Abstract
Intrarenal B cells in human renal allografts indicate transplant
recipients with a poor prognosis, but how these cells contribute to
rejection is unclear. Here we show using single-cell RNA sequencing
that intrarenal class-switched B cells have an innate cell
transcriptional state resembling mouse peritoneal B1 or B-innate (Bin)
cells. Antibodies generated by Bin cells do not bind donor-specific
antigens nor are they enriched for reactivity to ubiquitously expressed
self-antigens. Rather, Bin cells frequently express antibodies reactive
with either renal-specific or inflammation-associated antigens.
Furthermore, local antigens can drive Bin cell proliferation and
differentiation into plasma cells expressing self-reactive antibodies.
These data show a mechanism of human inflammation in which a breach in
organ-restricted tolerance by infiltrating innate-like B cells drives
local tissue destruction.
Subject terms: Peripheral tolerance, B-1 cells, Transplant immunology
__________________________________________________________________
Intrarenal B cells are indicative of poor prognosis in human renal
allografts. Here the authors use single cell RNA sequencing to examine
how intrarenal B cells contribute to renal rejection and find a
population of innate B cells reactive to renal-specific or
inflammation-associated antigens.
Introduction
In germinal centers (GCs), spatial and molecular orchestration of
clonal expansion, somatic hypermutation (SHM), and selection drive
production of high-affinity antibodies and immunological
memory^[52]1,[53]2. In many chronic inflammatory and autoimmune
diseases, GC-like structures form in afflicted organs (tertiary
lymphoid structures, TLSs)^[54]3,[55]4. TLSs are often associated with
hallmarks of antigen-driven B cell selection including local clonal
expansion and SHM. However, in most cases the antigens driving in situ
B cell selection in TLSs are not known. Furthermore, early in the
course of inflammation infiltrating T and B cells are not usually
organized into histologically obvious TLSs. Indeed, diffuse lymphocytic
infiltrates and T:B aggregates are more common than TLSs in most
diseases^[56]5–[57]9. It remains unclear if local antigens shape in
situ lymphocyte repertoire in these disease states. Therefore, our
fundamental understanding of in situ adaptive immunity, in both acute
and chronic inflammation, is incomplete.
An example of inflammation and progressive organ dysfunction is
provided by renal allograft rejection. Acute and early chronic
rejection is associated with disorganized lymphocytic infiltrates or
T:B cell aggregates while progression to end-stage rejection can be
associated with TLSs^[58]10,[59]11. B cell infiltrates appear important
in acute rejection as they predict poor graft survival^[60]12–[61]15.
Furthermore, in mice and humans, B cell depletion mitigates
rejection^[62]16,[63]17. These observations suggest an important role
for in situ adaptive immunity, and infiltrating B cells, in allograft
rejection.
One clear pathogenic function of B cells is the secretion of
donor-specific antibodies (DSAs) that recognize donor human leukocyte
antigen (HLA). Serum DSAs strongly predict early onset of allograft
rejection^[64]18–[65]20. The source of these DSAs is not known. A study
of a single infected end-stage kidney explant suggested infiltrating B
cell infrequently expressed DSAs^[66]21. It is not known if
infiltrating B cells express DSAs in ongoing rejection.
In both mice and humans, renal transplant rejection can be associated
with loss of tolerance and serum antibodies to self-antigens^[67]22.
However, it is not known how and where tolerance to self is broken in
allograft recipients who do not have an underlying autoimmune disease.
In mice, activation of innate immune pathways is sufficient to break B
cell tolerance^[68]23–[69]26. However, it is not clear how this
paradigm applies to humans. Therefore, in ongoing renal allograft
rejection, the antigens driving in situ B cell selection, and the
magnitude of that selection, remain unknown.
