieved using biomaRt v2.42.0 [ref [42]] from Ensembl release 93 [ref
[43]]. Additional
quality control was performed as follows. 1) To remove presumably lysed cells,
cells with mitochondrial content greater than 12.5% of all transcripts were
removed. 2) To remove likely doublets, cells with more than 7,000 features or
70,000 total UMIs were removed. 3) To remove cells with no detectable expression
of common housekeeping genes, cells with no transcript for any of
ACTB, GAPDH, B2M,
HSP90AB1, GUSB, PPIH,
PGK1, TBP, TFRC,
SDHA, and LDHA were removed[44]. 4) The feature matrix was
subset, based on their biotypes, to protein-coding, immunoglobulin, and T cell
receptor genes that were expressed in at least 0.05% of the cells in any sample.
The resultant feature matrix contained 15,479 genes. 5) Cells with detectable
expression of fewer than 200 genes were removed. After quality control, there
were a total of 130,757 cells from 21 samples (Extended Data Table 4).
Single-cell gene expression analysis.
Single-cell gene expression analysis was performed using Seurat v3.1.1
[ref [45]]. UMI counts measuring
gene expression were log-normalized. The top 2,000 highly variable genes (HVGs)
were identified using the “FindVariableFeatures” function with the
“vst” method. A set of 317 immune-related,
“immunoStates” marker genes[46] were added to the HVG list, whereas immunoglobulin and
T cell receptor genes were removed. The data were scaled and centered, and
principal component analysis (PCA) was performed based on HVG expression. The
PCA-guided t-distributed stochastic neighbor embedding (tSNE) plot was generated
using the top 20 principal components (PCs).Overall clusters (Extended Data Table
1) were identified using the “FindClusters” function
with resolution 0.07 (Extended Data Fig.
2b). Cluster identities were assigned by examining the expression of a
set of marker genes for different cell types (Extended Data Fig. 2c–e): MS4A1, CD19, and
CD79A for B cells; CD3D,
CD3E, CD3G, IL7R, and
CD4 or CD8A for CD4+ or CD8+ T cells,
respectively; GZMB, GNLY,
NKG7, and NCAM1 for natural killer (NK)
cells; CD14, LYZ, CST3, and
MS4A7 for monocytes; IL3RA and
CLEC4C for plasmacytoid dendritic cells (pDCs); and
PPBP for platelets.B cells from the overall B cell cluster were further clustered to
identify B cell subsets (Extended Data Table
1) using the “FindClusters” function with resolution
0.2 (Extended Data Fig. 4a). Cluster
identities were assigned by examining the expression of a set of marker genes
for different B cell subsets and the availability of BCRs (Extended Data Fig. 4b–g). The following marker genes were examined:
BCL6, RGS13, MEF2B,
STMN1, ELL3, and SERPINA9
for GC B cells; XBP1, IRF4,
SEC11C, FKBP11, JCHAIN,
and PRDM1 for PBs; TCL1A,
IL4R, CCR7, IGHM, and
IGHD for naïve B cells; TBX21,
FCRL5, ITGAX, NKG7,
ZEB2, and the lack of CR2 for activated B
cells (ABCs); and TNFRSF13B, CD27, and
CD24 for resting memory B (RMB) cells. Although clusters 5
and 9 both clustered with B cells during overall clustering, cluster 5 was
labelled “B & T” as its cells tended to have both BCRs (Extended Data Fig. 4c) and high expression
levels of CD2 and CD3E (Extended Data Fig. 4e), whereas cluster 9 was labelled
“Unassigned” as its cells tended to have no BCR (Extended Data Fig. 4c) and high expression levels of
CD2 and CD3E (Extended Data Fig. 4e). Clusters 5 and 9 were excluded
from the final B cell clustering (Extended Data
Fig. 4f). Cells that were found in the GC cluster but came from the
blood samples were labelled “PB-like” (Extended Data Fig. 4f–g). To further confirm the identities of these B cell
subsets, we assessed their corresponding heavy chain BCRs for SHM frequency
(Extended Data Fig. 4h) and isotype
usage (Extended Data Fig. 4i), which were
consistent with expected values.
EMPEM sample preparation and complexing.
