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Abstract and Figures

B cells emerge from the bone marrow as transitional (TS) B cells that differentiate through T1, T2 and T3 stages to become naïve B cells. We have identified a bifurcation of human B cell maturation from the T1 stage forming IgM hi and IgM lo developmental trajectories. IgM hi T2 cells have higher expression of 4 7 integrin and lower expression of IL4 receptor (IL4R) compared to the IgM lo branch and are selectively recruited into gut-associated lymphoid tissue. IgM hi T2 cells also share transcriptomic features with marginal zone B cells (MZB). Lineage progression from T1 cells to MZB via an IgM hi trajectory is identified by pseudotime analysis of scRNA-sequencing data. Reduced frequency of IgM hi gut homing T2 cells is observed in severe SLE and is associated with reduction of MZB and their putative IgM hi precursors. The collapse of the gut-associated MZB maturational axis in severe SLE affirms its existence and importance for maintaining health.
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Human marginal zone B cell development from early T2 progenitors
Thomas J.Tull1, Michael J. Pitcher1, William Guesdon1, Jacqueline H. Siu2, Cristina Lebrero-
Fernández3, Yuan Zhao1, Nedyalko Petrov4, Susanne Heck4, Richard Ellis4, Pawan Dhami4,
Ulrich D. Kadolsky4, Michelle Kleeman4, Yogesh Kamra4, David J. Fear1, Susan John1, Wayel
Jassem5, Richard W. Groves6, Jeremy D. Sanderson7, Michael D. Robson1, David D’Cruz1,
Mats Bemark3,8^, Jo Spencer1^,*
1School of Immunology and Microbial Sciences, King’s College London, Guy’s Campus,
London, SE1 9RT, UK.
2Department of Surgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2
0QQ, UK.
3Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska
Academy, University of Gothenburg, SE 405 30 Gothenburg, Sweden.
4Biomedical Research Centre, Guy’s and St. Thomas’ NHS Trust, London, SE1 9RT, UK.
5Liver Transplant Unit, Institute of Liver Studies, King's College Hospital, Denmark Hill,
London, SE5 9RS, UK.
6St John’s Institute of Dermatology, King’s College London, Guy’s Campus, London, SE1 9RT,
UK.
7Department of Gastroenterology, Guy’s and St Thomas’ NHS Trust, Guy’s Hospital, London,
SE1 9RT, UK.
8 Department of Clinical Immunology and Transfusion Medicine, Region Västra Götaland,
Sahlgrenska University Hospital, Gothenburg, Sweden.
^= joint senior authorship
*Correspondence for this submission
Jo Spencer
Department of Immunobiology
Guy’s Hospital, Borough Wing
St Thomas’ St
London SE1 9RT, UK
Email: jo.spencer@kcl.ac.uk
Tel: +44 207 848 9609
Keywords
Human, B cells, marginal zone, transitional B cells, regulatory B cells, gut, gut-associated
lymphoid tissue, cell migration, systemic lupus erythematosus, lupus nephritis.
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
Abstract
B cells emerge from the bone marrow as transitional (TS) B cells that differentiate through
T1, T2 and T3 stages to become naïve B cells. We have identified a bifurcation of human B
cell maturation from the T1 stage forming IgMhi and IgMlo developmental trajectories. IgMhi
T2 cells have higher expression of 47 integrin and lower expression of IL4 receptor (IL4R)
compared to the IgMlo branch and are selectively recruited into gut-associated lymphoid
tissue. IgMhi T2 cells also share transcriptomic features with marginal zone B cells (MZB).
Lineage progression from T1 cells to MZB via an IgMhi trajectory is identified by pseudotime
analysis of scRNA-sequencing data. Reduced frequency of IgMhi gut homing T2 cells is
observed in severe SLE and is associated with reduction of MZB and their putative IgMhi
precursors. The collapse of the gut-associated MZB maturational axis in severe SLE affirms
its existence and importance for maintaining health.
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
Introduction
Transitional (TS) B cells are the immature B cells in human blood from which all mature B
cells develop. Following emigration from bone marrow, TS B cells mature through T1, T2
and T3 phases, when autoreactive cells are depleted (Palanichamy et al., 2009; Suryani et
al., 2010; Yurasov et al., 2005).
In mice a B cell lineage split that is dependent on B cell receptor (BCR) engagement and the
serine/threonine kinase Taok3 is initiated at the T1 phase (Hammad et al., 2017). This
directs B cells towards marginal zone B (MZB) cell fate, requiring subsequent Notch2
cleavage by a disintegrin and metalloproteinase-containing protein-10 (ADAM10).
MZB lineage progression in humans is not clearly understood, or indeed, universally
accepted. A MZB precursor (MZP) population has been proposed that undergoes terminal
differentiation to MZB following NOTCH2 ligation and can be discriminated from naïve B
cells by expression of high levels of IgM (IgMhi), CD24 and the glycosylation-dependent
epitope CD45RBMEM55 (referred to here as CD45RB). An additional CD45RBhi IgMhi population
that lacks the ABCB1 cotransporter has previously been referred to as T, although the
relationship between this subset, MZB and MZP is unclear (Bemark et al., 2013; Descatoire
et al., 2014; Koethe et al., 2011; Zhao et al., 2018).
In humans, MZB develop over the first 2 years of life and are important for immunity against
encapsulated bacteria (Weller et al., 2004). They undergo a phase of clonal expansion and
receptor diversification in the germinal centres (GC) of gut-associated lymphoid tissue
(GALT) (Zhao et al., 2018) (Weill and Reynaud, 2019). The shared expression of MAdCAM1
between the splenic marginal zone reticular cells and GALT high endothelial venules (HEV)
creates the potential to recruit B cells to both sites mediated by 47 integrin binding (Kraal
et al., 1995; Vossenkamper et al., 2013). We have described the expression of 7 integrin
(used here and previously as a surrogate for 47) by T2 B cells in humans and observed
their selective recruitment into GALT where they become activated (Vossenkamper et al.,
2013). Therefore, exposure to the GALT microenvironment could be associated with
multiple stages of MZB cell development from as early as the T2 stage.
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The systemic autoimmune disease systemic lupus erythematosus (SLE), in particular the
severe variant lupus nephritis (LN), has markedly distorted profiles of B cell subsets in blood.
T TS       B     CD  ID   
  DN B  (Landolt-Marticorena et al., 2011; Wei et al., 2007).
Disproportionate expansion of a population of DN cells lacking expression of CD21 and
CXCR5 and with upregulated CD11c (DN2 cells) is a particular feature of LN (Jenks et al.,
2018). DN2 cells may be derived from activated naïve B cells (aNAV), driven by TLR7
engagement, resulting in the generation of self-reactive antibody producing plasma cells
(Jenks et al., 2018; Tipton et al., 2015). Interestingly, a recent study of a cohort of newly
diagnosed patients with SLE demonstrated that MZB may be reduced in frequency (Zhu et
al., 2018). Since we have previously shown that TS B cells in SLE may have significantly
reduced expression of 7 integrin, we were interested to know if this may be associated
with defective MZB development and the increase in aNAV and DN2 cells.
Here, we identify bifurcation in human B cell development from the T2 stage. Cells in one
branch are IgMhi, express 7 integrin and are gut homing. Cells in the alternative IgMlo
branch have high expression of IL4R, lower expression of 7 integrin and do not tend to
enter the gut. Transcriptomically, IgMhi T2 cells share features with MZB. B cell development
progresses from T1 to MZB via an IgMhi trajectory by pseudotime analysis. IgMhi T2 cells are
stably IgMhi in culture and have a greater tendency to make IL10 than IgMlo cells. Markedly
reduced frequency of IgMhi7hi T2 cells was seen in patients with severe SLE and this was
associated with stark reduction in cell populations associated with MZB development. Our
data link reduced access of IgMhi T2 cells to GALT with defects in all stages of MZB
differentiation and enables the assimilation of these elements of human MZB differentiation
into a model of human B cell development.
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Results
Segregation of B cell phenotypes from T2 through naïve B cell subsets
In mice, B cells commit to MZB differentiation soon after bone marrow emigration at the T1
stage. To seek evidence of this in humans, a deep phenotypic analysis of peripheral blood
mononuclear cells (PBMC) from healthy control donors (HCD) was undertaken by mass
cytometry (Fig. S1 A, B and C). SPADE on viSNE identified B cell subsets including TS B cells
represented by CD27-IgD+CD24+++/++CD38+++/++ nodes that included CD10+ T1 and T2 cells as
well as CD10- T3 cells (Fig. 1 A)(Qiu et al., 2011; Zhao et al., 2018). T3 cells can only be
definitively distinguished from naïve cells by their failure to extrude dyes such as rhodamine
123 (R123) due to lack of the ABCB1 cotransporter (Wirths and Lanzavecchia, 2005) (Fig. S1
D). Since mass cytometry cannot be used to detect dye extrusion, the boundary between T3
and naïve B cells was estimated to generate the TS SPADE bubble. As previously reported,
CD27+ B cells included CD27hi and CD27lo cells (Fig. 1 A) (Grimsholm et al., 2020).
To perform a deep phenotypic analysis of TS B cells, events within the TS bubble identified
in Fig. 1 A were exported and re-clustered by SPADE on viSNE using all expressed panel
markers and then grouped according to gradients of loss of CD10, CD38 and CD24 and gain
of CD21 corresponding to T1, T2 and T3 stages of differentiation (Fig. 1 B)(Bemark, 2015).
The SPADE trees branched, forming 2 chains of nodes that each extended through the T2
and T3 SPADE bubbles with no lateral connections between the branches. Branches differed
most notably in their expression of IgM (Fig. 1 C). IgMhi T2 B cells also had lower expression
of CCR7 but higher expression of 7 integrin than IgMlo T2 cells by mass cytometry. This was
validated by qPCR (Fig. 1 D and E). In addition, IgMhi T3 cells had higher median expression
of CD24 and CD45RB than IgMlo T3 cells (Fig. 1 B and F).
In the SPADE analysis of all CD19+ B cells, nodes representing IgMhi TS B cells were
continuous with IgMhi naïve B cells and IgMlo TS B cells were continuous with IgMlo naïve
cells (Fig. 1 A and G). Principle component analysis (PCA) using all markers expressed by cells
in nodes identified in Fig. 1 G grouped IgMhi TS B cells closest to IgMhi naïve B cells and most
distant to IgMlo TS B cells (Fig. 1 H). The major contributors to PCA1 and PCA2 in addition to
IgM and IgD were mediators of cell traffic (Fig. 1 I).
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Human B cells therefore segregate phenotypically as T1 cells enter into the T2 stage,
forming two branches that differ in their expression of IgM and in markers of migratory
potential. IgMhi T2 cells resemble IgMhi naïve cells more closely than they resemble IgMlo T2
cells with which they share markers of differentiation.
Gut-associated lymphoid tissue is enriched in IgMhi T2 cells
Human TS B cells can home to GALT where they become activated (Vossenkamper et al.,
2013). To determine whether the high expression of 7 integrin on IgMhi TS B cells is
associated with selective recruitment into GALT, mass cytometry was used to compare B
cells isolated from paired blood and gut biopsies from individuals undergoing surveillance
colonoscopies (n=7) (Fig. S1 E, F and G). TS B cells are a small subset and due to low
mononuclear cell yields from GALT biopsies, data from individual samples were
concatenated. SPADE on viSNE was then used to identify CD10+ T1 and T2 B cells within the
total CD19+ population (Fig. 2 A and S1 H). The undirected clustering algorithm FlowSOM
(Van Gassen et al., 2015) was then used to group T1 and T2 B cells. This identified six
metaclusters and the identity of each cluster was deduced from the relative expression of
CD21, CD24, CD38 and IgM (Fig. 2 B and C). This demonstrated that IgMhi T2 cells are
enriched in GALT, whereas both T1 and IgMlo T2 cells are depleted compared to PBMC (Fig.