Herein, using single-cell RNA sequencing (scRNA-seq) we report that in
allograft rejection, intrarenal B cells have a unique transcriptional
state that resembles mouse B1 innate-like (Bin) cells. Bin cells are
not a source of DSAs. Bin cells generate renal or inflammation-specific
antibodies and can give rise to plasma cells selected by local
antigens. These results demonstrate how intrarenal B cells drive local
inflammation in allograft rejection and provide an example of how
inflammation can give rise to organ-restricted autoimmunity.
Results
Distinct transcriptional states in activated intrarenal and tonsil B cells
We first sorted CD45^+ DAPI^- Calcein^+ CD19^+ CD38^+ activated B cells
from five renal allograft biopsies and four tonsillectomy samples
(Fig. [70]1a and Supplementary Fig. [71]1a). A paired biopsy from each
renal allograft patient was reviewed by a blinded renal pathologist.
The presence of B cells and C4d deposition was examined by
immunohistochemistry and serum assayed for DSAs. All biopsies displayed
diffuse infiltrates and/or lymphocyte aggregates without TLS.
Furthermore, all five biopsies had features of either chronic or
chronic and active antibody-mediated rejection (AMR). Pathological
features, including Banff scores^[72]27, clinical characteristics and
follow-up for each patient are provided in Supplementary Table [73]1.
Fig. 1. Sorting and scRNA-seq of activated B cells in renal allograft and
tonsil.
[74]Fig. 1
[75]Open in a new tab
a Gating scheme for single-cell sorting of CD19^+ CD38^+ activated B
cells in renal allograft and tonsil samples. b A t-SNE plot of
scRNA-seq. Color and shape respectively indicate patients and tissue
sources from which cells were derived.
Sorted B cells were then subjected to scRNA-seq (Smart-Seq2)^[76]28. We
excluded cells which had less than 3,000 or more than 15,000 expressed
genes (Supplementary Fig. [77]1b). We removed cells which had low
expression of immunoglobulin (Ig) constant region genes. After quality
control (QC), 655 renal and 129 tonsil B cells were used for subsequent
analyses. Batch effects from separate sequencing runs were normalized
using External RNA Control Consortium (ERCC) spike-in control and
RUVSeq R package^[78]29 (Supplementary Fig. [79]1c, d).
We first mapped sequenced cells onto a t-distributed stochastic
neighbor embedding (t-SNE) space (Fig. [80]1b). Renal B cells formed
one diffuse cluster while tonsil B cells formed two distinct clusters,
one of which overlapped with the kidney cluster and the other that was
distinct. This clustering was not due to batch-associated differences,
suggesting that B cells in renal allograft and tonsil had distinct
transcriptional profiles. Moreover, B cells from all five renal
biopsies were similarly distributed in the t-SNE space suggesting that
renal allograft-infiltrating B cells had a similar transcriptional
profile across patients regardless of their histological or clinical
features.
Intrarenal B cells could be separated based on Ig class switching
(Fig. [81]2a). Here, B cells expressing IgM or IgD as the most highly
expressed Ig isotype were categorized as “unswitched”, and those
expressing either IgG, IgA, or IgE as “switched.” Unswitched cells
composed about 70% of both renal and tonsil B cells, and the remaining
class-switched cells mostly expressed IgG or IgA (Supplementary
Table [82]2). Regardless of Ig class, switched cells from each tissue
source were similarly distributed in the t-SNE space (Fig. [83]2b, c).
The two tonsil B cell clusters were also distinguished by their Ig
class. Unswitched tonsil B cells largely overlapped with unswitched
renal B cells whereas switched tonsil cells formed a distinct cluster.
The differences between clusters were also apparent when examining
Pearson correlation coefficients of high-variant genes (Supplementary
Fig. [84]2a). These clustering differences persisted when Ig constant
region genes were removed (Supplementary Fig. [85]2 b). These data
suggest that B cells in rejected renal allograft and tonsil tissue are
similar prior to class-switch recombination but diverge thereafter.
Fig. 2. Transcriptional state of class-switched intrarenal B cells.
