ArticleSingle-cell BCR and transcriptome analysis after
influenza infection reveals spatiotemporal dynamics
of antigen-specific B cellsGraphical abstractHighlightsd Integrated, single-cell RNA- and BCR-seq in three organs
after influenza infection
d Switched, mutated memory B cells are continuously
produced from germinal centers
d Lung memory B cells originate from lymphoid organs and
assume residency phenotype
d Memory B cells are derived from both low- and high-affinity
GC precursorsMathew et al., 2021, Cell Reports 35, 109286
June 22, 2021 ª 2021 The Author(s).
https://doi.org/10.1016/j.celrep.2021.109286Authors
Nimitha R. Mathew, Jayalal K. Jayanthan,
Ilya V. Smirnov, ..., Victor Greiff,
Mats Bemark, Davide Angeletti
Correspondence
davide.angeletti@gu.se
In brief
Combining single-cell RNA and BCR
sequencing, Mathew et al. report that,
after influenza infection, memory B cells
(Bmems) are stochastically selected from
both low- and high-affinity germinal
center (GC) precursors. They reveal that
lung Bmems acquire tissue-residency
signatures and continuously differentiate
from the spleen and mediastinal lymph
nodes GCs.ll
OPEN ACCESS
llArticle
Single-cell BCR and transcriptome analysis
after influenza infection reveals spatiotemporal
dynamics of antigen-specific B cells
Nimitha R. Mathew,1 Jayalal K. Jayanthan,1 Ilya V. Smirnov,1 Jonathan L. Robinson,2 Hannes Axelsson,1
Sravya S. Nakka,1 Aikaterini Emmanouilidi,1 Paulo Czarnewski,3 William T. Yewdell,4 Karin Schön,1
Cristina Lebrero-Fernández,1 Valentina Bernasconi,1 William Rodin,1 Ali M. Harandi,1,5 Nils Lycke,1
Nicholas Borcherding,6 Jonathan W. Yewdell,7 Victor Greiff,8 Mats Bemark,1,9 and Davide Angeletti1,10,*
1Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
2Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers
University of Technology, Göteborg, Sweden
3Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm
University, Solna, Sweden
4Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
5Vaccine Evaluation Center, BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
6Department of Pathology and Immunology, Washington University, St. Louis, MO, USA
7Laboratory of Viral Diseases, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
8Department of Immunology, University of Oslo, Oslo, Norway
9Region Västra Götaland, Department of Clinical Immunology and Transfusion Medicine, Sahlgrenska University Hospital, Gothenburg,
Sweden
10Lead contact
*Correspondence: davide.angeletti@gu.se
https://doi.org/10.1016/j.celrep.2021.109286SUMMARYB cell responses are critical for antiviral immunity. However, a comprehensive picture of antigen-specific
B cell differentiation, clonal proliferation, and dynamics in different organs after infection is lacking. Here,
by combining single-cell RNA and B cell receptor (BCR) sequencing of antigen-specific cells in lymph
nodes, spleen, and lungs after influenza infection in mice, we identify several germinal center (GC)
B cell subpopulations and organ-specific differences that persist over the course of the response. We
discover transcriptional differences between memory cells in lungs and lymphoid organs and organ-
restricted clonal expansion. Remarkably, we find significant clonal overlap between GC-derived memory
and plasma cells. By combining BCR-mutational analyses with monoclonal antibody (mAb) expression
and affinity measurements, we find that memory B cells are highly diverse and can be selected from
both low- and high-affinity precursors. By linking antigen recognition with transcriptional programming,
clonal proliferation, and differentiation, these finding provide important advances in our understanding of
antiviral immunity.INTRODUCTION
Viral respiratory infections caused by influenza-, orthopneumo-,
or corona-virus are major concerns worldwide. Influenza A virus
(IAV) is a highly prevalent, respiratory virus that causes signifi-
cant morbidity and mortality in humans (Iuliano et al., 2018).
B-cell-derived antibodies (Abs) are a central feature of adaptive
immunity to viruses. Abs can greatly reduce viral pathogenicity in
primary infections and can provide complete protection against
disease-causing reinfections (Lam and Baumgarth, 2019). In
mice, intranasal (i.n.) infection with IAV initiates B cell responses
in several organs, characterized by a robust, early extrafollicular
plasmablast (PB) response, followed by persistent germinal cen-
ter (GC) formation in the drainingmediastinal lymph nodes (mlns)This is an open access article undand diffuse memory B cell (Bmem) dispersion across several or-
gans (Angeletti et al., 2017; Boyden et al., 2012; Frank et al.,
2015; Joo et al., 2008; Rothaeusler and Baumgarth, 2010). Res-
piratory virus infections can also promote circulating blood cells
to generate inducible bronchus-associated lymphoid tissues
(iBALTs) in the lung parenchyma (Moyron-Quiroz et al., 2004), re-
sulting in the formation of GC-like structures in mouse lungs by
14 days post infection (dpi) with IAV (Denton et al., 2019; Tan
et al., 2019).
The viral surface-glycoprotein hemagglutinin (HA) is the immu-
nodominant target of B cell response to IAV infection and immu-
nization (Altman et al., 2015; Angeletti and Yewdell, 2018).
Nevertheless, comprehensive studies assessing the link be-
tween transcriptional status and the clonal diversity of B cellCell Reports 35, 109286, June 22, 2021 ª 2021 The Author(s). 1
er the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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OPEN ACCESS Article
Figure 1. Dynamics of antiviral B cell response at single-cell resolution is organ specific
(A) Schematic diagram of the experimental setup of influenza infection, cell sorting, followed by scRNA-seq and BCR profiling.
(B) Representative gating for the cell sorting of single HA+ IgD
 B cells.
(C) UMAP plot of unsupervised clustering of HA-specific B cells, combining all organs and dpi.
(D) Mean expression of the top-five marker genes for each cell cluster. Color intensity denotes average expression, whereas dot size is the percentage of cells
expressing the gene.
(E) On the top UMAP plot, as in (C), showing average expression of gene signatures associated with plasma blasts and memory B cell programs from Bhat-
tacharya et al. (2007). On the bottom, enrichment score from GSEA comparing PB-C7s to all other clusters for antibody-secreting cell (ASC) genes versus
follicular B cell (FoB) genes from Shi et al. (2015) and Bmem-C5s to all other clusters for memory genes versus GC genes from Laidlaw et al. (2020).
(legend continued on next page)
2 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESSpopulations at different developmental stages within or between
organs after respiratory viral infections are lacking. Deciphering
how B cell receptor (BCR) characteristics are linked to cell differ-
entiation is crucial for our ability to understand and ultimately
manipulate B cell responses with more-effective vaccines or
adjuvants.
Few studies have identified lung Bmems as critical in prevent-
ing IAV reinfection (Allie et al., 2019; Onodera et al., 2012). These
tissue-resident Bmems (Allie et al., 2019) appear to have broader
specificity than splenic Bmems do (Adachi et al., 2015). Howev-
er, virtually nothing is known about the transcriptional program-
ming leading to their formation, their BCR profile, and whether
they originate from lung-iBALT versus other lymphoid organs.
Better appreciation of the origin and formation of lung-resident
memory cells after infection is a crucial first step in developing
mucosal vaccines against respiratory viruses.
GCs form as a consequence of rapid clonal proliferation dur-
ing T-cell-dependent B cell responses and are the site of B cell
affinity maturation through selection of high-affinity clones
generated via somatic hypermutation (SHM) (Mesin et al.,
2016). Signals that regulate terminal B cell differentiation to
PBs or Bmems have primarily been studied using model anti-
gens and transgenic mice (Kräutler et al., 2017; Phan et al.,
2006; Shinnakasu et al., 2016; Smith et al., 1997; Suan et al.,
2017a, 2017b; Weisel et al., 2016). The general consensus is
that B cells with higher avidity to antigens will differentiate
into PBs, whereas B cells of lower avidity will become Bmems
(Viant et al., 2020; Shinnakasu et al., 2016; Suan et al., 2017a,
2017b). In addition, a temporal switch, with Bmems being pro-
duced only early in the anti-(4-hydroxy-3-nitrophenyl)acetyl
(anti-NP) response was identified (Weisel et al., 2016). Impor-
tantly, unlike most natural responses, both NP and hen egg
lysozyme (HEL) models require only a single mutation for the
germline V region in the BCR to mature from low to high avidity.
Whether a similar selection of lower-avidity GC cells into the
memory compartment occurs after viral infection and how the
selection differs among organs is unclear. A recent study
(Wong et al., 2020) suggested that such affinity selection might
not be that pronounced during flavivirus infection and that
Bmems in the spleen might arise from lower-affinity germline
precursors. This is particularly relevant in understanding how
the first encounter with a virus shapes Bmem formation, a cen-
tral feature of the original antigenic-sin phenomenon in anti-IAV
responses (Henry et al., 2018; Yewdell and Santos, 2021).
To address these questions and to overcome limitations in
previous studies, we sequenced single-HA-specific B cells
to correlate their transcriptome with their paired heavy and
light chain BCRs from different organs and across time points
post-i.n. IAV infection. Our data provide important mecha-
nistic insights into B cell differentiation upon respiratory viral
infection.(F) UMAP plot of GC clusters showing average expression of Aicda and gene sign
(G) Enrichment score from GSEA comparing PreGC-C3 and EarlyGC-C14 to
Fowler et al. (2015).
(H) Alluvial plot showing proportion of cells with defined antibody isotype for eac
(I) Alluvial plot showing proportion of cells for each UMAP cluster, as in (C), divid
(J) UMAP plot of infected mice divided by organ.RESULTS
scRNA-seq of antigen-specific B cells after influenza
infection identifies a range of B cell differentiation
stages
i.n. mouse infection with IAV is a well-established, acute respira-
tory viral infection model. We infected mice i.n. with IAV PR8 and
tracked antigen-specific responses at 7, 14, and 28 dpi by sort-
ing antigen experienced, HA-binding immunoglobulin D (IgD
) B
cells from individual lungs, spleen, andmlns (Figures 1A, 1B, and
S1A). As a control, total B cells (50% of cells based on live B220+
IgD+/
 and 50% of cells based on live B220+ IgD
) were also
sorted from spleen and lungs of two mice (Figure S1B). We sub-
jected antigen-specific cells to single-cell RNA sequencing
(scRNA-seq) paired with single-cell B cell receptor sequencing
(scBCR-seq). In total, we analyzed results from 8,722 cells
from two naive mice and 30,242 cells from eight infected mice
(1,878, 15,428, and 12,936 cells, respectively, from 7, 14, and
28 dpi).