Polyclonal antibodies were prepared similarly to Bianchi and Turner et
al[20]. For each time
point after vaccination, 2 mL plasma samples were heat-inactivated at
55°C for 30 min and incubated on protein G resin (GE Healthcare) for 72
h. Protein G/sera samples were washed three times with PBS, IgG was eluted off
of protein G resin with 0.1 M glycine pH 2.5 buffer and neutralized with 1M
Tris-HCl pH 8 buffer, and IgG was buffer -exchanged with PBS by centrifugation
with 30 kDa concentrators (Amicon). For digestion, 4 mg IgG were buffer
-exchanged with freshly prepared digestion buffer (20 mM sodium phosphate, 10 mM
EDTA, 20 mM cysteine, pH 7.4) and incubated with immobilized papain (Fisher) for
20 h at 37°C. Digestion products were separated from immobilized papain
using Pierce spin columns (Fisher), and buffer -exchanged by centrifugation with
10 kDa concentrators (Amicon). IgG and Fab/Fc were separated by size exclusion
chromatography using a Superdex 200 increase 10/300 column (GE Healthcare). To
generate polyclonal immune complexes, 20 µg HA from
A/Singapore/INFIMH-16-0019/2016 (H3N2) and A/Michigan/51/2015 (H1N1) were
incubated with 1 mg Fab for 16 h at room temperature. Immune complexes were
purified by size exclusion chromatography using a Superose 6 increase 10/300
column (GE Healthcare). Purified immune complexes were concentrated to 50
µL for negative stain electron microscopy analysis.
mAb complexing.
mAbs expressed as Fab were complexed with HA
(A/Singapore/INFIMH-16-0019/2016 (H3N2) and A/Michigan/51/2015 (H1N1)) in a 3:1
molar ratio of Fab to HA for 1 h at room temperature or 16 h at 4°C.
Negative stain electron microscopy.
For negative stain analysis, 400 mesh copper grids (Electron Microscopy
Sciences, EMS) were coated with collodion 2% in amyl acetate (EMS), deposited
with carbon using a Cressington carbon evaporator, and plasma cleaned (EMS).
Immune complexes were diluted to 30 µg/mL, loaded onto prepared grids,
and stained with 2% w/v uranyl formate. Samples were imaged on three Tecnai
transmission electron microscopes (FEI): Spirit T12 with a CMOS 4k camera
(TVIPS), T20 with an Eagle CCD 4k camera (FEI), and Talos 200C with a Falcon II
direct electron detector and a CETA 4k camera (FEI). Micrographs were obtained
using Leginon[47],
100,000–300,000 particles were picked and stacked using Appion[48], and data were processed using
2D and 3D classification and reconstruction in Relion[49,50]. Fab densities were segmented and resampled onto reference
trimers (PDB IDs 4M4Y for group 1 HA and 3M5G for group 2 HA), and final images
were made using UCSF Chimera[51]
and Photoshop (Adobe). Polyclonal Fab specificities were determined using 3D
reconstructions and 2D class averages. Some specificities did not reconstruct
fully due to limited angular sampling caused by orientation bias,
sub-stoichiometric binding of Fabs, and/or low abundance of these immune
complexes even within the large datasets collected. However, in addition to
suggestive 3D density, our large internal database of images of HA-Fab complexes
provide references to extrapolate the 2D images onto 3D models of HA using the
same procedure we recently reported for interpreting neuraminidase-Fab
complexes[52].
In vivo challenge.
Animal experiments were approved by approved by the Washington
University IACUC. Mice were housed under specific-pathogen free conditions at
55±10% relative humidity with a 12h day/night cycle. Female
6–8-week-old C57BL/6 mice (5–7 mice/group) received 5 mg/kg mAb
321–04 1B05, 321–05 2C09, the broadly protective mAb 1G01 [ref
[14]] as a positive
control, or irrelevant human IgG control mAb in 100 μL PBS via
intraperitoneal administration. One day later, the mice were anesthetized with
isofluorane and challenged intranasally with 104 TCID50
A/California/04/2009 E3 (H1N1). Weight was measured daily for 14 days; mice were
euthanized if weight loss exceeded 20% of original weight.