[86]Fig. 2
[87]Open in a new tab
a, b t-SNE plots as in Fig. [88]1b. Color indicates Ig class-switch
state (a) or expressed Ig classes (b). The cells were categorized as
“switched” if their most highly expressed Ig heavy-chain genes were
either IgG, A or E, and categorized as “unswitched” otherwise. c Pie
charts showing the distribution of Ig class in intrarenal and tonsil B
cells. d A heatmap showing hierarchical clustering of the 2,855 DEGs.
Mean expression values were calculated for each four cell populations
based on their tissue source and Ig class-switch state and then
converted to Z-scores. e Enrichment of GO terms and KEGG pathways in
the five gene clusters. At most 10 most significantly enriched pathways
were shown per cluster. f–j Violin plots showing RNA expression of
TNFRSF13B (f), BCL2 (g), BCL6 (h), BACH2 (i), and AICDA (j).
Comparison across tissue sources and Ig class-switch states identified
2,855 differentially expressed genes (DEGs) which could be divided into
six hierarchical clusters (Fig. [89]2d and Supplementary Data [90]1).
Cluster 1 included genes enriched in unswitched tonsil B cells,
clusters 2 and 3 genes enriched in intrarenal cells, cluster 4 genes
enriched in intrarenal and tonsil switched cells, cluster 5 genes
enriched in tonsil switched cells and cluster 6 genes enriched in
tonsil B cells. A pathway enrichment analysis based on Gene Ontology
(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases
revealed specific biological pathways were enriched in most clusters
(Fig. [91]2e).
Many of the GO and KEGG pathways enriched in cluster 2 were related to
innate receptors and signaling pathways including the pattern
recognition receptors NLRP1, NOD1, TLR2, and TLR7 (Supplementary
Table [92]3). Therefore, we next examined if, globally, clusters 2 and
3 were enriched in GO genes termed “innate immune response”. When we
calculated a sum of scaled expression values for these genes,
intrarenal B cells, especially those that were class-switched, had
higher values than tonsil (Supplementary Fig. [93]2c). This enrichment
of innate immune response genes was consistent across all patients
(Supplementary Fig. [94]2d). These data reveal an enrichment for
innate immune response genes in intrarenal B cells.
Clusters 2 and 3 were enriched in interferon (IFN)-related pathways
including NLRC5, IFNAR2, IRF1, and STAT^[95]30. These clusters also
contained several cytokine ligands and receptors: IL15, TNFRSF1B, and
TNFRSF13B (Fig. [96]2f). TNFR13B encodes TACI, a receptor for BAFF
overexpression of which is associated with renal allograft
rejection^[97]31,[98]32.
Consistent with a previous report, the anti-apoptotic factor BCL2 was
enriched in cluster 2 (Fig. [99]2g)^[100]33. Many of the pathways
enriched in cluster 2, including BCL2, TLRs, interferons and cytokines
are directly repressed by BCL6^[101]34. Indeed, the expression of BCL6
was lower in renal B cells (Fig. [102]2h), as well as another
transcriptional repressor BACH2, which shares targets with BCL6^[103]35
(Fig. [104]2i).
BCL6 and BACH2 were preferentially expressed in class-switched tonsil B
cells. These cells were enriched in several pathways that have
previously been ascribed to GC B cells including proliferation and
somatic hypermutation. Notably, AICDA was expressed in class-switched
tonsil B cells but not significantly in other B cell populations
(Fig. [105]2j). These results indicate that intrarenal class-switched B
cells lack the essential transcriptional features of GC B cells.
Neither gene cluster 3 nor 4 demonstrated upregulation of specific GO
pathways. However, examination of individual differentially expressed
genes revealed potentially important differences. Most notable was
AHNAK (Fig. [106]3a). AHNAK mRNA levels were far higher in intrarenal B
cells compared to tonsil regardless of Ig class switch (Fig. [107]3b).