Unsupervised clustering, using the Sauron implementation of
the Seurat package, distinguished 16 populations of HA-specific
B cells that clustered according to their transcriptional profile
(Figure 1C). Differential gene expression analysis allowed us to
define specific cell populations (Figure 1D). Cell populations
were defined using genes canonical for specific differentiation
states, including Ighd, Aicda, Bcl6, Mki67, Cd83, Cd38, Cxcr4,
Ly6d, Cd1d1, Foxo1, Ccr6, Irf4, Sdc1, and Prdm1. Marginal
zone (MZ) B cells (cluster C6) were characterized by landmark
genes Cd1d1 and Cd9. Clusters C1, C4, and C2 comprised a
mix of naive and activated cells expressing Ccr7, Ebf1, Cd74,
and Nr4a1 (Nur77). It is possible that these cells were also
partially activated in vitro by binding to HA. In naive mice,
35%, 74%, and 74% of cells in C1, C2, and C4 expressed
Ighd, respectively, and those clusters were overrepresented
(Figure S1B). Ighm was particularly highly expressed in C1. C7
was high in canonical PB markers Irf4, Slpi, Sdc1, and Prdm1
and was confirmed by previously described gene signatures
(Figure 1E) (Bhattacharya et al., 2007; Shi et al., 2015). In C5,
Bmem-signature genes were highly expressed (Bhattacharya
et al., 2007), and the nature of that population was further
strengthened using gene sets from Laidlaw et al. (2020) (Fig-
ure 1E). Interestingly, GC cluster C9 also showed high expres-
sion of genes associated with Bmem fate (see below for further
discussion). Finally, clusters C3, C8, and C9–16 all expressed
prototypical GC signature genes (Victora et al., 2010, 2012).
We could readily divide GC clusters into light zone (LZ) and
dark zone (DZ) cells using sets of genes known to distinguish
these subsets (Figure 1F) (Victora et al., 2010). Despite having re-
gressed out cell cycle influence, GC clusters still separated
based on cell cycle phase, as expected (Figure S1C). We identi-
fied two LZ clusters (C3 and C14) that expressed geneatures associated with dark and light zone programs from Victora et al. (2010).
all others for genes involved in B cell activation and differentiation from
h cluster.
ed by organ and dpi.
Cell Reports 35, 109286, June 22, 2021 3
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OPEN ACCESS Articlesignatures typical for initiation of signaling cascades and Gene
Set Enrichment Analysis (GSEA) confirmed signatures consis-
tent with recently antigen-activated B cells (Figure 1G) (Fowler
et al., 2015).
Indeed, cluster C3 cells were mostly in G1 phase (PreGC),
whereas C14 cells were entering the cell cycle (earlyGC). Clusters
C15, C10, C16, andC8 had similar signatures, indicative of DZGC
B cells, with only differences in cell cycle status, with C16 in G2M,
C8 andC10 in S phase, andC15 betweenG2MandG1, indicating
cells exiting the cell cycle. Cluster C12 had a strong LZ signature,
as did C11, which was split between G2M and G1 and C13, in the
S phase (Figures S1B and S1C). Comparison of GC clusters using
signatures derived from human tonsils (Holmes et al., 2020),
confirmed our clusters assignments (Figure S1D).
To incorporate BCR sequence data into the overall analysis, we
compiled the sequence data, isotype, and somatic mutations for
the heavy-chain sequences using the Immcantation pipeline
(Gupta et al., 2015; Vander Heiden et al., 2014) and scRepertoire
(Borcherding et al., 2020) to define clonal status and expansion.
Consistent with the recent demonstration of pre-GC class switch-
ing (King et al., 2021; Roco et al., 2019), cluster preGC C3
exhibited more than 60% class-switched BCR sequences (Fig-
ure 1H) and was enriched for the class-switch recombination
signature (King et al., 2021) (Figures S1E and S1F). Therefore,
we identified PreGC-C3 cells as those actively recruited into the
earlyGC C14. Supporting this hypothesis, the fraction of B cells
within these two clusters was almost twice as many at day 7
compared with days 14 and 28. The continued presence of these
clusters weeks after infection is consistent with continued replen-
ishment of the GC reaction (Figure 1I). Approximately one half of
the HA-Bmem cells (cluster C5) had class switched, whereas all
GCcluster cellswere dominated by IgG2b/c. IgABCRswerehigh-
ly enriched both in HA-Bmems and in PBs in all organs, suggest-
ing a preferential recruitment of IgA cells (Figures 1I and S1G).
Antigen-specific clusters are organ specific and
independent of days after infection
Although most clusters were found in all organs, surprisingly, HA+
B cell clusterswere largely specific to organ and not dpi (Figures 1I
and 1J). While the fraction of naive and early activated cells was
somewhat greater at 7 dpi in all organs, the most marked differ-
ence was the distribution among cell types in the three organs
studied. The mln was characterized by strong GC activity with a
smaller proportion of HA-Bmems (3%–6%) and a considerable
number of PBs (from 7% on day 7 to 2% on day 28) (Figure 1I).
Conversely, few HA+ GC B cells were detected in the lungs
(10%), but a remarkably high number of HA-Bmems that rose
from 10% on day 7 to 30% of HA+ cells on days 14 and 28.
PBswere constant between 1%and 2%.HA+B cells in the spleen
exhibited strongGCactivity and a relatively constant proportion of
HA-Bmems (8%–13%) and PBs (0.5%–1%). Interestingly, PB
proportion was the highest inmlns on day 7 but lowest in other or-
gans. This, linked to the mutation analysis (Figure 6) showing
nearly germline BCRs in PBs, indicates an early and selective
expansion of PBs in the mln. Coordinately, we detected a burst
of HA-Bmems in mlns on day 7.
Overall, these data demonstrate early expansion and differen-
tiation of activated HA-specific B cells into PBs and Bmems in4 Cell Reports 35, 109286, June 22, 2021the mln and a gradual increase of HA-Bmems in the lungs, which
seemed to persist during the response.
Identification of Bmem precursors using scRNA-seq
The dynamics of GC B cells have not previously been studied at
single-cell resolution, on different days, and in different organs
after acute viral infection and, further, the identity of Bmem pre-
cursors in the GCs is somewhat controversial (Laidlaw et al.,
2017, 2020; Shinnakasu et al., 2016; Suan et al., 2017a). To
address those issues and decipher the pattern of Bmem differ-
entiation at the single-cell level, we performed trajectory analysis
using Slingshot (Street et al., 2018) and RNA velocity analysis
with scVelo (Bergen et al., 2019). RNA velocity analysis suggests
that preGC C3s differentiate to earlyGC C14s and subsequently
enter GCs (Figure 2A), in accordance with GSEA (Figure 1G).
Further, that analysis suggested that PreMem C9s could poten-
tially have some backflow into Bmems (C5). For trajectory anal-
ysis with Slingshot, we removed cluster PB C7 because that
cluster was clearly disconnected from all others. The trajectory
analysis, with preset start at preGC C3, showed a major trajec-
tory going from LZ to DZ to LZ again, with cells exiting from pre-
Mem C9 and differentiating to HA-Bmem C5s (Figure 2B).
Changes of gene expression along Slingshot pseudotime
showed a marked switch in transcriptional programming at pre-
Mem C9 (Figure 2C). The preMem C9 cluster expressed several
GCmarker genes (Mki67, Aicda, andBcl6) as well as genes such
as Foxo1,Bach2, andCd22 and have highmitochondrial content
(Figure 2D). All these genes and features have been implicated in
Bmem development (Chappell et al., 2017; Jang et al., 2015;
Shinnakasu et al., 2016). Altogether, this cell population closely
resembles the Bmem-precursor cell population previously iden-
tified by Shinnakasu et al. (2016). Further, our analysis suggested
that cells exiting the GC could undergo intermediate states that
can be captured in C4 and C2, hence, partially explaining the
mixture of cell states identified in these clusters.
To further confirm that preMem C9 was a Bmem-precursor
cluster, we ran a series of GSEA analyses. First, we defined
high- and low-affinity scores based on average expression of
genes corresponding to high- and low-affinity cells in Shinna-
kasu et al. (2016). GSEA analysis identified preMem C9 as a LZ
GCB cell population with a low-affinity signature as well as being
more similar to Bmems than to PBs (Figure 2E). In fact, when as-
signing low- and high-affinity scores to clusters, preMemC9 had
the highest ‘‘low-affinity score’’ among all GC-like clusters,
whereas other GC clusters were enriched for the ‘‘high-affinity’’
signature (Figure 2F). Furthermore, preMem C9 cells also
showed expression of genes identified by Laidlaw et al. (2020)
as expressed in pre-memory cells (Figure 2E), despite differ-
ences that exists between our models. Thus, cluster preMem
C9 likely represents a Bmem precursor population in the GC,
characterized by high Mki67, Bcl6, Cd22, Bach2, and Foxo1
expression and high mitochondrial content.
Bmems in the lungs have a distinct transcriptional
profile, independent of isotype
Next, we hypothesized that Bmem transcriptional profile may
be distinct when present in different organs. To address that
question, we performed unsupervised analysis of the HA-Bmem
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Figure 2. RNA velocity and trajectory analysis identify cluster 9 as memory B cell precursors
(A) RNA velocity, as determined by scVelo, projected onto a UMAP. Arrowheads determine predicted direction of the cell movement, and arrow size determines
strength of predicted directionality. In the squares are highlighted cells moving from PreGC-C3 to earlyGC-C14 (top) and cells moving from PreMem-C9 (bottom).
(B) Trajectory inference by Slingshot projected onto a UMAP with PreGC-C3 selected as the starting cluster. On the right, the same graph is shown with
pseudotime coloring. Cluster PB-C7 was excluded because it was clearly disconnected from the others.
(C) List of differentially expressed genes over trajectory-based pseudotime. Colors on top indicate clusters.
(D) UMAP plot of GC clusters showing average expression of selected genes.
(E) Enrichment score from GSEA comparing PreMem-C9 to all others for genes involved in the GC program; the LZ program; the memory cell program; a low-
affinity signature, as described by Shinnakasu et al. (2016); and the PreM cluster, as defined by Laidlaw et al. (2020).