2 D and E). Within GALT IgMhi T2 cells had higher expression of the activation markers CD69
and CD80 than PBMC (Fig. 2 E).
Having observed that IgMhi T2 cells are enriched in GALT we sought confirmation of
selective recruitment by asking whether this population is depleted from blood draining the
gut via the hepatic portal vein that we isolated from liver perfusion samples. Flow cytometry
demonstrated that liver perfusate samples were enriched in T1 cells as reported previously
(Vossenkamper et al., 2013)(Fig. 2 F), and that CD24hiIgMhi T2 and IgMhi naïve cells were
depleted compared to PBMC from HCD (Fig. 2 G, H and I), consistent with their selective
recruitment from blood into GALT.
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Transcriptomic analysis of IgMhi and IgMlo TS B cells demonstrates different upstream
regulators of phenotype
Having demonstrated contrasting surface phenotypes and migratory capacity of IgMhi and
IgMlo TS B cells, we next sought to identify transcriptomic features differing between them
and to gain insight into inducers and regulators of these subsets by single cell RNA
sequencing. IgMhi and IgMlo TS B cells from 5 HCD were sorted by FACS, pooled and gene
expression libraries were prepared using a 10x Genomics s    
workflow (Fig. S2 A, B, C and D). In total, 14499 genes were identified in 4268 cells after
quality filtering. The non-linear dimension reduction algorithm UMAP (Becht et al., 2018)
was run on differentially expressed genes and demonstrated discreet clustering of IgMhi and
IgMlo TS B cells (Fig. 3 A). Selected genes from the top 60 differentially expressed genes are
illustrated in Fig. 3 B and 3 C. Transcripts encoding CD1C and MZB1 that are expressed by
MZB cells, were amongst the most abundantly expressed genes in IgMhi TS B cells. The lupus
risk allele and regulator of toll like receptor 9 (TLR9) responses PLD4 was the most highly
differentially expressed gene in IgMhi TS B cells (Gavin et al., 2018). Undirected clustering of
pooled IgMhi or IgMlo TS B cells generated clusters that contained predominantly IgMhi or
IgMlo TS B cells (Fig. 3 D and E) that shared enrichment of the genes expressed by these cell
subsets (Fig. 3 B and F). High expression of IL4R by IgMlo TS and IgMlo naïve B cells was
confirmed by flow cytometry (Fig. S2 E and F). Importantly, KLF2, which drives murine
follicular B cell development, was upregulated in IgMlo TS B cells (Hart et al., 2011). CCR7
was upregulated in IgMlo TS B cells supporting the higher surface expression that was
evident in the mass cytometry analysis. Higher abundance of transcripts encoding L-selectin
by IgMlo TS B cells was also confirmed by qPCR using sorted populations (Fig. S2 G and H).
Ingenuity pathway analysis demonstrated enrichment of retinoic acid receptor and
lipopolysaccharide induced genes in IgMhi TS B cells (Fig. S2 I and J). IgMlo TS B cells were
enriched in genes induced by interferon- (IFN-), interleukin-1 and interleukin-2 (Fig. S2 K).
IgMhi TS B cells used less VH1 and more VH3 than IgMlo TS B cells consistent with published
profiles of MZB repertoire (Bagnara et al., 2015)(Fig. S2 L).
IgMhi and IgMlo TS B cells therefore have distinct transcriptomes. IgMlo cells are selectively
enriched in genes encoding peripheral circulation and inhibition of marginal zone B cell fate
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whereas IgMhi cells have gene expression signatures and IGHV gene family usage linking
them to MZB cells.
The abundance of CD1C transcripts in IgMhi TS B cells was of particular interest because
CD1c is characteristically highly expressed by human MZB (Weller et al., 2004). Consistent
with the transcriptomic profile, CD1c surface expression was higher on IgMhi than IgMlo TS B
cells (Fig. 3 G and H). As previously reported CD1c expression was high on MZB (Fig. 3 I) as
well as on MZP and CD45RBhi T3 cells     T that have been linked to
MZB development (Bemark et al., 2013; Descatoire et al., 2014; Koethe et al., 2011; Zhao et
al., 2018). MZP and CD45RBhi T3 cells were defined by the phenotype CD27-IgD-CD10-
CD45RBhi with expression of the ABCB1 cotransporter or not, respectively (Fig. 3 J, K and L).
Cells that express the ABCB1 cotransporter extrude rhodamine 123 (R123) and are therefore
identified as R123lo cells in this analysis. Both subsets share high expression of IgM and CD24
(Fig. S2 M and N)
Lineage progression from IgMhi TS B cells through to MZB
The shared surface properties of IgMhi TS with IgMhi naïve B cells (Fig. 1 H), the enrichment
of transcripts considered characteristic of MZB in IgMhi TS (Fig. 3 B and F), and shared high
expression of CD1c by IgMhi TS with MZB and other B cell subsets associated with MZB
development (Fig. 3 G-L) all support the existence of an IgMhi MZB differentiation pathway
that begins during TS B cell development. We investigated this further by performing
pseudotime trajectory analysis of single cell RNA sequencing data from HCD B cells from
blood.
CD19+ B cells were sorted from PBMC of 3 HCD (Fig. S3 A) and surface labelled with Total-
Seq-C antibodies prior to capture on the 10x chromium controller (Fig. S3 B). Gene
expression, and antibody detection tag (ADT) libraries were then prepared according to the
manufacturers instructions and sequenced on an Illumina HiSeq High Output platform (Fig.
S3 C).
Data from single HCD were initially analysed individually. UMAP plots were used to visualize
clusters and identify the B cell subsets they corresponded to by overlaying signal from
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lineage defining transcripts and CITE-Seq antibodies (Fig. S3 and S4). TS B cells were
identified as CD27-IgD+ clusters with high surface expression of CD38. Of the remaining
CD27-IgD+ clusters that represented naïve cells, those with the top 30% of median IgM-ADT
signal were designated IgMhi (Fig. S3 and S4). Note that because identification of MZP and
CD45RBhi T3 would require reagents that are incompatible with this method (Fig. 3 J), they
will be included in the IgMhi naïve cell groups in this analysis. CD27+IgD+ clusters that were
enriched in CD1C transcripts were designated as MZB. CD27+IgD-IgM+ clusters were
 IM-   CD+IgD-IgM- clusters enriched in HOPX and COCH
transcripts were designated as class switched memory B cells (CSM) (Descatoire et al.,
2014)(Fig. S3 and S4).
Three dimensional UMAP plots were then used to better visualise the spatial relationship
between these B cell subsets (Fig. 4 A, B, C and D and S4 C and F). This demonstrated clear
separation of CD27+ and CD27- islands of cells (Fig. 4 A, B and S4 and Supplemental videos
1, 2 and 3). In all three HCD two    ed the CD27- and CD27+
islands in the plot (Fig. 4 A, B and S4 C and F and Supplemental videos 1, 2 and 3). In each
HCD, an IgMhi bridge that was enriched in cells with CD1C transcripts linked the CD27- island
to MZB. (Fig. 4 A, B, C and D and S4 B and E). In contrast, IgM-only cells were connected to
the CD27- island by naïve cells with lower expression of IgM.
Having visualized the juxtaposition of IgMhi naïve cells with MZB in UMAP clusters we next
used the Slingshot tool for pseudotime trajectory analysis. Data from the three HCD were
normalized and integrated. UMAP plots were used to identify clusters representing 27-
CD38hiCD24hi TS cell and CD27+IgD+IgM+ MZB subsets by overlay of CD27, IgM, IgD and CD38
ADT signal (Fig. 4E and F). The TS B cell cluster was selected as the starting point for analysis
of pseudotime transitions in Slingshot. Importantly, end points were not specified.
Slingshot identified an IgMhi pseudotime trajectory from TS that passed through the MZB
cluster via IgMhi naïve B cells (Street et al., 2018) (Fig. 4 G and Supplemental video 4).
Amongst the 100 most differentially expressed genes along this trajectory were PLD4, CD1C,
SOX4 and MZB1, that were previously identified as differentially expressed between IgMhi
and IgMlo TS B cells (Fig. 3B and F and Fig. 4 H and I). Analysis of gene expression by cells
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along the trajectory demonstrated progressive downregulation of IGLL5 and VPREB1
markers of B cell immaturity (Fig. 4 H). Upregulated in the terminal stages of the trajectory
were genes encoding proteins implicated in cell adhesion, including ITGB2, PCDH9 and
activation including DAPP1 (Fig. 4 H and I). The final cluster in the pseudotime trajectory was
enriched in interferon regulated genes MX1 and transcription factor ZEB2 (Fig. 4 H and I).
IFN induced genes as well as DAPP1 and FCRL5 are highly expressed by DN2 cells, although
the relationship of this subset with MZB is not known (Jenks et al., 2018). Pseudotime
analysis of HCD PBMC therefore identified an IgMhi developmental trajectory from TS B cells
to MZB.
IgMhi and IgMlo TS B cells differ functionally, and in their potential to differentiate
We next determined if IgMhi and IgMlo TS B cells that have different cell surface and
transcriptomic characteristics maintain their relative levels of IgM expression in vitro
following stimulation and if they differ functionally. Initially, proliferation in response to
anti-IgM in the presence of CD40L was measured. IgMhi TS B cells proliferated more than
IgMlo cells in response to anti-IgM (Fig. 5 A and B). Next we investigated the response of
IgMhi and IgMlo TS B cells to the TLR9 agonist CpG, that has been proposed to drive MZB
differentiation (Guerrier et al., 2012). In culture, CpG increased surface expression of IgM on
both IgMhi and IgMlo B cells. However, IgMhi cells remained IgMhi compared to the IgMlo cells
(Fig. 5 C and D). Furthermore, culture with CpG resulted in greater upregulation of CD45RB
on IgMhi TS and IgMhi naïve cells than IgMlo TS and IgMlo naïve cells, consistent with
adoption of an MZP like phenotype (Fig. 5 E).
A subpopulation of human cells with a TS phenotype are regulatory, and murine IL10
producing B regulatory (Breg) cells are T2 marginal zone progenitor cells and the gut is
important for their induction (Blair et al., 2010; Pillai et al., 2005; Rosser et al., 2014). We
therefore investigated the capacity of IgMhi TS B cells to produce IL10. Following 6 hours
stimulation with PMA and ionomycin, IgMhi TS B cells produced significantly more IL10 than
IgMlo cells (Fig. 5 F), inferring greater regulatory capacity of this subset.
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Marginal zone B cell differentiation is defective in patients with severe SLE
We have previously observed reduced frequencies of circulating TS B cells expressing 7
integrin in a subset of SLE patients, implying reduced potential for TS B cells to access GALT
in these cases. Data presented here implicates GALT as an important site for MZB
differentiation and MZB depletion has been reported in SLE (Rodriguez-Bayona et al., 2010;
Zhu et al., 2018). Hence, we sought to determine whether our proposed MZB differentiation
pathway was defective in SLE.