Robust peripheral B cell response to influenza virus vaccination.
a) ELISpot quantification of QIV-binding IgG-, IgM-, and
IgA-secreting QIV-binding PBs 1 -week post-vaccination. Each symbol
represents one participant (n=8). b-e) Flow cytometry (b, c) and sorting (d,
e) gating strategies for PBMC (b, d) and FNA (c, e). Population counts per
mL of blood and frequencies are presented in f, below and in Figs. 1d, 2f,
and Extended Data Fig. 2c, m. f) Kinetics of HA-binding PBs
(CD20lo HA+, open triangles) and activated B cells
(ABCs, CD20+ HA+, closed circles) in PBMCs, gated as
in Fig. 1c, pre-gated IgDlo
CD19+ CD4− live singlet lymphocytes as in
(b). Symbols at each timepoint represent one sample (n=8). g, h) IgG plasma
antibody titers against QIV and Tetanus/Diptheria vaccine measured by ELISA
(g) and hemagglutination inhibition titers against QIV constituent viruses
(h) pre- and 4-weeks post-vaccination. Symbols at each timepoint represent
one sample (n = 8). Horizontal lines in a, g, and h
represent means. Dotted lines represent limit of detection.
P-values from paired two-sided Student’s
t -tests.
Defining influenza virus vaccine-induced GC B cell response in
humans.
a) Cortical thickness measurements of axillary LNs before each FNA
collection. b) FNA cell yields for each participant at the indicated
timepoint. Symbols at each timepoint represent one sample (n=7). c)
Participant 02 percentages of CD19+ CD4− B
cells (left), CD14− CD4+ T cells (middle), and
CD14+ CD4− monocytes or granulocytes
(right) of CD45+ in PBMC (red) and FNA (blue) from one set of
paired samples, representative of 6 FNA samples. d) Unsupervised clustering
via tSNE based on scRNAseq gene expression of all cells pooled from all
samples and timepoints from participant 05. Each dot represents a cell,
colored by phenotype as defined by gene expression profile. e) Dot plot
showing the average log-normalized expression of a set of marker genes and
the fraction of cells expressing the genes in each unsupervised cluster. f,
g) Annotated tSNE clusters of all cells from all scRNA-seq samples (f) and
IgDlo enriched B cells from PBMC scRNA-seq samples (g) pooled
from all time points from participant 05. Total number of cells is below
clusters. h) Dot plot for annotated clusters. i) Representative flow
cytometry gating of Bcl6 expression within CD20hi
CD38int in PBMC and FNA. Cells pre-gated IgDlo
CD19+ CD4− live singlet lymphocytes. j)
Representative histographs (upper) and median fluorescence intensity (lower)
of the indicated markers on GC B cells (IgDlo CD20hi
CD38int) compared to PBs (IgDlo
CD20− CD38+), memory B cells
(IgDlo CD27+ CD38−), and
naïve B cells (IgD+ CD27−). All
populations pre-gated CD19+ CD4− live singlet
lymphocytes. MFIs from 2- or 4-week FNA samples from participants 04, 05,
07, 08, 09, and 11. Lines represent medians. k) Representative gating of
HA+ GC B cells. Cells pre-gated CD20hi
CD38int IgDlo CD19+
CD4− live singlet lymphocytes. l) Kinetics of
HA-binding percent of GC B cells measured by flow cytometry in participants
04, 05, and 11. m) Kinetics of HA+ CD38+
CD20lo PBs (open triangles) and HA+
CD38− CD20+ ABCs (closed circles) in FNA,
as gated in Extended Data Fig. 1c.
Symbols at each timepoint represent one sample (n=7). Daggers denote samples
excluded from analysis due to low cell recovery or blood contamination.
GC B cell response to influenza virus vaccine is clonally
diverse.
a) Schematic of single cell mAb cloning and expression. Paired heavy
and light chain genes were amplified from singly sorted PBs or GC B cells.