(F) Violin plot showing high- and low-affinity gene expression scores by UMAP clusters, as defined by Shinnakasu et al. (2016). Data are presented as medians
and interquartile rangespopulation (C5) only. This revealed eight subclusters, with the
main determinant of separation being lung versus non-lung local-
ization (Figures 3A–3D). Clusters 0, 1, and 4 were almost
exclusively made from lung HA-Bmems, with most other clusters
encompassing cells from different organs (Figure 3C). Both GC-
dependent and -independent cells, represented by mutated and
unmutated BCR sequences and cells of different isotypes, with
theexceptionofcluster 2,whichwasdominatedby IgM,werepre-
sent in all clusters (Figures 3B and 3D). Although the number and
proportion of IgA HA-Bmems in the lung was higher compared
with that of spleen (Figure S2A), the B cell heavy-chain class
was only a partial determinant in the cell segregation.
Several genes strongly contributed to differential clustering
between lung and spleen/mln clustering (Figure 3E). Interest-
ingly, among the most differentially upregulated genes in the
lungs were Cd69, an adhesion molecule linked to tissue resi-
dency of immune cells, together with Cd83, Ahr, Ccr7, Cxcr4,
and Cd44. Conversely spleen and mln had significantly higherSell expression (encoding CD62L), together with Cd22, Cr2,
Bcl2 andCd55 (Figure 3E). GSEA analysis onCD8 tissue resident
memory signatures revealed striking similarity of lung HA-Bmem
toCD8 tissue-residentmemory (TRM), as opposed to spleen and
mln HA-Bmem (Figure 3F) (Mackay et al., 2013).
Validating the observed differences, PB and GC transcriptional
profiles appeared largely similar between organs (Figures S2D–
S2K). The only detected difference was between IgA PB and
others, as previously reported (Figure S2E) (Neu et al., 2019; Price
et al., 2019). Thanks to our approach, we detected transcriptional
differences in HA-Bmem between germline (likely GC-indepen-
dent) and mutated (GC-dependent) cells, suggesting long term
functional differences, depending on cell origin (Figures 3G and
S2B). Germline HA-Bmem expressed higher levels of Btg1,
Foxp1, Plac8, and other genes that control cell proliferation and
differentiation. On the other hand, highly mutated HA-Bmems
(GC dependent) expressed higher levels of Jchain (IgA specific),
Slpi, Txndc5, Cmah, and others associated with programmingCell Reports 35, 109286, June 22, 2021 5
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OPEN ACCESS Article
Figure 3. Memory B cells in the lungs have a distinct transcriptional programming compared with that of spleen and mln
(A) UMAP plot of unbiased clustering of HA-specific memory B cells (C5 in Figure 1C), combining all organs and dpi.
(B) UMAP plot of unbiased clustering of HA-specific memory B cells, as in (A), colored by the BCR isotype. On the right, an alluvial plot shows the proportion of
cells with a defined isotype per cluster.
(C) UMAP plot of unbiased clustering of HA-specific memory B cells, as in (A), colored by organ. On the right, an alluvial plot shows the proportion of cells
belonging to a specific organ per cluster.
(D) UMAP plot of unbiased clustering of HA-specific memory B cells, as in (A), colored by BCR mutation rate. Germline (not mutated), low (up to 1% nucleotide
mutation), medium (up to 2%), and high (more than 2% mutation). On the right, an alluvial plot shows the proportion of cells with a defined mutation rate per
cluster.
(E) Mean expression of the top-20 marker genes for each organ for Bmems. Color intensity denotes average expression, whereas dot size shows the percentage
of cells expressing the gene.
(F) Enrichment score from GSEA comparing lung Bmems to all others for genes expressed by CD8 TRM.
(G) Mean expression of the top-20 marker genes for cells divided by mutation rate for Bmems. Color intensity denotes average expression, whereas dot size
shows the percentage of cells expressing the gene.
(H) Flow cytometry histograms showing expression of the indicated genes by memory B cells (Dump
 B220+ CD38+ IgD
 IgM
) in lungs and spleen. Repre-
sentative results of three biological replicates with three mice each.
6 Cell Reports 35, 109286, June 22, 2021
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Figure 4. Memory cells broadly disseminate in several organs
(A) Percentage of cells using a specific Vh gene for each mouse, divided by organ.
(B) Hierarchical clustering of Pearson’s correlation of the V gene repertoire. Each tile represents the correlation of the V gene repertoire. Color intensity indicates
correlation strength. See Figure S4 for p values.
(C) Overlap between B cell clones in different organ and cell types, divided by mouse. Each tile represents the overlap coefficient of clones. Color intensity
indicates overlap strength.
(D) Alluvial plots showing clonal origin of Bmems and PBs based on CDR3 sequence, with germline (GC independent) cells excluded from analysis. Top row
shows Bmems, and bottom row shows PBs, divided by organ at each dpi. Grey bar indicates that the clones were not found in any GCs, whereas the color
indicates that clonal relatives were found in GCs.toward PB differentiation. Likewise, PB also transcriptionally
segregated based on mutation rate (Figure S2L).
To validate the transcriptional differences detected in HA-
Bmems, we infected mice and analyzed lung and spleen B cells
at 14 dpi by flow cytometry. This revealed upregulation of CD69
and CD44 in lung Bmems consistent with scRNA-seq data.
CD83 and CXCR4, even if upregulated at the mRNA level,
were not detected on the Bmem surface, as expected given theirrole in GC. Splenic Bmems had higher CD62L, CR2, and CD22
expression levels consistent with scRNA-seq data (Figure 3H).
Collectively, these data demonstrate a distinct transcriptional
profile for Bmems in the lung, with hallmarks of activation and
tissue residency. The low number of HA+ GC B cells in the lung
suggest that most lung Bmems are either GC independent or
derived from GCs in other organs, which acquire new transcrip-
tional signatures once they take residence in the lungs.Cell Reports 35, 109286, June 22, 2021 7
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Figure 5. Clonal expansion in GC is organ specific
(A) Graph showing the percentage of expanded clonotypes for each mouse, divided by organ.
(B) Graph showing clonal expansion for each cluster, divided by dpi and organ. Clones are ordered by their abundance for each cluster, dpi, and organ, and color
indicates the repertoire space occupied by the top-X clones as shown in figure legend.
(legend continued on next page)
8 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESSVh gene usage is mainly organ specific but is shared
among cell types
Whether there is a bias in Vh gene usage and clonotype for cells
differentiating along the PB versus Bmem axes remains a matter
of debate. To investigate that question, we assigned V gene
usage according to International ImMunoGeneTics (IMGT) stan-
dards (Giudicelli et al., 2011). As expected, cells from the unse-
lected naive repertoire were composed of many Vh genes,
whereas we could start observing selection already by day 7
(Figure 4A). At 7 dpi, Vh1-63-expressing B cells (Cb site specific)
dominated the PB and GC response on day 7 (Figure S3A), as
previously reported (Angeletti et al., 2017; Kavaler et al., 1990;
Rothaeusler and Baumgarth, 2010), producing germline or
near-germline Abs (Figure S3B). Extending previous findings,
we also found an increased proportion of these B cells within
the Bmemcompartment in unmutated form, indicating that these
B cells, with high-avidity germline BCRs not only dominated the
extrafollicular PB response but also differentiated into IgM and
switched HA-Bmem (Figure S3C). On day 14, for all individual
mice, Vh gene usage became far more polarized, indicating
vigorous clonal selection. Critically, by day 28, one Vh family
dominated in each mouse, ranging from 35% to 62% of the
response (Figure 4A).
When comparing cell clusters at different time points, our data
show some Vh genes undergo selection as early as 7 dpi in GCs
and that dominant genes also appear among PBs at 14 dpi
(Figure S3D). Finally, we detected some skewed Vh usage in
the HA-Bmem population at 28 dpi, consistent with prolonged
GC selection (Figure S3D). Selection of D and Jh genes was
mainly cell type specific (Figures S3E and S3F). V-gene usage
was private to individual mice, except for three genes (Vh14-2,
Vh1-81, and Vh1-63) (Figure S3G).
We then analyzed the Vh-gene usage overlap among different
mice, organs, and cell types, as defined by uniform manifold
approximation and projection (UMAP) clusters, using a Pear-
son’s correlation matrix (Figures 4B and S3H) (Greiff et al.,
2017a). That analysis provided information on overall Vh-gene
usage, selection, and clonal expansion and revealed that (1)
the V-gene repertoire is mostly clustered by high similarity be-
tween lung and spleen B cells, (2) numerous Vh genes can be
used to generate HA-binding Abs, (3) most of these genes can
be selected into GC and HA-Bmem compartments, whereas
(4) selection in to PB compartment is limited to much fewer Vh,
which are often also present within the GC and HA-Bmem
compartments.
Analyzing clonal overlap based on both heavy and light chains
(Figure S3I), revealed that clustering is dominated by differences
among mice, extending many prior findings (Greiff et al., 2015,
2017a, 2017b; Miho et al., 2019) that, even in mice with nearly(C) Alluvial plots showing clonal sharing between clusters and organs at different
the height of each represents the proportion of the clusters occupied by that clo
organ.
(D) UMAP plot of infected cells for all organs and dpi, colored by clonal expansio
medium (between 6 and 20 cells), large (between 21 and 100 cells), and hyperex
(E) UMAP plot of infected cells divided by organ and dpi, colored by clonal expa
(F) Graphs showing proportion of cells for each clonal-expansion status for each
(G) Pie charts showing the distribution of expanded clones (more than 20 cells sidentical genetic backgrounds, most B cells generate selected
repertoires that emerge stochastically.
Bmem cells disseminate in several organs
The high number of Bmems in the lungs, together with the low
GC activity, made us hypothesize that most Bmems found there
would originate from GCs in other organs. To test whether that
was the case, we studied CDR3 clonal overlap within individual
mice, organs, and cell types. We found that clonal overlap was
mostly organ specific. However, there were notable exceptions,
and in several mice, the HA-Bmempopulations in the lungs over-
lapped with GC and HA-Bmem populations in mlns. Interest-
ingly, PBs in mlns were strongly correlated with mln GC in
most mice, but the same was not always true for PBs in spleen
and lungs (Figures 4C and S3J).
We further investigated clonal sharing between PBs, HA-
Bmems, and GCs in different organs. We only considered
mutated, likely GC-dependent, clones. By day 14, we could
assign almost all PBs inmlns as having aGCorigin by clonal rela-
tionship (Figure 4D). This was not true for most cells in the lung
and spleen because of, at least in part, a low degree of clonal
expansion and limited sampling. However, it should be noted
that, at least for the spleen and mln, overall diversity for each
cluster was similar (Figures S4A and S4B). For HA-Bmems in
the spleen, we identified clonal relatives for only 20% of the
cells. This could be due to high diversity and smaller clonal fam-
ilies that make sampling limiting. We could, however, track as
many as 75% of HA-Bmems in the lung and mln on day 14 (Fig-
ure 1H), despite their high diversity (Figures S4C and S4D). Sur-
prisingly, we observed sharing of GC-derived HA-Bmems, with
spleen GCs being a source of HA-Bmems in mlns and lungs
and mln-derived HA-Bmems present in the lungs and spleen.