Flow cytometry was used to quantify B cell subsets in a cohort of 41 SLE patients and
matched HCD (Table S1,2). Reduced MZB frequency was seen in patients with SLE compared
to HCD (Fig. 6 A and B) and this was most marked in patients with lupus nephritis (LN)
compared to patients with other manifestations of SLE (OL) (Fig. 6 C). Reduced MZB
frequency was not a feature of other autoimmune diseases studied (Fig. 6 C, Table S3),
although it has been identified in patients with S disease (Roberts et al., 2014). We
found that a relative reduction of MZB in patients with SLE was associated with a reduction
of MZP (CD27-IgD+CD10-CD45RBhi R123-) (Fig. 6 D and E), T3 CD45RBhi cells (CD27-IgD+CD10-
CD45RBhiR123+)(Fig. 6 D and F). This was again most consistently observed in the LN patient
cohort. The proportion of naïve B cells (CD27-IgD+CD10-R123lo) was also diminished in SLE
(Fig. 6 G) but CD45RBloR123hi cells were more frequent (Fig. 6 H). This population was
further divided into T3 and aNAV by their expression of CD24 and CD38 (Fig. 6 I). Whilst
both subsets were increased in patients with LN, CD45RBloR123hi cells were predominantly
T3 cells (Fig. 6 I). Frequencies of MZB correlated with both MZP and T3 CD45RBhi
frequencies and inversely correlated with T3 CD45RBlo frequencies (Fig. 6 J).
We next asked if reduced MZB frequency in LN patients was intrinsic to the disease or
secondary to patient demographics, immunosuppressants or disease severity. HCD, LN and
OL cohorts were matched for age, gender and ethnicity (Table S1,2) and there was no
difference in MZB frequency between Caucasian and African Caribbean healthy donors and
SLE patients (Fig. 6 K). A subset of Caucasian SLE patients were seen to have higher MZB
than African-Caribbean patients but this subset had mild disease (Fig. 6 L). There was no
difference in immunosupression between SLE patients with high and low MZB frequencies
and MZB depletion was not seen in patients with pemphigus vulgaris (PV) taking
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prednisolone and mycophenolate mofetil (MMF) and did not differ between SLE patients
taking or not taking hydroxychloroquine (HCQ) (Fig. 6 M, N and O). However, MZB, MZP and
CD45RBhi T3 cell frequencies correlated with disease severity (Fig. 6 P). The more marked
reduction of MZB in the LN patients is therefore likely to be due to this patient group
representing a severe disease cohort. As previously reported, CD27-IgD- DN cells were more
abundant in LN (Fig. 6 Q). These were predominantly CD24loCD21lo and therefore DN2
consistent with other studies (Fig. 6 R)(Jenks et al., 2018).
MZB depletion in SLE is therefore associated with reduced frequency of MZP and T3
CD45RBhi cells. This consolidates the concept of these cells as being in a developmental
continuum in health and suggests that aberrant transitional B cell maturation may result in
failure of their genesis in SLE.
IgMhi
7 integrinhi T2 cells are reduced in frequency in lupus nephritis
To identify early stages of aberrant marginal zone lineage development in LN we used mass
cytometry to compare blood B cell subsets from LN patients and HCD in an undirected way
(Fig. S5 A, B and C). The automated clustering algorithm CITRUS identified populations that
differed significantly in abundance between LN and HCD. The three main clusters of nodes
(Fig. 7 A) can be identified by their relative expression of B cell lineage markers (Fig. 7 B and
C). CD27-IgD+CD10- CD45RBhi (MZP) and CD27+IgD+CD10- CD45RBhi (MZB) cells were
significantly depleted in LN patients (Fig. 7 Ci and Cii). TS B cells were more abundant (Fig. 7
Ciii) whilst IgA class switched cells were depleted in patients with LN (Fig. 7 Civ).
TS B cell subpopulations in HCD and LN were quantified by FlowSOM (Fig. 7 D and E). 5
metaclusters were identified representing subdivisions of T1 and T2 populations (Fig. 7 D).
Inspection of minimal spanning trees demonstrated stark depletion of certain TS
subpopulations in LN (Fig. 7 E). Quantification of events within metaclusters from all donors
revealed that T1 cells represented by metaclusters 1,2 and 3 were more abundant in LN
whilst IgMhi T2 cells with high expression of 7 integrin represented by metacluster 4 were
markedly less abundant in LN patients (Fig. 7 F).
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Depletion of IgMhi T2 cells with high expression of 7 integrin is therefore associated with
defective MZB maturation in LN patients. This affirms the association between MZB and
IgMhi T2 cells in health and implicates reduced access of these cells to GALT in the
breakdown of this developmental axis in patients with severe lupus (Fig. S5 D).
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Discussion
We have identified branches of human B cell lineage maturation that are evident from the
T2 stage. An IgMhi branch, that expresses higher levels of 7 integrin and lower levels of
IL4R compared to the IgMlo branch, is gut homing. Confirmation of differentiation through
IgMhi stages of differentiation from IgMhi T2, including IgMhiCD45RBhi T3 and naïve B cell
variants to MZB is gained from pseudotime analysis coupled with the observed concerted
reduction of the stages in this sequence in patients with severe SLE (Fig. S5 D). The reduced
frequencies of gut homing IgMhi T2 cells in severe SLE further consolidates the role of GALT
in early B cell fate decisions and supporting MZB development. The collapse of this gut
homing MZB maturational axis in severe SLE therefore affirms its existence in health as well
as having important disease relevant implications.
We have previously observed that human T2 cells are recruited into GALT where they are
activated by intestinal microbes (Vossenkamper et al., 2013). Here we demonstrate that
specifically the IgMhi T2 subset of TS B cells is recruited into GALT, where they have a
phenotype of activated cells including expression of CD69 and CD80. The IgMhi T2 subset is
also enriched in ROR and LPS inducible genes, consistent with exposure to the microbiota.
We show that the TLR9 agonist CpG that upregulates IgM and NOTCH2 in human TS B cells
(Capolunghi et al., 2008; Guerrier et al., 2012) also upregulates CD45RB on IgMhi TS and
IgMhi naïve cells. PDL4, that is a lupus risk allele and the most highly upregulated gene in
IgMhi compared to IgMlo TS B cells, is upregulated along the developmental pathway to MZB
and limits responses to CpG (Gavin et al., 2018). This suggests that PDL4 defects could
contribute to SLE pathogenesis by impacting an aspect of the development or function of
IgMhi TS B cells involving TLR9. Interestingly, PLD4 is also expressed in the splenic marginal
zone in mice (Yoshikawa et al., 2010) and PLD4 knockout mice develop autoantibodies and
immune complex mediated renal damage similar to SLE with LN (Gavin et al., 2018). IgMhi
TS B cells also show a transcriptomic signature indicative of retinoic acid regulation that is a
feature of GALT microenvironment. Together, these data suggest that innate signals and the
gut environment impact the origin, fate and function of IgMhi TS B cells.
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Consistent with proposed developmental continuum from the IgMhi T2 stage through to
MZB, GALT is involved in MZB development, including a stage of receptor diversification in
GALT GC. However, supporting a relatively short-term transit coupled to differentiation, the
frequencies of somatic mutations in MZB are lower than those of memory B cells or plasma
cells in the gut (Zhao et al., 2018). Together these data suggest that GALT transit and GC
occupancy are important but transient phases in IgMhi T2 to MZB lineage progression.
Pseudotime analysis also identified a population of B cells that appear to develop from MZB
and that are activated and more mature. It is possible that activation of MZB might generate
a novel population of effector or memory cells.
MZB differentiation is associated with a distinctive gene expression changes and acquisition
of the transcription factor ZEB2 (SIP1). ZEB2 has previously been identified as a component
of a network including miR200 and TGF-1 that can regulate cell fate decisions (Gregory et
al., 2008; Guan et al., 2018). Activated TGF-1 is produced abundantly in the gut. It is
possible that in addition to playing important roles in regulation of intestinal immunity as a
switch factor for IgA and induction of regulatory T cells, it could also be involved in gut-
associated MZB development by interactions with ZEB2 (Borsutzky et al., 2004; Chen et al.,
2003).
Collapse of the MZB developmental pathway in severe SLE was accompanied by expanded
T3, aNAV and DN2 cell populations. Expansion of aNAV and DN2 populations is a product of
excessive TLR7 and IFN- signalling. We were therefore interested in enrichment of IFN-
induced genes in IgMlo TS B cells. Interestingly, the IFN- regulated transcription factor KLF2
was transcriptionally upregulated in IgMlo TS B cells. KLF2 drives follicular B cell maturation
in mice and its deletion results in an expansion of MZB cells. The role of KLF2 in human B cell
development is not known, however loss-of-function KLF2 mutations along with NOTCH2
mutations that increase the stability of the notch intracellular domain (NICD) are the most
commonly encountered mutations in human MZB cell lymphoma (Campos-Martin et al.,
2017). This implicates KLF2 in human B cell fate decisions and eludes to a role for IFN- in B
cell development and supports its proposed involvement of the imbalance of B cell subsets
in LN. The role of IFN- in defective MZB maturation is also supported by depletion of this
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subset in patients with severe COVID19, which is associated with elevated serum IFN-
levels and extrafollicular B cell repsonses (Laing et al., 2020; Woodruff et al., 2020).
LN represents a severe lupus subtype associated with the worst clinical outcomes (Yap et
al., 2012). B regulatory (Breg) IL10 responses associated with expression of CD80 and CD86
are defective in SLE (Blair et al., 2010), permitting aberrant T effector functions (Oleinika et
al., 2019). In mice Breg cells are IgMhiCD21hiCD23hi T2 MZP cells and interaction with the gut
microbiome is essential for their induction (Evans et al., 2007; Rosser et al., 2014). We have
identified that IgMhi TS B cells express CD80 in GALT and represent the predominant IL10
producing TS B cell subset. Their depletion in LN may be synonymous with the loss of Breg
IL10 responses and associated with the lack of T cell regulation in SLE. MZB confer immunity
to encapsulated bacteria such as pneumococcus, thus their depletion in LN may confer
increased risk of such infections SLE (Danza and Ruiz-Irastorza, 2013). This also reinforces
the importance of pneumococcal vaccination in this patient cohort.
In summary, we identify an MZB maturation pathway that becomes evident at the T2 stage
of B cell development and that is depleted in severe SLE. Traffic through GALT is a
component of this pathway that is potentially linked to the induction of human IL10
producing Breg cells (Rosser et al., 2014). Together, this affirms the importance of tissue
microenvironments in shaping the B cell functional repertoire and maintaining health.
Understanding the regulators of early B cell fate will be a key to resolving the disturbances
in B cell function in severe SLE.
Acknowledgements
We thank sample donors clinical research support staff L.Nel and N.Morton. This work was
funded by the Medical Research Council of Great Britain (MR/R000964/1, MR/L009382/1,
MR/P021964/1 and MR/R000964/1) and T S T L Trust. We acknowledge
financial support from the Department of Health via the National Institute for Health
Research comprehensive Biomedical Research Centre award to Guys & St Thomas NHS
Foundation Trust and Kings College London for the flow and mass cytometry at the Flow
Cytometry Research Platform, and library preparation and sequencing at the Genomics
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Research Platform. MB is supported by research funds from The Swedish Research Council
and the County Council of Västra Götaland.
Author contributions
Conceptualization and design of study TJT YZ DDC M.B., J.S.; Sample identification
and collection: T.J.T, W.G., Y.Z., W.J., M.D.R, RWG JDS DDC.; Data acquisition and
methodology; T.J.T., C.L-F., W.G., Y.Z., U.D.K., P.D., S.J., R.E., S.H., M.B., J.S.; Data analysis:
T.J.T, M.J.P, W.G., J.H.S, N.P., S.H, R.E, M.B.; Supervision and funding: J.S., MB DDC
Declaration of Interests
The authors declare no competing interests.