Variable portions of heavy chains were cloned into a Cγ1 expression
vector and variable portions of κ and λ light chains were
cloned into respective expression vectors. Paired heavy and light chain
expression vectors were co-transfected into 293F cells, and mAbs were
purified from culture supernatant by protein A affinity chromatography, then
screened for QIV specificity by ELISA. b) Minimum positive concentrations of
clonally unique mAbs generated from singly sorted PBs as determined by QIV
ELISA; positive binding defined as greater than 3× background. c)
Distance-to-nearest-neighbor plots for choosing a distance threshold for
inferring clones via hierarchical clustering. After partitioning sequences
based on common V and J genes and junction length, the nucleotide Hamming
distance of a junction to its nearest non-identical neighbor from the same
participant within its partition was calculated and normalized by junction
length (blue histogram). For reference, the distance to the nearest
non-identical neighbor from other participants was calculated (green
histogram). A clustering threshold of 0.1 (dashed black line) was chosen via
manual inspection and kernel density estimate (dashed purple line) to
separate the two modes of the within-participant distance distribution
representing, respectively, sequences that were likely clonally related and
unrelated. d) Clonal overlap of sequences from mAb cloning and bulk
repertoire analysis between PBs sorted from PBMCs 1-week post-vaccination
and GC B cells from the indicated timepoint among total (top) and only
QIV-binding (bottom) sequences. Purple chords link overlapping GC and PB
clones; black chords link GC clones found at multiple timepoints that did
not participate in the early PB response. Chord width corresponds to clonal
population size. Percentages are of GC sequences overlapping with PBs. e)
Clonal rank-abundance distributions of GC B cells from indicated timepoints
(left) and of early blood PBs (right). The number of GC B cells or early
blood PBs in a clone as a percentage of the total GC or early blood PB
repertoire (y-axis) is plotted against the abundance rank of that clone
(x-axis). Solid lines represent the estimated clonal abundance curves, with
shaded bands representing the 95% confidence intervals from 200 bootstraps.
g) tSNE clusters of B cells from FNA scRNAseq samples from participant 05.
Each dot represents a cell, colored by phenotype as defined by gene
expression profile. Total numbers of cells are given below clusters. GC
percentages are indicated in blue. h) IGHV mutation
frequency of naïve B cells pooled from all timepoints (left) and the
indicated populations at the indicated timepoint (right) from scRNAseq of
whole and memory B cell-enriched PBMC and FNA samples from participant 05.
Horizontal lines represent medians. P-values from two-sided
Dunn’s multiple comparisons test.
B cell clustering for participant 05.
a) tSNE plot showing unsupervised clusters based on scRNAseq gene
expression of cells in the “B cell” cluster of Extended Data Fig. 2e, pooled from all samples and
timepoints from participant 05. b) Dot plot showing the average
log-normalized expression of a set of marker genes and the fraction of cells
expressing the genes in each unsupervised cluster. c) tSNE plot showing BCR
availability. d) tSNE plot showing interim annotated clusters. e) Dot plot
for interim annotated clusters. f) tSNE plot showing final annotated
clusters. g) Dot plot for final annotated clusters. h) tSNE plot showing
IHGV mutation frequency in BCRs. Total numbers of cells are given below
clusters. i) Bar plots showing isotype usage in annotated B cell clusters.
Numbers of cells per cluster are in Extended
Data Table 1.
GC and PB responses to influenza virus vaccine are functionally
diverse.
a–c) IVPM binding of H1- (a), H3- (b), and influenza B/HA-
(c) binding mAbs generated from singly sorted PBs and GC B cells that
overlapped clonally (purple) or did not overlap (red and blue) from the
indicated participant. Scale bar represents median fluorescence intensity.
Asterisks denote HAI+ mAbs. Vaccine strains in bold type;
underlined strains circulated in humans in participants’ lifetimes.
d) Percentages of mAbs that bound two or more HA strains from participants
04, 05, and 11 from GC clones that did not participate in the early PB
response (blue), clones that participated in both GC and early PB responses
(purple), and from PB clones not found in GCs (red). Numbers of mAbs are
indicated in the middle of the charts. e) Polyclonal epitopes of Fabs from
plasma at indicated timepoints from participants 04, 05, and 11 with HA from
A/Singapore/INFIMH-16-0019/2016. Epitopes were determined by 3D
reconstructions and/or 2D class averages (images to bottom right of 3D
reconstructions). HA proteins shown in grey; Fabs shown in multiple colors;
Fabs with dashed outlines have predicted epitopes due to limited particle
representation. f, g) Example 2D class averages of immune complexes from
participants 04, 05, and 11 plasma with HA from A/Michigan/45/2015 (f) and
A/Singapore/INFIMH-16-0019/2016 (g). h, i) Monoclonal and polyclonal
epitopes of immune complexes with HA from A/Michigan/45/2015 (h) or
A/Singapore/INFIMH-16-0019/2016 (i) and Fabs generated from indicated GC
mAbs in blue or purple mesh and plasma pAbs in red. Fabs with dashed
outlines have predicted epitopes due to limited particle representation. j,
k) Example 2D class averages of immune complexes from the indicated mAb with
HA from A/Michigan/45/2015 (j) and A/Singapore/INFIMH-16-0019/2016 (k).Cell counts in overall and B cell clusters based on single-cell gene
expression (Participant 05)Cell counts of FNA populations based on flow cytometrySample excluded due to low cell recovery or blood
contaminationProcessing of bulk sequencing BCR reads and number of nested PCR
sequences and clonally distinct monoclonal antibody sequencesProcessing of 10x Genomics single-cell BCR and 5’ gene
expression data (Participant 05)HA Strains for IVPMVaccine strains in bold type; underlined strains circulated in
humans in participants’ lifetimes.