These data are consistent with a high degree of dissemination
of GC-derived HA-Bmems among organs.
Clonal expansion is organ specific, and highly expanded
clones seed both PB and Bmem compartments
Having found that clones were shared among several cell types,
we asked whether there would be a bias in selection depending
on the clonal expansion status. Up to 75% of mln antigen-spe-
cific B cells belonged to expanded clonotypes. By contrast, at
most 20% of B cells present in the lung/spleen were expanded
(Figure 5A). Consistently, the top-50 expanded clones repre-
sented more than 50% of the repertoire in mlns, but only 15%–
30% in lungs and spleen (Figure S5A). Expectedly, we detected
no sign of clonal expansion in naive mice. Analyzing clonal
expansion in different clusters, organs, and dpi revealed lower
expansion in splenic GC B cells compared with that of mlns
(Figure 5B).dpi. Each cluster is divided in several bars, representing individual clones, and
ne. Connecting lines indicate the sharing of clones, with colors indicating the
n status. Clones were defined as single (1 cell), small (between 1 and 5 cells),
panded (more than 101 cells) clones.
nsion status.
cluster, divided by dpi and organ.
equenced) in different clusters. Numbers in the chart indicate frequency.
Cell Reports 35, 109286, June 22, 2021 9
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OPEN ACCESS Article
Figure 6. Sustained generation of highly mutated Bmems
(A) Graph showing Vh gene mutation frequency divided by UMAP clusters as in Figure 1C. Data are presented as the median and interquartile range.
(B) Graphs showing Vh gene mutation frequency for each cluster, divided by dpi and organ. Data are presented as median and interquartile range.
(C) Graphs showing Vh genemutation frequency for each organ, divided by dpi and isotype. Two-way ANOVAwith Tukey’s post test: for ‘‘All cells’’ at day 14, each
isotype versus IgM, p < 0.0001; IgA versus IgG2b, p < 0.05; IgA versus IgG2c, p < 0.001; IgA versus IgG3, p < 0.01; IgG2c versus IgG1, p < 0.001. For ‘‘All cells’’ at
day 28, each isotype versus IgM, p < 0.0001; IgA versus IgG1, p < 0.01; IgA versus IgG2b, p < 0.05; IgG2b versus IgG2c, p < 0.0001; IgG2c versus IgG3, p < 0.01;
IgG2c versus IgG1, p < 0.0001; other comparisons ns. For ‘‘GC’’ at day 28, IgG1 versus IgM, p < 0.001; IgG2b versus IgM, p < 0.0001; IgG3 versus IgM, p < 0.01;
IgG2c versus IgG1, p < 0.0001; IgG2c versus IgG2b, p < 0.0001; IgG2c versus IgG3, p < 0.0001. For ‘‘Bmem’’ at day 14, IgA versus IgM, p < 0.0001; IgA versus
IgG2b, p < 0. 0001; IgA versus IgG2c, p < 0. 0001; IgA versus IgG3, p < 0.001. For ‘‘PB’’ at day 28, IgA versus IgM, p < 0.0001; IgA versus IgG2b, p < 0. 0001; IgA
versus IgG2c, p < 0. 0001; IgG1 versus IgM. p < 0.01; IgG2b versus IgM, p < 0.01. All other comparisons are non-significant. Data are presented as medians and
interquartile ranges.
(D) UMAP plots of infected cells divided by organ and dpi, colored bymutation rate. Germline, notmutated; low, up to 1%nucleotidemutation;medium, up to 2%;
and high, more than 2% mutation.
(E) Graph showing proportion of cells for each mutation rate for each cluster, divided by dpi and organ.
(F) Mice were infected with PR8 and injected with EdU at the indicated time windows. At day 35, mice were sacrificed, and lungs were subjected to flow cy-
tometry. Shown is the frequency of EdU+ cells among the HA+ switched-memory-cell population. The experiment was performed once with n = 5 per group. Data
are presented as means.
(G) Violin plots comparing mutation frequency of total and GC-derived Bmems versus PBs. Statistical differences were tested using Student’s t test. Data are
presented as medians and interquartile ranges.
(H) Alluvial plot showing the proportion of PBs with a sequence identical to that of a Bmem, divided by dpi.
10 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESSHA-Bmem had the most BCR-diverse compartment and had
more unique clonotypes (Figure S4). Conversely, PBs, which
started with a few highly expanded clonotypes on day 7, became
more diverse by day 14 and narrowed again over the next
2 weeks. We generated alluvial graphs to assess the extent to
which highly expanded clonotypes are shared among clusters
or preferentially expanded in certain clusters over time (Fig-
ure 5C). On day 7, only a few clonotypes in the GC clusters
were shared among multiple clusters, regardless of the state of
clonal expansion. By day 14, 30%–50% of the highly expanded
clones were present in all GC clusters, and most of the highly
expanded clones also populated HA-Bmem and PB subsets.
Clonal sharing among GC, HA-Bmem, and PB was independent
of clonal size. On day 28, more than 50% of the BCR sequences
were shared among GC clusters, in particular, among highly
expanded clones. Consistent with the state of clonal expansion,
about 50% of the PBs were derived from highly expanded
clones. As in previous dpi, HA-Bmems originated not only from
highly expanded GC families but also from smaller clones (Fig-
ure 5C). When examining individual mice, the day-14 mice
M14_2 and M14_3 stood out, showing that most of the clonal
sharing was among splenic GCs and not mln GCs, whereas
M28_3 at day 28 had more than 75% clonally expanded cells
shared among clusters (Figure S5B).
Wevisualizedclonal expansionbyarbitrarily dividingclones into
five categories: single (1 cell), small (between 1 and 5 cells), me-
dium (between 6 and 20 cells), large (between 21 and 100 cells),
and hyperexpanded (more than 101 cells) clones and rendered
them on the UMAP plot (Figure 5D). This clearly demonstrated
that most of the expanded cell clones were in the GCs but were
also in PB and HA-Bmem clusters. As expected, cells in naive
and MZ clusters mostly belonged to single clones. Interestingly,
clones in PreGC-C3,which are likelymade upof cells just entering
the GCs, were mostly unique, consistent with our hypothesis.
Splitting the UMAP according to organ and day (Figures 5E,
3F, and S5C) revealed that GC clonal expansion is organ depen-
dent. In mlns, medium expanded clones are already present on
day 7 and are large and hyperexpanded clones by day 14.
Conversely, splenic GCs had few medium-sized clones by day
14 and maintained an essentially unmutated expansion profile
on day 28. Likewise, lung GC clones were mostly single or small
with occasional medium-sized clones appearing. These findings
are quite surprising because they suggest organ-dependent
regulation of GC clonal expansion. Notably, the number of
analyzed GC cells in mlns and spleens on day 14 is nearly iden-
tical (n = 2,125 versus 1,828). Nevertheless, to verify that sam-
pling differences didn’t affect our day 28 observation, we
randomly downsampled mlns on day 28 to the same number
of GC cells as those of spleen and reassigned the expansion sta-
tus of clonotypes (Figure S5D). We still detected most GC B cells
to be either large or hyperexpanded, in stark contrast with spleen
GCs, validating our conclusion.
Focusing on highly expanded clones (more than 20 cells), we
could define four patterns: clonotypes present in GCs only, GCs
and PBs, GCs and HA-Bmems, and GCs, HA-Bmems, and PBs.
Remarkably, the proportion of highly expanded clones found
only in the GCs increased from 33% to about 50% from day
14 to day 28. Conversely, the fraction of GC clones sharedonly with PBs decreased, whereas the fraction of HA-Bmems
derived from GCs stayed constant by 28 dpi (Figure 5G).
Together with the overall clonal-expansion status, this observa-
tion indicates that a constant number of GC-derived HA-Bmems
are generated as the immune response progresses, whereas the
PB diversity decreases as PB clones expand.
GC-derived PBs and Bmems have similar mutation rates
and avidity for antigens
According to previous studies, antigen avidity has a clear role in
determining B cell fate. To investigate that, we combined muta-
tion analysis with clonal-expansion data and monoclonal anti-
body (mAb) expression of HA-specific B cells.
In line with what would be expected, naive and MZ cells had
almost no mutations, whereas GC cells were, overall, the most
mutated, followed by PBs and Bmems (Figure 6A). Separating
them by dpi and differentiation clusters, we found that cells
from day 7 mice had very few mutations, indicating that PBs
and Bmems initially derive from the expansion of unmutated
cells. The mutation frequency increased at later dpi, with similar
trends for all cluster and organs (Figure 6B). When considering
the different heavy-chain classes (Figures 6C and S6A), we did
not detect major differences between clusters and organs, with
two exceptions: IgM cells were generally less mutated than
class-switched cells, and IgA cells tended to have a higher mu-
tation rate, starting from day 14. B cell mutation frequencies
among mice were comparable (Figure S6B).
To facilitate mutation analysis, we divided the cells into four
discrete bins: germline (not mutated), low (up to 1% nucleotide
mutation), medium (up to 2%), and high (more than 2%mutation)
(Figure 6D). By day 14, most of the GC cells carried BCRs with
low to medium mutations, whereas on day 28, they had me-
dium/high mutation rates. Comparing the mutation data with
clonal-expansion data (Figure 5F) highlights the different dy-
namics between mlns and other organs.
Approximately 75% of HA-Bmems remained at germline on
day 14 and 50% on day 28 (Figure 6E). Although we cannot
determine the timing of their production, the increased propor-
tion of highly mutated HA-Bmems suggest recent GC origin.
To confirm that, we performed a labeling experiment, in which
we administered 5-ethynyl-20-deoxyuridine (EdU) intraperitone-
ally (i.p.) to mice in a 7-day window after infection (days 1–6,
days 7–13, days 14–20, and days 21–27). After 35 days, we
analyzed lung-switched Bmems and found that HA+ Bmems
are produced constantly, in agreement with our sequencing
data (Figures 6F, S6C, and S6D). More than one half of the unmu-
tated HA-Bmems were of the IgM isotype, but we also detected
IgG and IgA (Figure S2C). Unexpectedly, we found that, when
excluding non-mutated, likely GC-independent, cells, the overall
mutation rates of PB and HA-Bmem BCRs were statistically
indistinguishable, with the exception of HA-Bmems in the lungs
and spleen on day 28, having lower mutation rates than PBs had
in the same organs (Figures 6G and S6E). Similarly, PreMem-C9,
identified by trajectory analysis to be HA-Bmem precursors (Fig-
ure 2), had mutation rates and clonal expansion profiles that
were indistinguishable from all other clusters. In addition, we
found that 22% of PBs had BCR sequences identical to that of
HA-Bmems. Further, mutation distribution did not correlateCell Reports 35, 109286, June 22, 2021 11
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OPEN ACCESS Articlewith clonal size, with families with only five members already
showingmembers with highmutation rate (Figure S6F). Although
an imperfect proxy, higher SHM usually reflects increased Ab
binding avidity (Gitlin et al., 2014; Kocks and Rajewsky, 1988;
Neu and Wilson, 2016). Indeed, high- and low-avidity signatures
correlated with mutation rate (Figure S6G). Finally, we found that
up to 25% of PBs had 100% identity with Bmems (Figure 6H).