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Figure legends
Figure 1. Segregation of B cell phenotypes from T2 through naïve B cell subsets.
A) SPADE on viSNE plots generated using the following markers; CD10, CD24, CD38, CD27,
CD45RB IgD, IgM, IgA and IgG. The plots are from a representative female HCD and
depict expression of lineage markers used to identify B cell subsets in HCD PBMC.
B) SPADE on viSNE plots of TS B cells exported from the TS SPADE bubble in Figure part A
and generated by re-running viSNE using all expressed B cell markers. The SPADE plots
depicted are from a representative female HCD. TS B cell populations were defined as T1
(CD10+CD24+++CD38+++CD21lo), T2 (CD10+CD24++CD38++CD21hi) and T3 (CD10-
CD24+CD38+CD21hi).
C) SPADE trees demonstrating that T2 and T3 TS B cells have prominent IgMhi and IgMlo
subpopulations.
D) SPADE trees demonstrating that IgMhi T2 B cells have higher expression of 7 integrin
and lower expression of CCR7 than IgMlo T2 B cells.
E) qPCR validation of CCR7 and ITGB7 (7 integrin) from sorted subsets expressed as 𝚫CT
values relative to 18S endogenous control (paired t test).
F) SPADE aligns IgMhi T2 cells with IgMhi T3 cells with relatively high expression of CD45RB.
G) A SPADE on viSNE plot from Figure part A demonstrating the identification of IgMhi TS
and naïve B cell populations.
H) A principle component analysis plot generated using all expressed markers on IgMhi and
IgMlo subsets identified in Figure part G. Data points represent individual donors and are
surrounded by 95% confidence ellipses with a larger central mean data marker.
I) Variable contribution bar graphs demonstrate that homing receptors are major
contributors to PCA1 and PCA2 in Figure part G. The dashed red reference line
represents the value were the contribution uniform.
Figure 2. Gut-associated lymphoid tissue is enriched in IgMhi T2 cells.
A) SPADE on viSNE plots depicting the expression of B cell lineage markers used to identify
T1 and T2 cells as CD27-IgD+CD10+ in a concatenated GALT sample, M only = IgM only
memory (CD27+IgD-IgM+), GC = germinal centre (IgD-CD10+), PB = plasmablast (IgD-
CD38hi) (see also Fig. S1 H).
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B) A minimal spanning tree generated by FlowSOM run on exported events (n= 4520) from
the TS bubble in Figure part A using CD10, CD24, CD38 and IgM as clustering
parameters. Automatic metaclustering of the flowSOM nodes identified 6 metaclusters,
the identity of each can be inferred by the relative expression of CD21, CD24, CD38 and
IgM (see also Figure part C).
C) Minimal spanning trees showing expression of CD21, CD24, CD38 and IgM on a
concatenated PBMC sample.
D) Pie charts demonstrating the proportion of TS cell subsets inferred from metaclusters in
figure part B confirm that GALT is enriched in IgMhi T2 cells.
E) Minimal spanning trees demonstrating higher expression of CD69 and CD80 on GALT TS
B cells.
F) Flow cytometry contour plots of concatenated (n=3) liver perfusate samples and
concatenated HCD PBMC (n=3) demonstrating reduced proportion of CD24++CD38++ T2
cells in liver perfusates.
G) Flow cytometry plots of concatenated liver perfusate and PBMC samples demonstrating
reduced frequency of IgMhiCD24hi T2 and naïve (CD27-IgD+CD10-) cells in liver perfusate
samples compared to HCD PBMC.
H) Flow cytometry dot plots with IgM MFI overlay of concatenated PBMC and liver
perfusate samples demonstrating reduced frequency of IgMhiCD24hi TS and naïve cells in
liver perfusate samples.
I) Scatter plots of flow cytometry data from individual samples gated as in Figure part G
demonstrating reduced frequency of IgMhiCD24hi TS and naïve cells (CD27-IgD+CD10-) in
liver perfusate samples compared to PBMC (median values).
Figure 3. Transcriptomic analysis of IgMhi and IgMlo TS B cells demonstrates different
upstream regulators of phenotype.
A) UMAP plot of IgMhi and IgMlo TS B cells clustered according to differentially expressed
genes identified using the Seurat SCTransform workflow (Hafmeister, 2020) in the two
subsets.
B) Heatmap of selected genes from the top 60 differentially expressed genes in IgMhi and
IgMlo TS B cells.
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C) Scatter plot demonstrating genes differentially expressed in IgMhi and IgMlo TS B cells.
D) A PCA based approach based on differentially expressed genes identified 6 clusters
among the IgMhi and IgMlo TS B cells that were demonstrated by UMAP.
E) Quantification of the frequency of IgMhi or IgMlo TS B cells within the clusters
demonstrated by UMAP in Figure part D reveals that IgMhi TS B cells dominate in clusters
0 and 2 and IgMlo TS B cells in clusters 1 and 3.
F) Dot plot demonstrating expression of selected genes within clusters 0-4.
G) Flow cytometry dot plots demonstrating the gating strategy to identify IgMhi and IgMlo
T2 (CD27-IgD+CD10+CD21hi) B cells.
H) Scatter plots demonstrating CD1c MFI in T1, IgMhi and IgMlo T2 B cells gated in Figure
part G (mean +/- SD, paired t test).
I) Histograms demonstrating CD1c MFI in B cell subsets gated in Figure part G.
J) Dot plot of flow cytometry data demonstrating the gating strategy used to identify
CD45RBhi T3 (R123+) and MZP (R123-) cells.
K) A histogram showing CD1c MFI on subsets as gated in Figure part J.
L) Dot plots demonstrating CD1c MFI on subsets gated in Figure part J (mean +/- SD, paired
t test).
Figure 4. Lineage progression from IgMhi TS B cells through to MZB.
A. A 3D UMAP plot of CD19+ cells from a HCD (10x HCD1) generated from a PCA run on
2000 differentially expressed genes. Clusters were merged and pseudocoloured
according to the B cell subsets they represent as described in Fig. S3 D, E, F and G.
B. CD27 and IgM ADT and CD1C gene signal overlay on the 3D UMAP plot displayed in
Figure part A.
C. The 3D UMAP plot depicted in Figure part A viewed using different UMAP axis
coordinates to Figure part A.
D. CD27 and IgM ADT and CD1C gene signal overlay on the 3D UMAP projection in Figure
part C.
E. A UMAP plot generated by integrating 3 HCD 10x datasets and generated from a PCA
run on 2000 variably expressed genes.
F. Feature plots demonstrating overlay of ADT signal on the UMAP plot from Figure part E
enables identification of clusters representing TS and MZB.
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G. A Slingshot developmental trajectory overlaid onto the UMAP plot from Figure part E
demonstrating developmental progression from clusters representing TS B cells to MZB
via IgMhi naïve cells.
H. A heatmap of selected genes from the top 100 most differentially expressed genes along
the Slingshot trajectory demonstrated in Figure part G.
I. Feature plots demonstrating overlay of gene signal from the differentially expressed
genes along the Slingshot trajectory identified in Figure part G.
Figure 5. IgMhi and IgMlo TS B cells differ functionally and in their potential to
differentiate.
A) Histograms of flow cytometry data demonstrating cell trace violet (CTV) dilution
following 5 days of culture with CD40L+anti-IgM in IgMhi and IgMlo TS B cells.
B) Scatter plots demonstrating increased proliferation of IgMhi TS compared to IgMlo TS B
cells when stimulated with CD40L+anti-IgM for 5 days (medians, Wilcoxon test).
C) Flow cytometry contour plots demonstrating that an IgMhi phenotype is maintained on
IgMhi TS B cells after CpG stimulation for 5 days.
D) Scatter plots demonstrating that an IgMhi phenotype is maintained on IgMhi TS B cells
after CpG stimulation for 5 days (mean +/- SD, paired t test).
E) Flow cytometry contour plots and scatter plots demonstrating greater upregulation of
CD45RB by IgMhi than IgMlo TS and naïve (CD27-IgD+CD10-) subsets at day 5 culture with
CpG (mean +/- SD, paired t test).
F) Flow cytometry contour plots and scatter plots demonstrating higher frequency of IL10
expressing cells among IgMhi TS B cells following 6 hour stimulation with
PMA/ionomycin (median, Wilcoxon test).
Figure 6. Marginal zone B cell differentiation is defective in patients with severe SLE.
A) Flow cytometry dot plots and scatter plots demonstrating reduced frequency of
CD27+IgD+ (MZB) cells in a patient with SLE compared to a HCD.
B) Scatter plots of flow cytometry data demonstrating reduced frequency of MZB in
patients with SLE compared to HCD (medians, Mann-Whitney test).
C) Scatter plots of flow cytometry data demonstrating reduced MZB frequency in lupus
nephritis (LN), other lupus subtypes (OL) but not in ulcerative colitis (UC),
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granulomatosis with polyangiitis (GPA) and pemphigus vulgaris (PV) (medians, Mann-
Whitney test).
D) Flow cytometry dot plots of a HCD and an SLE patient demonstrating identification of T3
(R123hi) and naïve (R123lo) subsets with high and low expression of CD45RB. Stark
reduced frequency of CD45RBhi T3 and naïve (MZP) populations is evident in SLE.
E) Scatter plot of flow cytometry data demonstrating reduced frequency of MZP
(CD45RBhiR123lo) cells in LN patients (medians, Mann-Whitney test).
F) Scatter plot of flow cytometry data demonstrating reduced frequency of CD45RBhi T3
(R123hi) cells in LN patients (medians, Mann-Whitney test).
G) Scatter plot of flow cytometry data demonstrating reduced frequency of naïve
(CD45RBloR123lo) cells in LN patients (mean +/- SD, unpaired t test).
H) Scatter plot of flow cytometry data demonstrating enrichment of CD45RBloR123hi cells in
LN patients (mean +/- SD, unpaired t test).
I) Gating of CD24+CD38+ T3 and CD24-CD38- aNAV cells revealed that CD45RBloR123hi cells
were mostly T3 cells, and that it was this population that was enriched in LN patients.
J) The proportion of MZB showed a positive correlation with the proportion of MZP and T3
CD45RBhi cells but a negative correlation with T3 (CD45RBloR123hiCD24+CD38+) cells
S ank coefficient).
K) Scatter plots of flow cytometry data demonstrating no difference in MZB frequency in
African Caribbean (-AC) and Caucasian (-C) HCD and SLE patients (medians, Mann-
Whitney test).
L) Correlation of MZB and SLEDAI score in Caucasian SLE patients S ank
coefficient).
M) Bar graphs demonstrating the immunosuppressive burden of
SLE patients with low MZB counts (< 3.13% CD19+ cells) vs high MZB counts (>3.13%
CD19+ cells) where 3.13% represents the median MZB value in all SLE patients.
N) Scatter plots of flow cytometry data demonstrate that pemphigus vulgaris (PV) patients
taking MMF and / or prednisolone did not have reduced MZB when compared to SLE
patients on the same immunosuppressive medication (mean +/- SD, unpaired t test).
O) Scatter plots of flow cytometry data demonstrate there was no difference in MZB counts
in non-renal SLE (OL) patients taking or not taking hydroxychloroquine (HCQ) therapy
(medians, Mann-Whitney test).
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P) The proportion of MZB, MZP and T3 CD45RBhi showed a negative correlation with
disease activity as indicated by the SLEDAI score S ank coefficient).