Authors: Kimberly M Cirelli; Diane G Carnathan; Bartek Nogal; Jacob T Martin; Oscar L Rodriguez; Amit A Upadhyay; Chiamaka A Enemuo; Etse H Gebru; Yury Choe; Federico Viviano; Catherine Nakao; Matthias G Pauthner; Samantha Reiss; Christopher A Cottrell; Melissa L Smith; Raiza Bastidas; William Gibson; Amber N Wolabaugh; Mariane B Melo; Benjamin Cossette; Venkatesh Kumar; Nirav B Patel; Talar Tokatlian; Sergey Menis; Daniel W Kulp; Dennis R Burton; Ben Murrell; William R Schief; Steven E Bosinger; Andrew B Ward; Corey T Watson; Guido Silvestri; Darrell J Irvine; Shane Crotty Journal: Cell Date: 2019-05-09 Impact factor: 41.582
Authors: Jens Wrammert; Kenneth Smith; Joe Miller; William A Langley; Kenneth Kokko; Christian Larsen; Nai-Ying Zheng; Israel Mays; Lori Garman; Christina Helms; Judith James; Gillian M Air; J Donald Capra; Rafi Ahmed; Patrick C Wilson Journal: Nature Date: 2008-04-30 Impact factor: 49.962
Authors: A Danielle Iuliano; Katherine M Roguski; Howard H Chang; David J Muscatello; Rakhee Palekar; Stefano Tempia; Cheryl Cohen; Jon Michael Gran; Dena Schanzer; Benjamin J Cowling; Peng Wu; Jan Kyncl; Li Wei Ang; Minah Park; Monika Redlberger-Fritz; Hongjie Yu; Laura Espenhain; Anand Krishnan; Gideon Emukule; Liselotte van Asten; Susana Pereira da Silva; Suchunya Aungkulanon; Udo Buchholz; Marc-Alain Widdowson; Joseph S Bresee Journal: Lancet Date: 2017-12-14 Impact factor: 79.321
Authors: Colin Havenar-Daughton; Diane G Carnathan; Alba Torrents de la Peña; Matthias Pauthner; Bryan Briney; Samantha M Reiss; Jennifer S Wood; Kirti Kaushik; Marit J van Gils; Sandy L Rosales; Patricia van der Woude; Michela Locci; Khoa M Le; Steven W de Taeye; Devin Sok; Ata Ur Rasheed Mohammed; Jessica Huang; Sanjeev Gumber; AnaPatricia Garcia; Sudhir P Kasturi; Bali Pulendran; John P Moore; Rafi Ahmed; Grégory Seumois; Dennis R Burton; Rogier W Sanders; Guido Silvestri; Shane Crotty Journal: Cell Rep Date: 2016-11-22 Impact factor: 9.423
Authors: Jens Wrammert; Dimitrios Koutsonanos; Gui-Mei Li; Srilatha Edupuganti; Jianhua Sui; Michael Morrissey; Megan McCausland; Ioanna Skountzou; Mady Hornig; W Ian Lipkin; Aneesh Mehta; Behzad Razavi; Carlos Del Rio; Nai-Ying Zheng; Jane-Hwei Lee; Min Huang; Zahida Ali; Kaval Kaur; Sarah Andrews; Rama Rao Amara; Youliang Wang; Suman Ranjan Das; Christopher David O'Donnell; Jon W Yewdell; Kanta Subbarao; Wayne A Marasco; Mark J Mulligan; Richard Compans; Rafi Ahmed; Patrick C Wilson Journal: J Exp Med Date: 2011-01-10 Impact factor: 14.307
Authors: Ali H Ellebedy; Katherine J L Jackson; Haydn T Kissick; Helder I Nakaya; Carl W Davis; Krishna M Roskin; Anita K McElroy; Christine M Oshansky; Rivka Elbein; Shine Thomas; George M Lyon; Christina F Spiropoulou; Aneesh K Mehta; Paul G Thomas; Scott D Boyd; Rafi Ahmed Journal: Nat Immunol Date: 2016-08-15 Impact factor: 25.606