To measure affinity, we expressed mAbs from mutated HA-
Bmems and PBs from members of large clonal families. We
generated clonal trees for five families (one from M1 and two
from M2 at 14 dpi and one each from M5 and M6 at 28 dpi) (Fig-
ure 7A). The branching point for differentiation into PBs versus
HA-Bmems appears to be random. In more-complex trees,
some branches gave rise to both PBs and HA-Bmems.
We expressed 23 representative, switchedmAbs (13 fromHA-
Bmems and 10 from PBs). All the mAbs bound the surface of
IAV-infected cells (Figures 7B and S7A), both recombinant and
virus-purified HAs, in ELISA (Figures S7B and S7C), albeit with
different avidity. We then measured their affinity to HAs by bio-
layer interferometry (BLI) (Figure 7C and S7D). Of note, three of
the selected mAbs were identical between PBs and HA-Bmems.
BLI-affinity measurement showed no pattern of differential affin-
ity betweenHA-Bmems and PBs. Themajor determinant of affin-
ity was the clonal family, rather than the cell type or the number of
mutations, similar to what was recently reported for HA-Bmem
recall after immunization (Mesin et al., 2020). Even in the in-
fected-cell-binding assay, the strength of binding was highly
correlated with clonal family. Surprisingly, mAbs from the highly
expanded clonotype 566, with more than 700 sequenced cells
(>70% of all cells of M28_3) actually exhibited low to extremely
low avidity for HAs by BLI. In an extreme example of diverse
avidity within a single clonotype, in clone 660, mAb 11 and
mAb 10, which differ by two amino acids (with one in the
CDR3), exhibit a nearly million-fold difference in KD (2.2 mM
versus 3.4 nM). We confirmed the BLI measurements by testing
the mAbs by ELISA on HAs and HAs from virus and from PR8
virus treated at pH5 to expose hidden epitopes (Figures S7B–
S7D). Interestingly, pH treatment affected mostly mAbs from
one clonal family (1243) by decreasing their apparent KD,
whereas only one mAb had increased KD upon pH treatment
(mAb 10). Although the results did not fully recapitulate the BLI
measurements, they confirmed that all mAbs bound virus, that
there was no difference in apparent avidity between HA-Bmems
and PBs within the same family, and that the clonal family was
the main determinant in avidity.
DISCUSSION
Bcell responses are the cornerstone of preventing viral infections.
A better understanding of how antigen-specific B cell immunity
develops after a respiratory viral infection is crucial for designing
effective vaccines for influenza viruses, parainfluenza viruses,
and, more recently, SARS-CoV-2. How antigen-specific B cells
differentiate before GC entry and from the GC to PBs and Bmems
remains elusive. Here, by combining sorting of antigen-specific
B cells, scRNA-seq and scBCR-seq, we have generated a
detailed map of differentiation stages of de novo B cells in
response to respiratory viral infection. By analyzing events in12 Cell Reports 35, 109286, June 22, 2021lungs, draining lymph nodes, and spleen, our data elucidate the
complex mechanisms involved in B cell responses to infection.
Most seminal discoveries in B cell biology have, so far, been
made in mice immunized with haptens or simple, monovalent
protein antigens (e.g., NP, ovalbumin [OVA], HEL, chicken
g-globulin [CGG]). Such models do not fully recapitulate the
complexity of infectious agents; each of which expresses
dozens to hundreds epitopes and, also, idiosyncratically, acti-
vates innate immunity, which sculpts the adaptive response.
Indeed, recent studies that have used more-complex protein
immunogen for immunization (IAV-HA) have challenged estab-
lished principles of B cell differentiation (Kuraoka et al., 2016;
Mesin et al., 2020).
We found that lungs harbor a large population of HA-specific
Bmems, much more abundant than expected from the number
of iBALT HA-specific GC B cells. This, together with the fact
that lung HA-Bmems exhibit a distinct transcriptional profile,
lead us to conclude that Bmems generated in other organs
emigrate to lungs, as previously speculated, but not formally
shown, by Allie et al. (2019). It should be noted that some of
the increased expression of activation markers in lungs Bmems
could be due to the enzymatic digestion we performedwhile pre-
paring these tissues. Here, we show that GC-derived Bmems in
the lungs can be generated in spleenGCs up to day 14 and inmln
GCs up to day 28 and, subsequently, traffic to the lungs where
they become tissue resident, by upregulating Cd69, Cd44, and
Ahr and downregulating CD62L (Sell), Cr2, Cd22, among others.
Our findings also highlight the complexity of the Bmem compart-
ment, reflecting a need for specialization and rapid response of
Bmems, depending on organ.
Surprisingly, we found different rates of clonal expansion in
GCs from mln and spleen, despite similar diversity, even after
subsampling to equalize cell numbers. It is possible that clonal
bursts (Tas et al., 2016) may be more common in mlns because
the total number of GCs is lower or because of increased/persis-
tent antigen levels. Alternatively, clonal expansion could be
similar, but splenic GCs may experience increased apoptosis.
Whatever the explanation, the net result is the presence of a
few hyperexpanded clonal families in mln GCs and many small
clonal families in spleen GCs.
Notably, previous studies using NP and HEL immunization
models suggested a switch in the output of PBs and Bmems,
with early Bmems being unswitched, followed by switched
immunoglobulin (swIg), Bmems (between weeks 1 and 2) and,
then, on day 21, the generation of PBs (Weisel and Shlomchik,
2017). Except for early IgMBmems, the response to IAV infection
differs from this simplified model, featuring a constant output of
PBs and Bmems from GCs, as judged by the mutation rate and
EdU-labeling experiment. The strong correlation between clonal
size and, importantly, mutational pattern suggests that Bmems
are output constantly from GCs.
The origin of Bmem from GCs has been hotly debated. Both
inductive and stochastic models for Bmem differentiation have
been proposed. Recently, it was proposed that Bmem precur-
sors in the GCs are selected into the memory compartment
because of lower affinity as compared with PBs (Shinnakasu
et al., 2016; Suan et al., 2017a). This notion is based on NP
and HEL immunization using BCR transgenic mice. In both
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Article OPEN ACCESS
Figure 7. mAbs derived from Bmems and PBs have similar affinity for HAs
(A) Clonal trees of five selected clonal families from four mice at different dpi. Color indicates cell types, and each circle or square is a cell that was sequenced in
our experiments. Where symbols are missing between junctions, it denotes an inferred member of the clonal family. Expressed mAbs are indicated by name.
(B) Madin-Darby canine kidney (MDCK) cells were infected with a PR8-mcherry virus and, at 5 h after infection, were stained with mAbs and detected with anti-
mouse k. The histogram shows the binding to viral HA.
(C) Characteristics of the expressed mAbs including KD value measured by BLI.
Cell Reports 35, 109286, June 22, 2021 13
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OPEN ACCESS Articlecases, just one amino acid substitution is needed to dramatically
improve BCR avidity. To relate this to anti-viral B cell responses,
we first examined the prototypical C12 idiotype in the anti-HA
response, first described by Kavaler et al (1990), which is specific
for the Cb antigenic site of HA. These cells carry a BCR with high
germline affinity for HA, rapidly differentiate into extrafollicular
plasma cells, and do not participate in secondary responses to
flu and, therefore, were assumed not to be forming memory
(Kavaler et al., 1991; Rothaeusler and Baumgarth, 2010). Howev-
er, our analysis shows that these cells, of high affinity, are also
capable of forming Bmems both through GC-dependent and -in-
dependent pathways.
Based on pseudotime analysis and comparison with previ-
ously identified gene signatures, we identified PreMem-C9 as
Bmem-precursor cells. This cluster was somehow similar to
the one identified by Laidlaw et al. (2020); however, in lympho-
cytic choriomeningitis virus (LCMV) chronic infection, Bmems
peak at day 11 (Laidlaw et al., 2017) while they are constantly
produced after influenza infection. It is possible that B cells un-
dergo different signaling in chronic, versus acute, infection,
and it is unclear how the results are representative of chronic in-
fections in which there is extensive remodeling of lymphoid tis-
sues. Although somatic hypermutation is not a perfect proxy
for affinity, it is suggestive that these cells underwent a similar
number of selection cycles in the GC. Importantly, we did not
sample long-lived plasma cells in the bone marrow; however, af-
ter infection, PBs can persist in tissues and mucosa for a long
time (Hyland et al., 1994; Jones and Ada, 1987; Khodadadi
et al., 2019; Wolf et al., 2011). By expressing a number of
mAbs from different clusters of five hyperexpanded clonal fam-
ilies, we found clonality to be the major determinant for affinity
differences among B cells, a finding that would have been obvi-
ously impossible when using transgenic monoclonal mice.
Importantly, soluble mAb expression might not fully recapitulate
the complex GC environment in which avidity is also determined
by multivalency and BCR density on B cells (Lingwood et al.,
2012; Slifka and Amanna, 2019; Tolar and Pierce, 2010). Further,
HA used for avidity measurements might not be in the same form
presented in GCs on follicular dendritic cells (FDCs), but, never-
theless, the results were reproducible on HAs expressed on the
surface of infected cells.
Recently, two studies suggested that even complex antigen
Bmems are selected from lower-affinity germline cells (Wong
et al., 2020) and that most of them do not bind antigens (Viant
et al., 2020). A caveat of our study is that, by using antigen-
sorted cells, we might miss B cells of the lowest avidity, which
can be activated by multimeric binding in vivo (in particular,
from the Bmem pool). According to a previous study, these
should be approximately 50% of GC cells and 65% of Bmems
(Viant et al., 2020) for a tetrameric protein, such as the one
used here. Conversely, we might be missing some of the plasma
cells that express the lower amount of BCRs on the surface.