Q) Scatter plots showing the proportion of CD27-IgD- B cells as gated in Figure part A
demonstrate increased frequency of this population in LN (medians, Mann-Whitney
test).
R) Flow cytometry dot plot demonstrating the identification of DN1 and DN2 cells based on
expression of CD21 and CD24, and scatter plots showing that DN2 cells were more
abundant in LN than in HCD (medians, Mann-Whitney test).
Figure 7. IgMhi 7 integrinhi T2 cells are reduced in frequency in lupus nephritis.
A) CITRUS trees generated from CD19+ cells from HCD (n=8) and LN patients (n=8) and
clustered according to the expression of CD5, CD9, CD10, CD24, CD27, CD38, CD45RB,
IgD, IgM and IgA (see also Fig. S5 A, B and C, Table S1). Red nodes indicate significantly
different population abundances between HCD and LN patients. A grey background is
automatically assigned to aggregates of significant nodes. Arrows and roman numerals
indicate nodes further analysed in Figure part C.
B) CITRUS trees demonstrating the median expression of the clustering panel markers in
the nodes.
C) Histograms demonstrating the abundance and expression of panel markers in selected
nodes, the identify of these nodes can be inferred as (i) MZP, (ii) MZB, (iii) TS B cells and
(iv) class switched IgA memory.
D) Minimal spanning tree generated by FlowSOM from events exported from CITRUS node
(iii) identified in Figure parts A and C. This generated 5 metaclusters, the identification of
each could be inferred by median expression of panel markers in each metacluster.
E) Minimal spanning trees generated by FlowSOM plots demonstrating CD38 expression in
a representative HCD and LN patient. Prominent skewing of TS cell subpopulations is
evident in LN.
F) Relative abundances of metaclusters as a percentage of TS B cells indicate reduced
frequency of events within metacluster 4 corresponding to IgMhi hi TS B cells in LN,
and increased frequency of events in metaclusters 1,2 and 3 corresponding to T1 cells
(medians, Mann-Whitney test).
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Materials and Methods
Lead contact
Information regarding reagents and resources should be directed to Professor Jo Spencer
(jo.spencer@kcl.ac.uk).
Data and code availability
The datasets generated during this study will be made available on acceptance of the
manuscript.
EXPERIMENTAL SUBJECT DETAILS
All blood and tissue samples were obtained from adults with REC approval and informed
consent. SLE patients were recruited using the following criteria; i) fulfilment of 4 or more
revised ACR classification criteria; ii) ANA positive; iii) Biologic (Belimumab or rituximab)
naïve; iv) Immunosuppressive regimen does not include azathioprine or cyclophosphamide
within 6 months of sample collection due to the severe depletion of naïve B cells by these
medications. All LN patients had diagnostic confirmation by renal biopsy. Blood was
obtained from SLE patients and HCD (REC reference 11/LO/1433: Immune regulation in
autoimmune rheumatic disease). Paired gut biopsies and blood were obtained from
individuals undergoing colonoscopies in whom no mucosal abnormality was detected (REC
reference 11/LO/1274: Immunology the intestine; features associated with autoimmunity).
Samples of draining the gut via the hepatic portal vein were obtained from liver perfusion
prior to transplantation (REC reference 09/H0802/100: The role of innate immune system in
hepatic allograft outcome). Patient demographic data can be found in Tables S1 and S2.
METHODS
Sample Processing
Blood samples were diluted 1:1 in RPMI-1640 containing 10% foetal calf serum (FCS), 100
U/ml penicillin and    (RPMI-P/S). Diluted blood was then layered
onto Ficoll and centrifuged for 25 minutes with brake and accelerator set to 0. The buffy
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coat layer was then removed and cells were washed in RPMI-P/S. PBMC isolated from
patients undergoing colonoscopy was used fresh, whilst PBMC used for the analysis of HCD
and patients with SLE, UC, GPA and PV was cryopreserved in FCS + 10% dimethyl sulfoxide
(DMSO). Mononuclear cells from gut were obtained by the removal of epithelial cells with
1mM EDTA in HBSS containing 100 U/ml penicillin and    for 30
minutes. Collagenase digest was then used to generate a cell suspension using collagenase
D (1 mg/ml) and DNase (10 U/ml) in RPMI-P/S for 1 h.
Mass cytometry
3 mass cytometry panels were utilized, the staining protocols were as follows.
Panel 1: Cryopreserved cells were washed and rested in RPMI-P/S + 0.1mg/ml DNase at 37
degrees for 45 minutes. B cells were then negatively enriched using a Miltenyi B cell
isolation kit II. 4 x 106 were then viability stained with 1ml cisplatin 25M in 1x PBS. Cells
were then washed in PBS containing 0.5% BSA with 2mM EDTA (Cell staining medium, C-SM)
and resuspended in 10ul Fc receptor blocking solution and left for 10 minutes on ice. IgG
staining was then performed in 100l staining volume for 30 minutes on ice. Cells were then
washed in CS-M and resuspended in the pre-titrated volume of antibody mastermix, the
volume was then adjusted to 100ul with CS-M and cells were stained for 30 minutes on ice.
Metal tagged antibodies used are listed in Fig S1 and The Key Resources Table. Cells were
then washed twice in 1xPBS and fixed overnight in 16% paraformaldehyde. The following
day cells were washed in 1xPBS and DNA was stained with 1M Intercalatin in 500ul
permeabilization buffer at room temperature for 20 minutes. Cells were then washed twice
in 1xPBS and twice in Milli-Q water before being resuspended in Milli-Q water plus EQ beads
to a concentration of 0.5 x 106 /ml and run on a Helios Mass cytometer.
Panel 2: As for panel 1 except cells were stained fresh, were not enriched and 2 x 106 cells
were viability stained with 1ml rhodium intercalator diluted in 1:500 in PBS for 20 minutes
at room temperature. Metal tagged antibodies used are listed in Fig S2 and The Key
Resources Table.
Panel 3: As per panel 1 except cells were not enriched and IgG staining was not performed.
Metal tagged antibodies used are listed in Fig S6 and The Key Resources Table.
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Analysis of mass cytometry data
FCS files were normalised using Nolan lab software (v0.3, available online at
https://github.com/nolanlab/beadnormalization/releases). Pre and post normalisation plots
are shown in Fig. S1 B and F and S5 A for the respective datasets. Where files were
concatenated the Cytobank FCS File Concatenation Tool was used (available online at
https://support.cytobank.org/hc/en-us/articles/206336147-FCS-file-concatenation-tool).
Files were then loaded onto the Cytobank (https://mrc.cytobank.org/) and gated to identify
live CD19 B cells and analysed as described in Fig. S1 C and G and S5 C.
For the analysis of HCD PBMC in Fig. 1, viSNE was run on equal numbers of CD19+ events
(n=35000) from each HCD (n=10). SPADE was then run on the viSNE coordinates and B cell
subsets were identified by placing nodes into bubbles. The TS bubble was identified as
CD27-IgD+CD24+++/++CD38+++/++. Events within the TS bubble were exported and a further
viSNE was run using equal events (n=3535) and all panel markers except CD45, CD3, CD14
and class switched isotypes IgA and IgG which are not expressed by TS B cells. CD45 was
excluded due to homogenous expression and lack of contribution to clustering. SPADE was
then run on the viSNE coordinates and TS populations were defined as demonstrated in Fig.
1 B.
For the analysis of PBMC and GALT derived B cells in Fig. 2, equal numbers of CD19+ events
(n=118,934) from concatenated PBMC (n=7) and GALT (n=7) samples were used to run a
viSNE using all markers except for CD45, CD3 and CD14. SPADE was then run on the viSNE
coordinates and TS B cells identified as CD27-IgD+CD10+ nodes. Events within the TS bubble
were then exported and equal numbers of events (n=4520) were clustered using FlowSOM.
CD10, CD24, CD38, IgM as clustering channels to allow the undirected visualization of
markers of TS populations.
For the analysis of PBMC from HCD and SLE samples in Fig 7, CITRUS was run using equal
numbers of CD19+ events (n=20000) from HCD (n=8) and SLE patients (n=8) and the
following clustering channels : CD5, CD9 CD10, CD24, CD27, CD38, CD45RB, IgD, IgM, IgA.
Due to event sharing amongst CITRUS nodes, node 321672 identified in Fig. 7 A contains all
CD27-IgD+CD24+++/++CD38+++/++ events and was therefore used for analysis of TS B cells. All
events from this node were exported and FlowSOM was run using equal event sampling (n=
657) and using all marker channels except CD45, CD3, CD14 and IgA.
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Flow cytometry and cell sorting
Cryopreserved cells used for flow cytometry were thawed and washed in RPMI-P/S and then
rested at 37 degrees in RPMI-P/S + 0.1mg/ml DNase for 45 minutes. Viability staining with
Zombie aqua dye was performed using 100l 1:200 dilution in 1xPBS, or with DAPI
0.1mg/ml diluted 1:1000 and added prior to sample acquisition on the flow cytometer. Cells
were stained on ice for 15 mins with pre-titrated concentrations of antibodies listed in The
Key Resources Table. Staining with R123 was performed for 10 minutes at a concentration
of 6M and cells were washed and chased for 3 hours in RPMI-P/S. All samples were
analysed by a BD LSRFortessa (BD Biosciences). Anti-Mouse/Rat beads (BD) were used for
fluorescent compensation and gates were set using appropriate isotype controls. Cell
sorting was performed using a BD FACSAria (BD Biosciences) and live single CD19+ B cells
were gated as follows IgMhi TS : CD27-IgD+CD10+IgMhi, IgMlo TS : CD27-IgD+CD10+IgMlo, IgMhi
naïve: CD27-IgD+CD10-IgMhi, IgMlo naïve : CD27-IgD+CD10-IgMlo, where IgMhi and IgMlo gates
captured 30% of the highest and lowest IgM expressing cells respectively.
Cytokine detection
Fresh PBMC was isolated from HCD and incubated for 6 hours at 37 degrees with phorbol
12-myristate 13-Acetate (PMA) 50ng/ml and Ionomycin 250ng/ml with Golgiplug at a
dilution of 1:1000. Cells were then surface stained as above followed by fixation with
Cytofast buffer (Biolegend). Cells were then washed twice and stained with resuspended
conjugated antibodies in permeabilization / wash buffer (Biolegend) for 20 minutes at room
temperature.
Cell culture and stimulation analysis
Sorted IgMhi and IgMlo TS and naïve (CD27-IgD+CD10-) B cell subsets were plated onto a 96
well plate seeded with 2 x 104 cells per well. Wells containing CD40L expressing HEK cells
were also seeded with 2 x 104 irradiated HEK cells per well. Cells were then stimulated with
CPG-ODN 2.5g/ml or anti-IgM 10g/ml. Proliferation assays were performed on cells
stained with CellTrace violet as per the manufacturers guidelines. Cells were then stained
and analysed by flow cytometry as above.
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Single cell RNA sequencing library preparation
Sorted cell populations were loaded onto a 10x Genomics Chromium Controller   
expression, VDJ and ADT (for samples in Fig. 4) were prepared according to the
manufacturers guidelines. Samples used in Fig. 3 were sequenced using an Illumina NextSeq
500 platform. Samples used in Fig. 4 were sequenced using an Illumina HiSeq 2500 High
Output platform. The 10x Genomics Cellranger workflow was then used for transcript
alignment and the generation of sparse matrices for downstream analysis.
CITE-Seq antibody staining
Cryopreserved samples were thawed and sorted using the gating strategy in Fig. S3 A. Cells
were then washed and stained in a CITE-Seq antibody cocktail at a concentration of 8g/ml
for 30 minutes on ice. Cells were then washed three times before loading onto the 10x
Chromium controller.