Authors: Anne Eugster; Magnolia L Bostick; Nidhi Gupta; Encarnita Mariotti-Ferrandiz; Gloria Kraus; Wenzhao Meng; Cinque Soto; Johannes Trück; Ulrik Stervbo; Eline T Luning Prak Journal: Methods Mol Biol Date: 2022
Authors: Rachna T Shroff; Pavani Chalasani; Ran Wei; Daniel Pennington; Grace Quirk; Marta V Schoenle; Kameron L Peyton; Jennifer L Uhrlaub; Tyler J Ripperger; Mladen Jergović; Shelby Dalgai; Alexander Wolf; Rebecca Whitmer; Hytham Hammad; Amy Carrier; Aaron J Scott; Janko Nikolich-Žugich; Michael Worobey; Ryan Sprissler; Michael Dake; Bonnie J LaFleur; Deepta Bhattacharya Journal: Nat Med Date: 2021-09-30 Impact factor: 53.440
Authors: Ramin Sedaghat Herati; David A Knorr; Laura A Vella; Luisa Victoria Silva; Lakshmi Chilukuri; Sokratis A Apostolidis; Alexander C Huang; Alexander Muselman; Sasikanth Manne; Oliva Kuthuru; Ryan P Staupe; Sharon A Adamski; Senthil Kannan; Raj K Kurupati; Hildegund C J Ertl; Jeffrey L Wong; Stylianos Bournazos; Suzanne McGettigan; Lynn M Schuchter; Ritesh R Kotecha; Samuel A Funt; Martin H Voss; Robert J Motzer; Chung-Han Lee; Dean F Bajorin; Tara C Mitchell; Jeffrey V Ravetch; E John Wherry Journal: Nat Immunol Date: 2022-07-28 Impact factor: 31.250
Authors: Rishi R Goel; Sokratis A Apostolidis; Mark M Painter; Divij Mathew; Ajinkya Pattekar; Oliva Kuthuru; Sigrid Gouma; Philip Hicks; Wenzhao Meng; Aaron M Rosenfeld; Sarah Dysinger; Kendall A Lundgreen; Leticia Kuri-Cervantes; Sharon Adamski; Amanda Hicks; Scott Korte; Derek A Oldridge; Amy E Baxter; Josephine R Giles; Madison E Weirick; Christopher M McAllister; Jeanette Dougherty; Sherea Long; Kurt D'Andrea; Jacob T Hamilton; Michael R Betts; Eline T Luning Prak; Paul Bates; Scott E Hensley; Allison R Greenplate; E John Wherry Journal: Sci Immunol Date: 2021-04-15
Authors: Fatima Amanat; Mahima Thapa; Tinting Lei; Shaza M Sayed Ahmed; Daniel C Adelsberg; Juan Manuel Carreño; Shirin Strohmeier; Aaron J Schmitz; Sarah Zafar; Julian Q Zhou; Willemijn Rijnink; Hala Alshammary; Nicholas Borcherding; Ana Gonzalez Reiche; Komal Srivastava; Emilia Mia Sordillo; Harm van Bakel; Jackson S Turner; Goran Bajic; Viviana Simon; Ali H Ellebedy; Florian Krammer Journal: Cell Date: 2021-06-08 Impact factor: 66.850
Authors: Jackson S Turner; Jane A O'Halloran; Elizaveta Kalaidina; Wooseob Kim; Aaron J Schmitz; Julian Q Zhou; Tingting Lei; Mahima Thapa; Rita E Chen; James Brett Case; Fatima Amanat; Adriana M Rauseo; Alem Haile; Xuping Xie; Michael K Klebert; Teresa Suessen; William D Middleton; Pei-Yong Shi; Florian Krammer; Sharlene A Teefey; Michael S Diamond; Rachel M Presti; Ali H Ellebedy Journal: Nature Date: 2021-06-28 Impact factor: 49.962