However, the diversity results from our data suggest that most
of the Bmem selection from the GCs might be stochastic (Blink
et al., 2005; Good-Jacobson and Shlomchik, 2010; Pélissier
et al., 2020; Smith et al., 2000), from both low- and high-affinity
precursors, whereas PBs are selected almost exclusively from
expanded clones. This is supported by the detection of a high14 Cell Reports 35, 109286, June 22, 2021proportion of cells expressing identical BCRs in both HA-
Bmem and PB compartments. Our findings are consistent with
the previous reports, i.e., we find low-affinity cells only among
Bmems, the difference is that, by analyzing a large number of se-
quences, we are better able to capture a larger fraction of the
highly diverse antigen-specific Bmem repertoire. As previously
suggested (Baumgarth, 2013), the goal of such a diverse
Bmem population would be to sample as much as the HA-reac-
tive repertoire as possible for the host to be prepared for subse-
quent infection with a variable virus. Our data indicate a possible
mechanism behind ‘‘original antigenic sin’’ (Fazekas de St. Groth
and Webster, 1966) and highlight current challenges to
designing immunogens that are able to recall broadly neutral-
izing Bmem cells of defined specificity from a highly diverse
repertoire.
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 Materials availability
B Data and code availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Mice
B Cell lines
d METHOD DETAILS
B Mice infection
B Cell sorting of hemagglutinin-specific B cells
B Flow cytometry
B Generation and sequencing of single cell gene expres-
sion and enriched B cell libraries
B Single-cell RNA-seq data processing
B Trajectory inference analysis and RNA velocity
B BCR sequence data processing
B Identification of clones and diversity using scReper-
toire
B Differential gene expression analysis
B Gene set enrichment analysis (GSEA)
B Generation of clonal trees and expression of mono-
clonal antibodies
B Bio-layer interferometry
B Infected cells binding assay
d QUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
celrep.2021.109286.
ACKNOWLEDGMENTS
We would like to thank the staff at the Experimental Biomedicine (EBM) core
facility at the University of Gothenburg for animal management; E. Rekabdar
and A. Almstedt, Genomics core facility at Sahlgrenska Hospital for running
the sequencing and data pre-processing; M. Bäckstrom and R. Lymer,
ll
Article OPEN ACCESSMammalian Protein Expression (MPE) core facility at the University of Gothen-
burg for recombinant HA production and purification; D. Anastasakis, NIAMS,
NIH, for help retrieving data sets for GSEA; and A. Svitorka-Härtlova for assis-
tance with figure creation with Biorender. D.A. is supported by a European
Research Council starting grant (B-DOMINANCE, grant no. 850638), a Swed-
ish Research Council starting grant (grant no. 2017-01439), the Jeanssons
Foundation (grant nos. JS2018-0011 and JS2019-0038), the Claes Groschin-
sky Foundation (grant no. M18237), the Institute of Biomedicine at the
University of Gothenburg and Bioinformatic long-term support by the National
Bioinformatics Infrastructure Sweden (NBIS) at SciLifeLab by the Knut and
Alice Wallenberg Foundation (grant no. 1910). J.L.R. and P.C. are financially
supported by the Knut and Alice Wallenberg Foundation as part of the NBIS
at SciLifeLab. The computations were enabled by resources in project SNIC
2019/8-255 provided by the Swedish National Infrastructure for Computing
(SNIC) at UPPMAX, partially funded by the Swedish Research Council through
grant agreement no. 2018-05973. A.M.H. is supported by funding from the
European Union’s Horizon 2020 research and innovation programme under
SHIGETECVAX project (grant agreement no. 815568) and VASA project (grant
agreement no. 815643), and the Innovative Medicines Initiative 2 Joint
Undertaking under VSV-EBOPLUS project (grant agreement no. 116068).
V.G. is supported by the UiO World-Leading Research Community, the
UiO:LifeSciences Convergence Environment Immunolingo, and an EUHorizon
2020 iReceptorplus (no. 825821) project. M.B. is supported by the Swedish
Research Council (grant no. 2019-01708). The graphical abstract was created
using Biorender.com.
AUTHOR CONTRIBUTIONS
Conceptualization, D.A.; methodology, N.R.M., J.K.J., I.V.S., J.L.R., H.A.,
S.S.N., A.E., P.C., N.B., V.G., M.B., and D.A.; formal analysis, N.R.M., J.K.J.,
J.L.R., H.A., S.S.N., P.C., M.B., and D.A.; investigation, N.R.M., I.V.S., H.A.,
S.S.N., A.E., K.S., C.L.-F., V.B., W.R., M.B., and D.A.; data curation, J.K.J.,
J.L.R., P.C., N.B., and D.A.; software, J.L.R., P.C., N.B., and V.G.; resources,
W.T.Y., A.M.H., N.L., N.B., J.W.Y., and V.G.; project administration, N.R.M.
and D.A.; supervision, A.M.H., N.L., and D.A.; funding acquisition, D.A.; writing
– original draft, D.A.; writing – review & editing, N.R.M., J.K.J., I.V.S., J.L.R.,
S.S.N., A.E., P.C., W.T.Y., A.M.H., N.B., J.W.Y., V.G., M.B., and D.A.
DECLARATION OF INTERESTS
The authors declare no competing interests.
Received: February 21, 2021
Revised: May 7, 2021
Accepted: June 1, 2021
Published: June 22, 2021
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OPEN ACCESS ArticleSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Hamster anti-mouse CD3e BV510 BD Biosciences 563024; RRID:AB_2737959
Rat anti-mouse/human CD45R/B220 PE/Cy7 Biolegend 103222; RRID:AB_313005
Rat anti-mouse/human CD45R/B220 APC/Cy7 Biolegend 103224; RRID:AB_313007
Mouse anti-mouse NK-1.1 BV510 Biolegend 108737; RRID:AB_2562216
Rat anti-mouse CD38 FITC BD Biosciences 558813; RRID:AB_397126
Rat anti-mouse IgD BUV395 BD Biosciences 564274; RRID:AB_2738723
Rat anti-IgD Pacific Blue Biolegend 405712; RRID:AB_1937244
Rat Anti-Mouse IgD BV786 BD Biosciences 563618; RRID:AB_2738322
Rat anti-IgM BUV395 BD Biosciences 564025; RRID:AB_2738550
Hamster anti-mouse CD69 BUV737 BD Biosciences 564684; RRID:AB_2738891
Rat anti-mouse CD62L BV711 Biolegend 104445; RRID:AB_2564215
Rat anti-mouse CCR7 BV605 Biolegend 120125; RRID:AB_2715777
Rat anti-mouse CD44 PE/Dazzle 594 Biolegend 103055; RRID:AB_2564043
Rat anti-mouse CD180 BV711 BD Biosciences 740765; RRID:AB_2740428
Mouse anti-mouse CD22.2 BUV737 BD Biosciences 741732; RRID:AB_2871102
Rat anti-mouse CXCR4 PE/Dazzle 594 Biolegend 146513; RRID:AB_2563682
Rat anti-mouse CD83 PE Biolegend 121507; RRID:AB_572014
Rat anti-mouse CR2/CR1 BV421 Biolegend 123421; RRID:AB_10965544
Rat anti-mouse CD38 BV786 BD Biosciences 740887; RRID:AB_2740536
Hamster anti-mouse CD95 BUV737 BD Biosciences 741763; RRID:AB_2871122
Rat anti-mouse IgG1 PE/CF594 BD Biosciences 562559; RRID:AB_2737654
Rat anti-mouse CD80 BV650 Biolegend 104732; RRID:AB_2686972
Rat anti-mouse CD273 (B7-DC, PD-L2) BV421 Biolegend 107219; RRID:AB_2728127
Rat anti-mouse CD19 Alexa fluor 700 Biolegend 115527; RRID:AB_493734
Rat anti-mouse IgM PE BD Biosciences 553409; RRID:AB_394845
Streptavidin APC Invitrogen S868
HRP Horse Anti-Mouse IgG Vector Laboratories PI-2000-1
BV421 Rat Anti-Mouse Ig, k Light Chain BD Biosciences 562888; RRID:AB_2737867
Bacterial and virus strains
TOP10 Invitrogen/ThermoFisher Cat#C404050
Mouse Influenza A/Puerto Rico/8/34 (PR8) This lab N/A
influenza strain grown in 10 day old embryonated
chicken eggs
Mouse Influenza A/Puerto Rico/8/34 (PR8) (Kosik et al., 2019) N/A
influenza strain grown in 10 day old embryonated
chicken eggs
Chemicals, peptides, and recombinant proteins
Recombinant antibodies This paper N/A
AviTagged recombinant hemagglutinin (PR8) Mammalian Protein N/A
Expression Core Facility
(University of Gothenburg)
Critical commercial assays
Hanks Balanced Salt Solution (HBSS) Lonza 10-527F
DMEM, high glucose, GlutaMAX Supplement, pyruvate ThermoFisher Scientific 31966021
(Continued on next page)
e1 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESS
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
Click-iT Plus EdU Alexa Fluor 488 Flow Cytometry Assay Kit ThermoFisher Scientific C10633
Lung Dissociation Kit, mouse Miltenyi 130-095-927
LIVE/DEAD Fixable Aqua Dead Cell Stain Kit Invitrogen L34957
Chromium Single Cell 50 Library & Gel Bead Kit 10X Genomics 1000006
Chromium Single Cell 50 Library Construction Kit 10X Genomics 1000020
Chromium Single Cell V(D)J Enrichment Kit, Mouse B Cell 10X Genomics 1000072
Chromium Single Cell A Chip Kit, 16 rxns 10X Genomics 1000009
Chromium i7 Multiplex Kit 10X Genomics 120262
NextSeq 500/550 High Output Kit v2.5 (150 Cycles) Illumina 20024907
NovaSeq 6000 S1 Reagent Kit v1.5 (100 cycles) Illumina 20028319
NextSeq 500/550 Mid Output Kit v2.5 (300 Cycles) Illumina 20024905
MiSeq Reagent Kit v2 (300-cycles) Illumina MS-102-2002
HiTrap Protein G High Performance Sigma Aldrich GE17-0404-03
1-step Ultra TMB-ELISA Thermo Fisher 34029
EasySep Mouse Pan-B Cell Isolation kit Stem Cell Technologies 19844
Deposited data
Raw sequencing data files for single-cell RNA sequencing This paper ArrayExpress (E-MTAB-9478)
Raw sequencing data files for single-cell VDJ sequencing This paper (ArrayExpress) E-MTAB-9491
Experimental models: Cell lines
Expi293F ThermoFisher Scientific Cat#A14527
MDCK Lab of Jonathan W. Yewdell N/A
Experimental models: Organisms/strains
C57BL/6NTac Taconic Biosciences B6-F
Oligonucleotides
Primer for plasmid sequencing: This paper N/A
50-CTAACAGACTGTTCCTTTCCATG-30
Recombinant DNA
Mouse IgG1 Heavy chain expression vector Lab of Jonathan W. Yewdell N/A
Mouse kappa chain expression vector Lab of Jonathan W. Yewdell N/A
Software and algorithms
Graphpad Prism 9 Graphpad RRID: SCR_002798
FlowJo version 10 Tree Star RRID: SCR_008520
Excel Microsoft RRID: SCR_016137
Magellan Tecan https://lifesciences.tecan.com/software-
magellan
R (version 3.6) The Comprehensive R Archive https://cran.r-project.org/
Network
RStudio (version 1.1.463) RStudio, Inc. https://www.rstudio.com/
Seurat (v3.0.1) Stuart et al., 2019 RRID: SCR_007322
Sauron This lab https://github.com/angelettilab/
scMouseBcellFlu
Scripts for scRNA seq processing This lab https://github.com/angelettilab/
scMouseBcellFlu
tradeSeq (Van den Berge et al., 2019) RRID: SCR_019238
Velocyto command line tool (La Manno et al., 2018) https://velocyto.org/velocyto.py/
scVelo (Bergen et al., 2019) RRID: SCR_018168
(Continued on next page)
Cell Reports 35, 109286, June 22, 2021 e2
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OPEN ACCESS Article
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
Immcantation toolbox (v4.0.0)
TIgGER (Gadala-Maria et al., 2015) https://cran.r-project.org/web/packages/
tigger/index.html
SHazaM (Gupta et al., 2015) https://cran.r-project.org/web/packages/
shazam/index.html
Change-O (Gupta et al., 2015) https://changeo.readthedocs.io/en/stable/
overview.html
scRepertoire (Borcherding et al., 2020) https://github.com/ncborcherding/
scRepertoire
Ggalluvial http://corybrunson.github.io/ https://cran.r-project.org/web/packages/
ggalluvial/ ggalluvial/index.html
pRESTO Vander Heiden et al., 2014 RRID: SCR_001782
Ggplot2 https://ggplot2.tidyverse.org/ RRID: SCR_014601
Destiny (Angerer et al., 2016) https://bioconductor.org/packages/
release/bioc/html/destiny.html
Immcantation (Gupta et al., 2015; https://immcantation.readthedocs.io/
Vander Heiden et al., 2014) en/stable/
Slingshot (Street et al., 2018) RRID: SCR_017012
Fgsea (Korotkevich et al., 2019) RRID: SCR_020938
Alakazam (Gupta et al., 2015) https://cran.rstudio.com/web/packages/
alakazam/index.html
iGraph https://igraph.org/ RRID: SCR_019225
Octet Software Version 10.0 Forté Bio https://www.sartorius.com/en/products/
protein-analysis/octet-systems-software
PHYLIP https://evolution.genetics. RRID: SCR_006244
washington.edu/phylip.html
Affinity Designer Affinity RRID: SCR_016952
10X Cell Ranger package 10X Genomics https://support.10xgenomics.comRESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Davide
Angeletti (davide.angeletti@gu.se).