Single cell sequencing analysis
The Seurat R package (vs 3.1.1) was used to filter data to remove cells with low numbers of
RNA transcripts, doublets and cells with high levels of mitochondrial transcripts indicative of
cell death. Immunoglobulin variable genes were then removed from the dataset as well as
cells with low expression of B cell genes CD79A, CD79B, CD19 or MS4A1. Data from IgMhi
and IgMlo TS B cells were merged and the data was transformed in accordance with the
SCTransform workflow before UMAP based reduction of dimensionality and PCA-based
clustering to identify populations (Hafmeister, 2020). Heatmaps were then created using
select genes from the top 60 differentially expressed genes in each sample, and dot plots
and violin plots on selected genes. Data from sorted CD19+ cells from HCD used for Fig. 4
and Fig. S3 and 4 was initially analysed individually followed by an integrated analysis.
Individual analysis was performed using the quality control (QC) steps as well as the removal
of IGHV genes and non- B cells as described above. Data was then normalized and scaled
and UMAP run on a PCA generated using 2000 variable genes. Overlay of ADT and gene
signal, violin plots and median expression of markers by UMAP clusters was used to identify
which B cell subsets they corresponded to. For the integrated data analysis, data from 3
HCD was filtered using the QC steps as well as the removal of IGHV genes and non- B cells as
described above. Data was then normalized using the SCTransform wrapper in Seurat
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followed by integration using The Satija Laboratory Integration and Label Transfer
protocol (Butler et al., 2018), using 3000 integration features. The 2000 most variable genes
were then used to perform PCA and a 3-dimensional UMAP was obtained from this.
Clusters were obtained using the FindNeighbours and FindClusters functions within Seurat,
using default parameters. The UMAP coordinates and cluster allocations were then used to
run Slingshot (Street et al., 2018). Randomised downsampling of 50% was required to improve
the performance of trajectory inference in Slingshot. ADT overlay of the UMAP plot was used
to identify the cluster composed of CD27-IgD+CD38hi cells that best represented TS B cells
and this was chosen as the starting point from which Slingshot would build trajectories. A
heatmap was then created using genes of interest amongst the top 100 differentially
expressed genes on the trajectory.
Quantitative RT-PCR
Quantitative RT-PCR was performed using Taqman Gene Expression Assays (FAM,
Thermofisher Scientific) were used to quantify CCR7 and ITGB7 expression in cDNA from
sorted IgMhi and IgMlo TS B cell subsets. Reactions were performed in duplicate and
multiplexed with Eukaryotic 18S rRNA Endogenous Control (VIC). Samples were run on a
QuantStudio 5 Real Time PCR System (Thermofisher Scientific). CT was calculated using
Thermofisher Connect software (available online at
(https://apps.thermofisher.com/apps/spa/#/dataconnect).
QUANTIFICATION AND STATISTICAL ANALYSIS
Flow cytometry and mass cytometry data
Flow cytometry data was visualized and gated using FlowJo v 10.6.1. Mass cytometry data
was analysed using cytobank software.
Statistical analysis
Graphpad prism version 7.0 was used for statistical analysis. Paired t tests or Wilcoxon tests
were used to compare paired samples whilst unpaired t tests or Mann-Whitney tests were
used for unpaired samples. Adjusted p values were represented as * p=<0.05, ** p=<0.01,
*** p=<0.001. All error bars show the mean +/- standard deviation.
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Supplemental videos
Supplemental video 1. Rotation of the 3D UMAP plot demonstrating B cell subsets from 10x
HCD1 as depicted in Fig. 4A and S3 D, E, F and G.
Supplemental video 2. Rotation of the 3D UMAP plot demonstrating B cell subsets from 10x
HCD2 as demonstrated in Fig. S4 A, B and C.
Supplemental video 3. Rotation of the 3D UMAP plot demonstrating B cell subsets from 10x
HCD3 as demonstrated in Fig. S4 D, E and F.
Supplemental video 4. Rotation of 3D UMAP plot as depicted in Fig. 4 G with overlay of IgM
ADT signal demonstrating that the Slingshot trajectory passes through IgMhi naïve B cells.
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CD10 CD24
B
CD45RB
CCR7
F
C
T1
T2
T3
CD21 IgM D β7 integrin
A
CD38
E
I
IgD CD45RB
Contributions (%)
CCR7
CD62L
CD5
CLA
CD21
CD69
RANKL
CD138
CD40
HLA-DR
CD19
CD20
IGM
BCMA
BAFFR
CCR6
CD23
IgD
CD45RB
CD180
CD22
CD38
CD24
β integrin
4 integrin
β integrin
5
10
Dim1
Dim2
CD27
IgM CD38 CD24 CD10 CD21
CD27-IgD+
CD24+++/++
CD38+++/++
CD10+/-
CD27-IgD+
CD24+
CD38+
CD10-
CD27-IgD+
CD45RBhi
CD27-IgD+
CD24-CD38-CD21-
IgA+
IgG+
TS
11 cells
687 cells
2 cells
135 cells
CCR7
ITGB7
*
TS IgM hi
TS IgM lo
0
2
4
6
B7 integrin
2-DCT * 100000
IgMhi TS IgMlo TS
**
TS IgM hi
TS IgM lo
0
10
20
30
CCR7
2-DCT +100000
IgMhi TS IgMlo TS
H
Dim2 (22.6%)
Dim1 (44.3%)
IgMhi TS
IgMlo TS
IgMhi naïve
IgMlo naïve
Contributions (%)
β integrin
IgD
IgM
HLA-DR
BCMA
CLA
CD22
CD180
CD69
CD21
CD38
BAFFR
CD20
4 integrin
RANKL
CD24
CD40
CD45RB
CD5
CD62L
CD23
CCR7
CCR6
CD19
CD138
β integrin
2
4
6
G
Fig. 1
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
CD38
CD24
IgM
C
CD10
E
G
IgD CD27
CD21
IgM
F
CD38
TS
HCD PBMC Liver perfusate
IgM
CD69
CD80
CD24
CD38
H
PBMC GALT
D
T1
T2 Mhi
T2Mlo
T1
T2 Mhi
T2Mlo
T1
T2 Mhi
T2Mlo
PBMC GALT
I
IgM
A
Naïve
CD27-IgD+
T2
IgMhiCD24hi T2 IgMhiCD24hi Naïve
HCD
PBMC
Liver
perfusate
% T2
% Naïve
HCD
PBMC
Liver
perfusate
IgM
2.98 cells
1745.18 cells
T1
36.0
T2
64.2
T1
51.2
T2
49.3
IgMhiCD24hi
44.9
IgMhiCD24hi
28.4
IgMhiCD24hi
26.6
IgMhiCD24hi
4.67
4
1
3
2
5
6
Metacluster Population
inferred
T1
IgMhi T2
B
IgMlo T2
HCD
LW
0
10
20
30
CD10- Mhi CD24hi
CD10-IgMhiCD24hi %CD10-
HCD
LW
0
20
40
60
80
T2 Mhi24hi
T2 IgMhi CD24 hi (%T2)
Fig. 2
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
A B
C
E F
Expression
D
IgMhi TS
IgMlo TS
IgMlo TS
IgMhi TS
IgMhi TS
IgMlo TS
IgMhi TS IgMlo TS
MZB
CSM
CD10+
CD10-
CD27
IgD
CD10
FSC-A
MZB
CSM
CD19+ CD27-IgD+
R123
CD45RB
T3 RBHI MZP
T3
RBLO
Naïve
RBLO
CD27-IgD+CD10-
CD21
IGM
CD38 FSC-A
T2
T1
CD27-IgD+CD10+
G
J I
H
CD1C MFI
Count
MZP
Naïve RBLO
T3 RBLO
T3 +RBHI
CD1C MFI
T3
RBLO
Naïve
RBLO
MZP T3
RBHI
RH123+RBHI
RH123+RBLO
RH123-RBHI
RH123-RBLO
0
2000
4000
6000
8000
10000
CD1C MFI RBHI vs Lo subsets
CD1C MFI
***
****
CD10+
CD10-
IgMlo T2
IgMhi T2
T1 IgMlo
T2
IgMhi
T2
CD19+
CD1C MFI
Count
K L
T1
T2 MLO
T2 MHI
0
2000
4000
6000
8000
CD1C T1 T2 subsets
CD1C MFI
****
Fig. 3
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
IgM-ADT CD1C
CD27-ADT
High
Low
Immature
B cell
IgMhi
TS genes
B cell
function
Cell activation
and migration
IFN inducible
Transcription
Pseudotime
TS
MZB
A
D C
B
E
G
H
F
I
High
Low
High
Low
UMAP 1
UMAP 3
High
Low
UMAP 1
UMAP 3
UMAP 1
UMAP 3
UMAP 1
UMAP 3
Fig. 4
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
CD45RB
TS
Naïve
CTV
Count
A
CD27-IgD+CD10+
IL-10
D
TS IgM lo
TS IgM hi
0
20000
40000
60000
80000
100000
IgM MFI
CPG IgM MFI
**
TS IgM lo
TS IgM hi
0
1000
2000
3000
4000
CD45RB MFI
CD45RB MFI CPG
*
FSC-A
MN IgM lo
MN IgM hi
0
2000
4000
6000
8000
10000
CD45RB MFI
CD45RB MFI CPG
**
FSC-A
CD40L+anti-IgM
B
Undivided
Undivided
E F
IgMhi TS
IgMhi TS IgMlo TS
IgMlo TS
IgMhi TS IgMlo TS
IgMlo IgMhi
IgMhi TS
IgMlo TS
IgMhi
Naïve
IgMlo
Naïve
IgMhi TS
IgMlo TS
IgMhi TS
IgMlo TS
% divided
C
IgM
FSC-A
IgMlo TS
IgMhi TS
Day 0 Day 5
TS IgM lo
TS IgM lo
70
80
90
100
% divided
TS CD40L+aIGM
*
TS IgM lo
TS IgM hi
0
5
10
15
20
25
%IL10+
*
Fig. 5
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
010 20 30 40
0
2
4
6
MZB vs MZP
MZB (%CD19+)
MZP (%CD19+)
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
010 20 30 40
0
5
10
15
MZB vs T3 RBHI
MZB (%CD19+)
T3 RBHI (%CD19+)
A
O
B
D
CD27
IGD
HCD SLE
R123
CD45RB
CD27-IgD+CD10-
HCD
SLE
J
CD21
CD24
CD27-IgD-
I
CD38
CD24
MZP T3 RBHI
Naïve R123hi
RBLO
TS3
aNAV
DN1
DN2
R= 0.132
p= <0.0027
R= 0.160
p= 0.0009 R= -0.312
p= <0.0001
E F
G H
Q
SLE
HCD LN OL HCD LN OL
MMF PRED HCQ
HCD LN OL
HCD LN OL
HCD LN OL TS3 aNAV
HCD LN OL HCD LN OL
CD27+IgD+ (%CD19+)
CD27+IgD+ (%CD19+)
MZP (%CD19+)
T3RBHI (%CD19+)
TS3 (%CD19+)
Naïve (%CD19+)
R123hi RBLO (%CD19+)
% R123hi RBLO
aNAV (%CD19+)
TS3
aNAV
0
50
100
% R123HI RBLO
aNAV vs TS3
HCD