Materials availability
The information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact. All plasmids
generated in this study are available from the lead contact with a completed Materials Transfer Agreement.
Data and code availability
The complete workflow and associated scripts are available on https://github.com/angelettilab/scMouseBcellFlu. A set of
instructions on how to use the workflow and completely reproduce the results shown herein are available there. Raw sequencing
data files for single-cell RNA sequencing and single-cell VDJ sequencing are available at ArrayExpress: E-MTAB-9478 and
E-MTAB-9491.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Mice
All the experiments were conducted according to the protocols (Ethical permit number: 1666/19) approved by regional animal ethics
committee in Gothenburg. C57BL/6 mice were purchased from Taconic Biosciences, Denmark. They were housed in the specific
pathogen free animal facility of Experimental Biomedicine Unit at the University of Gothenburg. Female mice, which are eight to
twelve weeks old, were used in the experiments.e3 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESSCell lines
MDCK cells were cultured in DMEM (ThermoFisher Scientific) supplemented with 10% heat inactivated FBS (ThermoFisher
Scientific) under 5%CO2 atmosphere at 37
C. Expi293F cells were cultured in Expi293 ExpressionMedium (ThermoFisher Scientific)
under 8% CO2 atmosphere at 37
C in an orbital shaker (125 rpm).
METHOD DETAILS
Mice infection
Mice were anesthetized with isoflurane and infected through nasal inoculation with 50 TCID50 Influenza A/Puerto Rico/8/34 (PR8)
(Molecular clone; H1N1) diluted in HBSS containing 0.1% BSA. For EdU labeling experiments, infected mice were injected i.p. daily
with 1mg of EdU per mouse.
Cell sorting of hemagglutinin-specific B cells
C57BL/6 mice were infected with PR8 H1N1 virus and were euthanized on different days post- infection. Lungs, spleen and medi-
astinal lymph nodes (mln) were isolated. The same organs from naive mice were used as controls. Spleen and mln were mashed and
passed through a 70mm filter to obtain single cell suspension. Lungs were perfused and processed into single cell suspension using
the mouse lung dissociation kit (Miltenyi Biotec) according to manufacturer’s instruction. Splenocytes and lung cells were enriched
for total B cells using the EasySep Mouse Pan-B Cell Isolation kit (StemCell Technologies) while whole mln cells were used for
downstream processing. The cells were incubated for one hour at 4C with a cocktail of fluorochrome-labeled antibodies consisting
of anti-CD3-BV510 (cat. no: 563024, BD), anti-B220-PE-Cy7 (cat. no: 103222, Biolegend) and anti-IgD-Pacific Blue (cat. no: 405712,
Biolegend), and 1mg/ml biotinylated recombinant hemagglutinin (rHA) (Whittle et al., 2014) conjugated to streptavidin APC (cat. no:
S868, Invitrogen). To exclude dead cells, the cells were washed and stained with LIVE/DEAD Fixable Aqua Dead Cell Stain (cat. no:
L34957, Invitrogen) according to manufacturer’s instruction. A maximum of 10,000 live HA-specific mature B cells
(CD3-B220+IgD-rHA+) were sorted and collected in a BD FACSAria fusion or BD FACSAria III (BD Biosciences) cell sorter and
processed immediately.
Flow cytometry
All the fluorochrome-labeled antibodies used in flow cytometry were titrated for determining the optimal concentration. Briefly,
spleens and lungs were harvested from C57BL/6 mice on day 14 post-PR8 H1N1 infection after euthanization. Spleens were
processed into single cell suspension by mashing them and passing through a 70mm filter. Lungs were processed into single cell
suspension using the mouse lung dissociation kit (Miltenyi Biotec). The cells were stained with indicated fluorochrome conjugated
antibodies. The complete list of antibodies can be found in Key Resources Table. The cells were stained with fluorochrome labeled
antibodies for 20 min at 4C. After washing, the cells are stained with LIVE/DEAD Fixable Aqua Dead Cell Stain (cat. no: L34957,
Invitrogen) to exclude dead cells. For EdU experiments, after surface staining the Click reaction was performed using Click-iT
Plus EdU Alexa Fluor 488 Flow Cytometry Assay Kit (Thermo Fisher, C10633), according to manufacturer’s instructions. The labeled
cells were run and the data was acquired on the BD LSR Fortessa X-20 (BD Biosciences) and was analyzed using Flow-Jo software
(Tree Star).
Generation and sequencing of single cell gene expression and enriched B cell libraries
Nearly 1500-10,000 sorted HA-specific mature B cells from individual organs were processed into single cells in a chromium
controller (10X genomics). During this process, individual cells are embedded in Gel Beads-in-emulsion (GEMs) where all generated
cDNA share a common 10X oligonucleotide barcode. After amplification of the cDNA, 50gene expression library and enriched B cell
library, with paired heavy and light chain were generated from cDNA of the same cell using Chromium single cell VDJ reagent kit (V1.1
chemistry, 10X genomics). The 50gene expression libraries were sequenced in NextSeq or NovaSeq6000 sequencer (Illumina) using
NextSeq 500/550 v2.5 sequencing reagent kit (23 75 bp) or NovaSeq S1 sequencing reagent kit (23 100 bp) (Illumina) respectively.
The enriched B cell libraries were sequenced in NextSeq or MiSeq sequencer using NextSeqMid Output v2.5 sequencing reagent kit
(2 3 150 bp) or MiSeq Reagent Kit v2 (2 3 150 bp) (Illumina) respectively.
Lungs from mice M0_1, M7_2 and M14_1 and spleen from M28_3 failed to yield good quality GEMs and libraries and were not
sequenced.
Single-cell RNA-seq data processing
Single-cell RNA-seq data was processed in R with Sauron (https://github.com/NBISweden/sauron), which primarily utilizes the
Seurat (v3.0.1) package (Stuart et al., 2019). This workflow comprises a generalized set of tools and commands to analyze single
cell data in a more reproducible and standardized manner, either locally or in a computer cluster. The complete workflow and asso-
ciated scripts are available on https://github.com/angelettilab/scMouseBcellFlu. A set of instructions on how to use theworkflow and
completely reproduce the results shown herein are available there.
Raw UMI count matrices generated from the cellranger 10X pipeline were loaded and merged into a single Seurat object. Cells
were discarded if they met any one of the following criteria: percentage of mitochondrial counts > 25%; percentage of ribosomalCell Reports 35, 109286, June 22, 2021 e4
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OPEN ACCESS Article(Rps or Rpl) counts > 25%; number of unique features or total counts was in the bottom or top 0.5% of all cells; number of unique
features < 200; Gini or Simpson diversity index < 0.8. Furthermore, mitochondrial genes, non-protein-coding genes, and genes
expressed in fewer than 5 cells were discarded, whereas the immunoglobulin genes Ighd, Ighm, Ighg1, Ighg2c, Ighg2b, Ighg3,
Igha, and Ighe were retained in the dataset regardless of their properties.
Gene counts were normalized to the same total counts per cell (1000) and natural log transformed (after the addition of a pseudo-
count of 1). The normalized counts in each cell were mean-centered and scaled by their standard deviation, and the following vari-
ables were regressed out: number of features, percentage of mitochondrial counts, and the difference between theG2Mand S phase
scores.