LN
OL
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
DN1 (%CD19+)
DN2 (%CD19+)
HCD LN GPA PV
HCD LN OL
CD27-IgD- (%CD19+)
CD27+IgD+ (%CD19+)
HCD SLE
C
OL UC
K
HCD
-AC
HCD
-C
SLE
-AC
SLE
-C
MMF
Pred
HCQ
0
20
40
60
80
100
Drugs low MZB vs hi all lupus
% patients
Low MZB <4%
High MZB >4%
M
HCD LN OL
PV
N
P
0 5 10 15
0
5
10
15
20
25
All lupus SLEDAI vs T3RBHI
T3 RBHI (%CD19+)
SLEDAI
010 20 30
0
5
10
15
20
25
All lupus SLEDAI vs MZB
MZB (%CD19+)
SLEDAI
HCD
LN
OL
0
20
40
60
80
Naive
Naive (%CD19+)
**
*
HCD
LN
OL
0
20
40
60
RH+RBLO
RH123+ RBLO (%CD19+)
****
***
*
HCD
LN
OL
0
10
20
30
40
50
TS3
TS3 (%CD19+)
****
*
*
HCD
LN
OL
0
5
10
aNAV (%CD19+)
aNAV
**
0 2 4 6
0
5
10
15
20
25
MZP (%CD19+)
SLEDAI
All lupus SLEDAI vs MZP
R= 0.117
p= 0.026
R= 0.158
p= 0.01
R= 0.147
p= 0.013
R
HCQ No
HCQ
CD27+IgD+ (%CD19+)
HCQ
No HCQ
0
10
20
30
MZB (%CD19+)
MZB OL HCQ vs no HCQ
010 20 30
0
5
10
15
MZB vs SLEDAI caucasian
MZB
SLEDAI
SLEDAI
MZB (%CD19+)
L
MMF
Pred
HCQ
0
20
40
60
80
100
Drugs low MZB vs hi all lupus median values
% patients
Low MZB
High MZB
% patients
R= 0.39
p= 0.002
LN
OL
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
010 20 30 40
0
20
40
60
MZB (%CD19+)
T3 (%CD19+)
MZB vs TS3
HCD
SLE
0
10
20
30
40
CD27+IgD+ (%CD19)
MZB HCD vs all lupus
****
HCD
LN
OL
UC
GPA
PV
0
10
20
30
40
MZB (%CD19+)
MZB other diseases
****
***
HCD
LN
OL
0
2
4
6
MZP
MZP (%CD19)
****
*
**
HCD
LN
OL
0
5
10
15
T3 RBHI
T3 RBHI (%CD19+)
***
Afrocaribbean HCD
Caucasian HCD
Afrocaribbean SLE
Caucasian SLE
0
10
20
30
40
MZB (%CD19+)
MZB afrocaribbean HCD vs Caucasian
HCQ
No HCQ
0
10
20
30
MZB (%CD19+)
MZB OL HCQ vs no HCQ
HCD
LN
OL
0
10
20
30
40
DN
CD27-IgD- (%CD19+)
*
HCD
LN
OL
0
10
20
DN2 (%CD19+)
DN2
****
****
HCD
LN
OL
0
5
10
15
20
25
DN1
DN1 (%CD19+)
Fig. 6
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
CD5 CD45RB IgD IgA CD38 CD10 CD24 CD27 CD9 IgM
i
ii
iii
i
ii
iv
iv
A B
C
iii
IgM IgD CD45RB CD27
CD24 CD38 CD9 IgA CD5
D
Metacluster
4
1
3
2
5
Median cluster expression
Metacluster 4
Metacluster 3 Metacluster 5
E
Inferred
population
T1
T1/T2
T2IgMhi
T2IgMlo
LN HCD
HCD
LN
F
HCD LN HCD LN
HCD LN HCD LN HCD LN
Metacluster 1 Metacluster 2
HC
LN
0
5
10
15
20
% TS
Metacluster 1
HC
LN
0
20
40
60
80
Metacluster 2
%TS
***
HC
LN
0
5
10
15
20
25
% TS
Metacluster 4
**
HC
LN
0
20
40
60
80
Metacluster 5
% TS
***
HC
LN
0
5
10
15
20
Metacluster3
% TS
***
Fig. 7
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
A
E
B
C
Intact cells Not beads Live Cleanup Residual
Cleanup Centre Cleanup offset Cleanup length CD3/CD14-CD19+
Label
Target
Clone
141Pr
CD196 (CCR6)
11A9
142Nd
CD40
5C3
143Nd
CD5
UCHT2
144Nd
CD69
FN50
145Nd
CD45RB
MEM
-55
146Nd
IgD
IA6
-2
147Sm
CD20
2H7
148Nd
IgA
Polyclonal
149Sm
RANKL
MIH24
150Nd
CD138
DL
-101
151Eu
CD49d (a4)
9F10
152Sm
CD21
BL13
153Eu
CD62L (L
-
selectin)
DREG
-56
154Sm
CD38
HIT2
155Gd
CD268 (BAFF
-R)
11C1
156Gd
CD29 (integrin b1)
TS2/16
158Gd
CD10
HI10A
159Tb
CD22
HIB22
160Gd
CD14
M5E2
160Gd
CD3
UCHT1
161Dy
CD80 (B7
-1)
2D10.4
162Dy
Integrin b7
FIB504
163Dy
CD180
MHR7311
164Dy
CD23
EBVCS
-5
165Ho
CD19
HIB19
166Er
CCR10 (GPR
-2)
314305
167Er
CD27
323
168Er
TACI
1A1
169Tm
CD24
ML5
170Er
FcRL4
413D12
171Yb
IgG
G18
-145
172Yb
IgM
MHM
-88
173Yb
HLA
-DR
L243
174Yb
BCMA
19F2
175Lu
CD197 (CCR7)
G043H7
176Yb
CLA
HECA
-452
89Y
CD45
H130
103Rh
Viability
D
F
Fig. S1. Gating and analysis of mass cytometry data used for Fig. 1 and 2.
A)
Mass cytometry panel used for analysis in Figure 1.
B)
Pre-and post normalization plots of mass cytometry data used for Fig.1 .
C)
Gating strategy of mass
cytometry data to identify live CD19+B cells.
D)
Flow cytometry plots demonstrating identification of T3 as CD27-IgD+CD10-R123hi and naïve (N) B cells as CD27-
IgD+CD10-R123lo.
E)
Mass cytometry panel used for analysis in Figure 2.
F)
Pre-and post normalization plots of mass cytometry data used for Fig. 2.
G)
Gating
strategy of mass cytometry data to identify live CD19+B cells.
H)
Spade trees demonstrating expression of IgM and CD45RB in the concatenated GALT sample
(see also Fig. 2 A.)
Label
Target
Clone
141Pr
CD196 (CCR6)
11A9
142Nd
CD40
5C3
143Nd
CD5
UCHT2
144Nd
CD69
FN50
145Nd
CD45RB
MEM
-55
146Nd
IgD
IA6
-2
147Sm
CD20
2H7
148Nd
IgA
Polyclonal
149Sm
RANKL
MIH24
150Nd
CD138
DL
-101
151Eu
CD49d (a4)
9F10
152Sm
CD21
BL13
153Eu
CD62L (L
-selectin)
DREG
-56
154Sm
CD38
HIT2
155Gd
CD268 (BAFF
-R)
11C1
156Gd
CD29 (integrin b1)
TS2/16
158Gd
CD10
HI10A
159Tb
CD22
HIB22
160Gd
CD14
M5E2
160Gd
CD3
UCHT1
161Dy
CD80 (B7
-1)
2D10.4
162Dy
Integrin b7
FIB504
164Dy
CD23
EBVCS
-5
165Ho
CD19
HIB19
166Er
CCR10 (GPR
-2)
314305
167Er
CD27
323
168Er
TACI
1A1
169Tm
CD24
ML5
170Er
FcRL4
413D12
171Yb
IgG
G18
-145
172Yb
IgM
MHM
-88
173Yb
HLA
-DR
L243
174Yb
BCMA
19F2
175Lu
CD197 (CCR7)
G043H7
176Yb
CD180
HECA
-452
89Y
CD45
H130
103Rh
Viability
IgM CD45RB
H
G
Intact cells Not beads Live
CD3/CD14-CD19+
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Sample Cells
captured
Sequencing
depth
(reads/cell)
Median
genes/cell
TS
IgMhi
2475 52231 1613
TS
IgMlo
2311 55622 1488
Fig. S2. Sort strategy, 10x genomics workflow and validation.
A)
FACS sort strategy to identify IgMhi and IgMlo TS B cell subsets.
B)
Purity plots of sorted IgMhi and IgMlo TS B cell subsets.
C)
10x genomics experimental
workflow detailing pooling of HCD samples.
D)
Summary table of cell numbers captured by the 10X controller and sequencing depth of IgMhi and IgMlo TS B
cell subsets.
E)
Violin plot demonstrating expression of the IL4R gene in IgMlo TS B cells.
F)
Scatter plots of flow cytometry data demonstrating higher
frequency of IL4R on IgMlo compared to IgMhi TS (CD27-IgD+CD10+) and naïve (CD27-IgD+CD10-) cells (mean +/-SD, paired t test).
G)
Violin plot demonstrating
expression of the SELL (CD62L) gene in IgMlo TS B cells.
H)
qPCR confirms higher levels of the SELL gene transcript in IgMhi TS B cells expressed as !CT values
relative to an 18S endogenous control (paired t test).
I)
IPA upstream regulator plot demonstrating enrichment of LPS induced genes in IgMhi TS B cells.
J)
IPA
upstream regulator plot demonstrating enrichment of retinoic acid induced genes in IgMhi TS B cells.
K)
IPA upstream regulator plot demonstrating
enrichment of IFN-"induced genes in IgMlo TS B cells.
L)
Bar graphs demonstrating a lower frequency of VH1 and higher frequency of VH3 immunoglobulin
variable heavy chain gene usage in IgMhi TS B cells than TS IgMlo cells (Chi squared test with Bonferroni correction).
M)
Scatter plot of flow cytometry data
from HCD demonstrating that T3 and naïve CD45RBhi subsets as gated in Figure 3J share high IgM expression (MFI mean +/-SD, paired t test).
N)
T3 and naïve
CD45RBhi subsets share similar high surface expression of CD24 (MFI mean +/-SD, paired t test).
A
C
D
TS IgMhi TS IgMlo
B
F
IgM hi
IgM lo
0
20
40
60
80
% IL4R+
*
CD27-IgD+CD10+
IgM hi
IgM lo
0
20
40
60
80
% IL4R+
*
CD27-IgD+CD10-
EIL4R SELL
G H
*
TS IgM hi
TS IgM lo
0
10
20
30
40
50
CD62L
2-DCT * 100000
IgMhi
TS
IgMhi
TS
IgMlo
TS
IgMlo
TS
IgMhi
TS
IgMlo
TS
IgMhi
naïve
IgMlo
naïve
IgMhi
TS
IgMlo
TS
SELL
% IL4R+
% IL4R+
SSC-A
FSC-A
SSC-W
SSC-H
FSC-W
FSC-H
Viability
CD19
CD27
IgD
CD10
FSC-A
CD10
FSC-A
IgM
CD21
JI
K
T3 RBHI
T3 RBLO
N RBHI
NRBLO
0
5000
10000
15000
IgM MFI
IgM MFI
****
****
T3 RBHI
T3 RBLO
N RBHI
NRBLO
0
2000
4000
6000
CD24 MFI
CD24 MFI
****
****
M
N
T3
RBHI
T3
RBLO
MZP Naïve
RBLO
L
VH1 VH2 VH3 VH4 VH5 VH6
0
10
20
30
40
50
% of total sequences
TS IgMlo
TS IgMhi
*
***
VH1 VH2 VH3 VH4 VH5 VH6
0
10
20
30
40
50
% of total sequences
TS IgMlo
TS IgMhi
*
***
IgMhi TS
IgMlo TS
T3
RBHI
T3
RBLO
MZP Naïve
RBLO
TS IgMhi and IgMlo
cells loaded onto
the 10x controller in
separate lanes
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
SSC-A
Live dead
SSC-A
CD19
FSC-A FSC-A FSC-A FSC-A
A
CSample Age Sex Cells
captured
Sequencing
depth
(reads/cell)
Median
genes/cell
10x HCD1 34 F4138 19764 940
10x HCD 2 43 F6849 21,428 681
10x HCD 3 42 F11445 20,030 566
B
Antibody Clone
TotalSeq™-C0154 anti-human CD27 0323
TotalSeq™-C0384 anti-human IgD IA62
TotalSeq™-C0136 anti-human IgM MHM-88
TotalSeq™-C0062 anti-human CD10 HI10a
TotalSeq™-C0181 anti-human CD21 Bu32
TotalSeq™-C0180 anti-human CD24 ML5
TotalSeq™-C0389 anti-human CD38 HIT-2
Fig. S3. Sort strategy and 10x genomics workflow and identification of B cell subsets represented by UMAP clusters in 10x
HCD 1.
A)
Gating strategy to sort live CD19+ cells.
B)
Total-Seq antibodies and clones used for surface labelling of CD19+B cells.
C)
Demographic
details of HCD, cells captured and sequencing depth.
D)
UMAP plot demonstrating clusters generated from a PCA run on 2000 differentially
expressed genes from 10x HCD1.
E)
Feature plots demonstrating lineage defining ADT (CITE-Seq antibody) and transcript signal overlay on
the UMAP plot.
F)
Table of median IgM expression within clusters representing naïve cells (CD27-IgD+CD38int), the top 30% of clusters were
designated as IgMhi.
G)
Merged and pseudocoloured clusters representing B cell subsets defined by ADT and gene signal of lineage defining
targets.
ED ADT-CD27 ADT-IgM ADT-IgD ADT-CD38
High
Low
CD27 IGHDIGHM CD1C
High
Low
HOPX COCH
CLUSTER
IGM
ID
CLUSTER
IGM
ID
7
121
H
35
62
N
13
110.5
H
188
55
N
41
96
H
34
54
N
39
95
H
29
52
N
19
86.5
H
32
52
N
27
82
H
28
50
N
43
78.5
H
38
48.5
N
2
74.5
H
24
46
N
45
71.5
H
33
45
N
37
69.5
H
16
43
N
14
68
N
9
41
N
31
68
N
6
40.5
N
20
66
N
44
39
N
36
66
N
42
38
N
21
65.5
N
1
33.5
N
40
64
N
25
33.5
N
15
62.5
N
12
17
N
F G
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
AB
Fig. S4. Identification of B cell subsets represented by UMAP clusters in 10x HCD 2 and 3.
A)
UMAP plot generated from a PCA run on 2000 differentially expressed genes from 10x HCD 2. Clusters were merged and pseudocoloured to
represent B cell subsets defined by ADT and gene signal of lineage defining targets as described in Figure S4D-G.
B)
Feature plots demonstrating lineage
defining ADT (CITE-Seq antibody) and transcript signal overlay on the UMAP plot in Figure part A.
C)
3D UMAP plots demonstrating the spatial
relationship of clusters identified in Figure part A.
D)
UMAP plot generated from a PCA run on 2000 differentially expressed genes from 10x HCD 3.
Clusters were merged and pseudocoloured to represent B cell subsets defined by ADT and gene signal of lineage defining targets as described in Figure
S4D-G.
E)
Feature plots demonstrating lineage defining ADT (CITE-Seq antibody) and transcript signal overlay on the UMAP plot in Figure part D.
F)
3D
UMAP plots demonstrating the spatial relationship of clusters identified in Figure part D.
ADT-CD27 ADT-IgM ADT-IgD
ADT-CD38 CD1C COCH
High
Low
High
Low
ADT-CD27 ADT-IgM ADT-IgD
ADT-CD38
High
Low
High
Low
CD1C COCH
C
DE
F
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
Fig. S5. Gating and analysis of mass cytometry data used for Fig. 7 and proposed model of defective MZB differentiation in
severe SLE.
A)
Mass cytometry panel used for the analysis in Fig.7.
B)
Pre-and post normalization plots of mass cytometry data used in Fig. 7.
C)
Gating
strategy to identify live CD19+B cells.
D)
Proposed model of defective MZB differentiation in severe SLE.
AB
C
D
Intact cells Not beads
Live
Cleanup Residual Cleanup Centre
Cleanup offset CD3/CD14-CD19+
Label
Target
Clone
142Nd
CD19
HIB19
143Nd
CD5
UCHT2
144Nd
CD69
FN50
145Nd
CD45RB
MEM
-55
146Nd
IgD
IA6
-2
147Sm
CD11c
Bu15
148Nd
IgA
Polyclonal
149Sm
IL4R
GP77F6
150Nd
CD138
DL
-101
152Sm
CD21
BL13
153Eu
CXCR5
RF8B2
154Sm
CD38
HIT2
155Gd
CD268 (BAFF
-R)
11C1
156Gd
CD29 (integrin b1)
TS2/16
158Gd
CD10
HI10A
159Tb
CD22
HIB22
160Gd
CD14
M5E2
160Gd
CD3
UCHT1
161Dy
CD80 (B7
-1)
2D10.4
162Dy
Integrin b7
FIB504
164Dy
CD23
EBVCS
-5
165Ho
FCRL5
509F6
166Er
CD24
ML5
167Er
CD27
323
168Er
TACI
1A1
169Tm
CD25
2A3
170Er
FcRL4
413D12
171Yb
CD9
SN4 C33A2
172Yb
IgM
MHM
-88
173Yb
HLA
-DR
L243
174Yb
BCMA
19F2
175Lu
CD197 (CCR7)
G043H7
89Y
CD45
H130
195
Cisplatin viability
T1
Naïve
MZP
MZB T3 T2
CD27-CD10+CD24+++CD38+++
CD21lo ABCB1
-
CD27-CD10+CD24++CD38++
CD21hi ABCB1-
CD27-CD10-CD24++CD38+
CD21hiABCB1-
CD45RBhi and lo variants
CD27-CD10-CD24+CD38+
CD21hiABCB1+
CD27-CD10-CD24++CD38+
ABCB1+CD45RBhi
CD27+CD10-CD24++CD38+
ABCB1-CD45RBhi
IgM+IgD+
Gut/spleen
IgMhib7hi
Periphery
IgMlob7loIL4Rhi
IgMhi IgMlo
Down in severe lupus ; CD45RBhi variants down in severe lupus ; up in severe lupus
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
Table S1. SLE patient demographic data used for Figure 6 and 7. C = Caucasian, AC = African
Caribbean, SEA = South East Asian, MMF = mycophenolate mofetil, PRED = prednisolone, HCQ =
hydroxychloroquine.
Age
Sex
Ethnicity
Nephritis
Cutaneous
Arthritis
Antibodies
Medication
SLEDAI
Figure
50
F
C
ANA, dsDNA
Pred, MMF, HCQ
9
6,7
63
f
AC
ANA, dsDNA,
Ro, RNP, Sm
MMF, HCQ
2
6,7
49
F
AC
dsDNA, Ro
PRED, MMF, HCQ
9
6
51
F
C
ANA, dsDNA,
RNP
PRED, MMF, HCQ
2
6
24
F
C
dsDNA, Sm,
RNP
PRED, MMF, HCQ
6
6,7
30
F
AC
ANA, dsDNA, la,
RNP, Sm
PRED, MMF, HCQ
7
6
46
F
AC
ANA, RNP, Sm
MMF, HCQ
4
6
31
F
C
ANA, dsDNA
Pred, MMF, HCQ
9
6
31
F
AC
ANA, C1Q,
dsDNA, RNP,
Sm
None
21
6,7
48
F
C
ANA, dsDNA,
RNP
HCQ
13
6
45
M
C
ANA, Sm
PRED, MMF, HCQ
9
6
30
f
C
ANA, dsDNA
Pred, HCQ
7
6,7
27
F
C
ANA, dsDNA,
C1Q
Pred, MMF, HCQ
9
5
43
F
AC
ANA, dsDNA,
RNP, Sm
Pred, MMF, HCQ
18
6
53
F
C
ANA, C1Q,
dsDNA, RNP, Ro
PRED, MMF
9
6
41
F
C
ANA, dsDNA
PRED, MMF, HCQ
12
6,7
31
F
C
ANA, dsDNA
PRED, MMF, HCQ
5
6
36
F
AC
ANA, dsDNA, Ro
PRED, MMF, HCQ
8
6
29
F
AC
ANA, C1Q,
dsDNA, La, RNP,
Ro, Sm
MMF, HCQ
4
6
25
F
AC
ANA, dsDNA, Ro
PRED, MMF, HCQ
10
6
68
F
AC
ANA, dsDNA,
RNP, Sm
PRED, HCQ
13
6
48
F
C
ANA, dsDNA,
RNP, Sm
PRED, MMF, HCQ
8
6
20
F
C
ANA, dsDNA,
Sm
PRED, MMF, HCQ
10
6
57
F
AC
ANA, Sm, RNP
MMF, HCQ
5
7
48
F
C
ANA, dsDNA
MMF, PRED, HCQ
13
7
36
F
C
ANA, RNP, Ro,
Sm
PRED
6
27
M
SEA
dsDNA, RNP,
Sm
PRED, MMF, HCQ
4
6
30
F
AC
Sm, RNP
HCQ
10
6
59
f
AC
dsDNA
PRED.
5
6
35
F
AC
ANA, dsDNA,
La, Ro, Sm
PRED, HCQ
13
6
56
F
AC
ANA, RNP, Ro
HCQ
8
6
53
F
AC
ANA, RNP, Sm
NONE
3
6
54
M
AC
ANA, dsDNA,
RNP, Sm
HCQ
3
6
43
F
C
ANA, C1Q,
dsDNA
PRED, HCQ
8
6
22
F
AC
ANA, RNP
PRED, MMF, HCQ
3
6
52
F
AC
ANA, dsDNA, Ro
PRED, MMF, HCQ
0
6
52
F
C
ANA
NONE
0
6
58
F
AC
ANA, Ro
MEP
3
6
29
F
C
ANA, dsDNA
PRED, HCQ
3
6
53
F
C
ANA, Ro
PRED
4
6
21
F
C
ANA, Ro
PRED, MMF
6
6
30
F
C
ANA
HCQ
0
6
39
F
C
ANA, dsDNA
MMF, HCQ
2
6
Supplementary Table 1. SLE patient demographic data used for Figure 5 and 6. C =
Caucasian, AC = African Caribbean, SEA = South East Asian, MMF = mycophenolate mofetil,
PRED = prednisolone, HCQ = hydroxychloroquine.
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
Table S2. HCD demographic data used in Fig. 6. SEA = south east Asian, IA = Indian Asian.
Table S3. Demographic data of patients with other autoimmune diseases. GPA =
granulomatosis with polyangiitis, PV = pemphigus vulgaris, UC = ulcerative colitis, AZA =
azathioprine, MTX = methotrexate, MES = mesalazine.
.CC-BY 4.0 International licensepreprint (which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for thisthis version posted September 25, 2020. . https://doi.org/10.1101/2020.09.24.311498doi: bioRxiv preprint
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