Data integration across cells originating from different samples, time points and tissues were done on regressed scaled counts
using the mutual nearest neighbors (MNN) (Haghverdi et al., 2018) on a set of highly variable genes (HVGs) identified within each
sample individually and combined. The top 20 nearest neighbors (k) with a final dimensionality of 51 were used. Uniform Manifold
Approximation and Projection (UMAP) (McInnes et al., 2018) was applied to theMNN-integrated data to further reduce dimensionality
for visualization (2 dimensions) or for unsupervised clustering (10 dimensions).
At this stage, differential expression between clusters, and cell correlation with cell-type specific gene lists were evaluated to
identify clusters of non-B cells (such as NK or T cells). Predicted non-B cells were removed from the data, and the entire single-
cell RNA-seq processing pipeline was re-run using only the remaining B cells. Finally, hierarchical clustering was performed on
the 10-dimensional UMAP embedding using Ward’s method with Euclidean distances to define 16 clusters of B cell subtypes, which
were then visualized on the 2-dimensional UMAP embedding.
Trajectory inference analysis and RNA velocity
Trajectory inference analysis was performed on a diffusion map embedding (20 diffusion components; DCs) of the MNN-
integrated count data using the destiny package (Angerer et al., 2016). The cell differentiation lineages were then predicted
from the DCs using the slingshot package (Street et al., 2018). Cluster 3 (cells entering the GC) was specified as the starting
point and cluster 5 as the end point (Bmem). Distance along the resulting curve was used to define the position of each cell
in pseudotime. Identification of differentially expressed genes was done by fitting a generalized additive model (GAM) to the
trajectory curve using the tradeSeq package (Van den Berge et al., 2019), allowing us to detect which genes exhibited expres-
sion behavior that was most strongly associated with progression along the defined lineage. Single-cell RNA-seq BAM files
were processed using the velocyto command line tool (La Manno et al., 2018) to quantify the amount of unspliced and spliced
RNA reads of each gene in each sample.
The scVelo package (Bergen et al., 2019) was used to perform the RNA velocity analysis. The first- and second-order moments for
velocity estimation were calculated using the MNN-integrated data as the representation, and the cell velocities were computed
using the likelihood-based dynamical model. A velocity graph was calculated based on cosine similarities between cells, and cell
velocities were visualized as streamlines overlaid on the 2-dimensional UMAP embedding.
BCR sequence data processing
The BCR sequence data was processed using the Immcantation toolbox (v4.0.0) using the IgBLAST and IMGT germline sequence
databases, with default parameter values unless otherwise noted. The IgBLAST database was used to assign V(D)J gene annotations
to the BCR FASTA files for each sample using the Change-O package (Gupta et al., 2015), resulting in a matrix containing sequence
alignment information for each sample for both light and heavy chain sequences.
BCR sequence database files associated with the same individual (mouse) were combined and processed to infer the genotype
using the TIgGER package (Gadala-Maria et al., 2015) as well as to correct allele calls based on the inferred genotype. The SHazaM
package (Gupta et al., 2015) was used to evaluate sequence similarities based on their Hamming distance and estimate the distance
threshold separating clonally related from unrelated sequences. The predicted thresholds ranged from 0.096 to 0.169, where a
default value of 0.1 was assumed for cases when the automatic threshold detection failed. Ig sequences were assigned to clones
using Change-O, where the distance threshold was set to the corresponding value predicted with SHazaM in the previous step.
Germline sequences were generated for each mouse using the genotyped sequences (FASTA files) obtained using TIgGER (Ga-
dala-Maria et al., 2015). BCRmutation frequencies were then estimated using SHazaM. TheBCR sequence data, clone assignments,
and estimated mutation frequencies were integrated with the single-cell RNA-seq data by aligning and merging the data with the
metadata slot in the processed RNA-seq Seurat object.
Identification of clones and diversity using scRepertoire
scRepertoire (Borcherding et al., 2020) was used to determine clonal groups based on paired heavy and light chains. This package
uses the filtered contig annotation obtained from cell ranger. Clones were assigned only for cells where high quality paired heavy and
light chains were sequenced. Clones were assigned based on the CTstrict function per each mouse. The CTstrict function considers
clonally related two sequences with identical V gene usage and > 85% normalized hamming distance of the nucleotide sequence.
Percent of unique clonotypes were obtained using the quantContig function. Integration with the Seurat object was done using the
combineExpression function. Ranking of clones were determined using the clonalProportion function and Shannon and Simpson’s
diversity determined using the clonalDiversity function. All functions were run using the exportTable = T function to obtain a table ande5 Cell Reports 35, 109286, June 22, 2021
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Article OPEN ACCESScustomarily facet the graph in R using the ggplot package. Sharing of clones between clusters was visualized using the ggalluvial
package.
Differential gene expression analysis
Differentially expressed genes between different clusters, organs, isotypes or differentially mutated cells were identified using the
FindAllMarkers function from Seurat using default settings (Wilcoxon test and Bonferroni p value correction). Significant genes
with average log fold change > 0.25 and expressed in > 25% of cells in that group were ranked according to fold change and rep-
resented in the FeaturePlot.
Gene set enrichment analysis (GSEA)
For GSEA analysis, differentially expressed genes for each cluster or organ were calculated using the Wilcoxon rank sum test via the
wilcoxauc function of the presto package using default parameters (including Benjamini-Hochberg false discovery rate correction)
and filtered on logFC > 1 and padj < 0.05. GSEA was run on pre-ranked genes using the fgsea package (Korotkevich et al., 2019).For
each enrichment graph we report p, padj (FDR q) and NES (enrichment score normalized to mean enrichment of random samples of
the same size) values in the figure.
Generation of clonal trees and expression of monoclonal antibodies
Five clonal families were randomly selected among the hyperexpanded, as defined by scRepertoire. Clonal trees were reconstructed
using the Alakazam package of Immcantation (Gupta et al., 2015). In brief, clones were made with the function makeChangeOclones
and lineages were reconstructed using the dnapars function of the Phylip package via buildPhylipLineage function. Clonal trees were
visualized via the iGraph package in R. Random representative clones were selected from each family. Both heavy and light chain
sequences were synthesized (Twist Bioscience) and subsequently cloned into a mouse IgG1 expression vector (from the Yewdell
laboratory). To confirm the cloning, developed vectors were sequenced by Eurofins Genomics. To express recombinant antibodies
plasmids encoding corresponding heavy and light chains weremixed in equal ratio. Transfection of Expi293F cells was carried out by
ExpiFectamine 293 Transfection Kit (Thermo Fisher) according to the manufacturer instruction. After four days supernatants were
collected and filtered. Purification of the immunoglobulins was carried out by Akta Start System (GE Healthcare) using protein G
column. Elution of bound antibodies was done by 0.1 M glycine buffer, pH 2.7. To neutralize the solution coming from the column
collecting tubes contained 1 M Tris buffer, pH 9.0. Antibody-containing eluates were concentrated by centrifugation through
VivaSpin columns with a 30 kDa cut-off. Estimation of antibody concentration was done by NanoDrop (Thermo Fisher) equipment.
Binding of all mAbs was confirmed using ELISA. Briefly, plates were coated overnight with 20HAU of PR8 virus in PBS. Plates were
blocked with PBS 2%milk for 1h at RT. mAbs were serially diluted and incubated for 1hr at RT. After washing, plates were incubated
with HRP Horse Anti-Mouse IgG Antibody (Vector laboratorie, cat. no: PI-2000-1, dilution 1:1000) 1hr at RT. Plates were developed
with 1-step Ultra TMB-ELISA (Thermo Fisher), reaction stopped by 2MH2SO4 and read at 450nm in an Tecanmicroplate reader using
Magellen software.
Bio-layer interferometry
Biolayer Interferometry (BLI)-based assay was set up to measure the affinity of murine mAbs to Pro-haemeagglutinin (HA). In
principle, BLI is an optical analytical, label-free, technique and is used to analyze biological interactions using the difference in
interference pattern of white light reflected from two surfaces: a layer of immobilized ligand on the biosensor tip and an internal refer-
ence (blank) layer.
Kinetic assays were optimized for buffer, pH, temperature conditions, orbital shake speed for affinity analysis. Briefly, 0.5 mg of
AviTagged HA diluted in acetate 4.5 (pall fortebio, Sartorius group, Amsterdam, Netherlands) was immobilized onto SAX biosensors
(high precision streptavidin sensors: Pall fortebio, Sartorius group, Amsterdam, Netherlands). mAbs were diluted 1:1000 ratio in
kinetic buffer (1%BSA in PBS, pH 7.5) Pall fortebio, Sartorius group, Amsterdam, Netherlands). H26A1, H28-E23 and H17-40
mAbs were used as positive controls.
Kinetics of binding interactions of mAbs to the immobilized HA were determined using octet data acquisition software
(version 10.0.087) blank experiment with the following experimental steps: wash (PBS buffer, pH 7.5; 60 s), immobilization
(HA, 0.5 mg; 300 s), association (analytes, 1:1000 of mAbs; 420 s), dissociation (PBS buffer, pH 7.5; 600 s), regeneration
(10mM glycine, Sigma-Aldrich, Stockholm, Sweden; 60 s), wash (PBS buffer, pH 7.5; 60 s). Experiments were carried out at
plate shake speed of 1000 rpm and plate temperature of 25 o C. Reference sensor was immobilized with PBS (pH 7.5, GIBCO,
Sweden) and samples were run with same experimental conditions as ligand immobilized sensor. Data was processed using
octet data analysis software (version 10.0) and on-rate (ka), off-rate (kd) and affinity (KD) were calculated upon reference
subtraction.
Infected cells binding assay
The assay was carried out as previously described (Angeletti et al., 2019). MDCK cells were infected using PR8-mCherry expressing
virus, at MOI = 5 for 5h at 37C. After incubation, cells were transferred into tubes and stained with mAbs or control mAbs (H26A1 as
positive control and unrelated PE-specific mAb as negative) at 25ug/ml for 60 min at 37C. After washes with PBS/0.1% BSA cellsCell Reports 35, 109286, June 22, 2021 e6
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OPEN ACCESS Articlewere stained with BV421 anti-mouse Ig light chain (clone 187.1)(BD, cat 562888) for 30 min at 4C before acquisition via flow
cytometry.
QUANTIFICATION AND STATISTICAL ANALYSIS
For single cells analyses, statistics are described in Method details section. GraphPad Prism was used for data analysis except for
single cell analyses. For multiple comparison between groups, One-way ANOVA or two-way ANOVA with Tukey’s multiple
comparisons were used. Violin plots and boxplots are represented as median and interquartile range while other statistical data
are presented as mean ± SEM p < 0.05 was considered statistically significant. The number of animals used in the experiment
are indicated either in the figure legend or figure.e7 Cell Reports 35, 109286, June 22, 2021