This corresponded to detectable expression of the AHNAK protein in
intrarenal but not tonsil B cells (Fig. [108]3c). Interestingly, within
mouse B cell subsets, Ahnak is preferentially expressed in peritoneal
cavity B1a and B1b cells (Immgen, Fig. [109]3d)^[110]36. This
expression pattern is shared with murine homologues of several other
cluster 3 genes, such as ITGAM and VIM (Supplementary Fig. [111]2e, f).
Therefore, we examined whether cluster 3 was enriched for genes having
an AHNAK covariant expression pattern.
Fig. 3. Intrarenal B cells have an innate-like gene signature.
[112]Fig. 3
[113]Open in a new tab
a A volcano plot showing DEGs between Ig class-switched intrarenal and
tonsil B cells. Genes expressed higher in intrarenal B cells are shown
on the right side of the plot. b A violin plot demonstrating RNA
expression of AHNAK. c Staining images of AHNAK with nuclei (Hoechst)
and a B-cell marker CD19 in rejected renal allograft and tonsil. The
high-magnification panel corresponds to the yellow square on the merged
panel. Scale bars indicate 50 μm or 25 μm (high-magnification panel).
Staining has been tested on tissues from two patients and a
representative result is shown. d Expression of 333 murine genes that
correlate with Ahnak in Immgen. The mean value of the 333
Ahnak-covariant genes (including Ahnak itself) is shown as the black
line with the gray shade indicating standard deviation. Expression of
Ahnak is the red line. T: transitional, Fo: follicular, GC: germinal
center, MZ: marginal zone, Sp: spleen, and PC: peritoneal cavity. e
Enrichment of GO terms and KEGG pathways in the 293 AHNAK-covariant
genes. At most 10 most significantly enriched pathways are shown. f
Enrichment of the AHNAK-covariant genes in each gene cluster from
Fig. [114]2d. g A heatmap showing DGE scores, a sum of scaled
expression levels of each gene cluster within each murine B cell subset
in Immgen data. Each row and column represents the gene clusters found
in Fig. [115]2d and the murine B cell subpopulations. DEG scores were
scaled by row to obtain Z-scores.
We identified 333 mouse genes whose expression pattern in peripheral B
cell populations was similar to Ahnak (correlation coefficient ≥ 0.8)
(Fig. [116]3d and Supplementary Data [117]2). The Ahnak-covariant
murine genes corresponded to 293 human homologues. These human genes
were enriched for cell adhesion and lymphocyte activation pathways, as
well as an innate immune pathway related to lipopolysaccharide
responses (Fig. [118]3e). AHNAK-covariant genes were highly enriched in
cluster 3 and to a lesser degree in clusters 2 and 4 (Fig. [119]3f).
Furthermore, these differences were not solely dependent upon AHNAK as
they persisted when AHNAK was removed (Supplementary Fig. [120]2g).
These results suggest that AHNAK-covariant genes are a signature of
intrarenal B cell activation.
Although the AHNAK-covariant genes were enriched in cluster 3, they
represented only 5% of the cluster. Therefore, we next tested if
cluster 3 was generally enriched for peritoneal cavity B1
cell-associated genes. First, we converted 2,855 differentially
expressed human genes to their murine orthologs. Then, in the Immgen
data, we compared murine B cell subsets for the expression of genes in
each human gene cluster. For each gene cluster, we calculated a sum of
gene expression values scaled across the B cell subsets. This analysis
demonstrated that genes in cluster 3 were preferentially expressed in
murine peritoneal cavity B1 cells (Fig. [121]3g, Supplementary
Table [122]3). These results suggest that cluster 3, containing genes
highly expressed in the graft-infiltrating B cells, has a gene
signature of peritoneal B1 cells. We refer to these unique human innate
B cells as Bin cells.
Double negative (CD27^-IgD^-, DN) B cells have been identified in
tissue inflammation including lupus nephritis^[123]37. They are
characterized by increased expression of TBX21 (T-bet), ITGAX (CD11c),
TLR2, and TLR7. Although both TLR2 and TLR7 were preferentially
expressed by intra-graft B cells, we found no difference between
intrarenal and tonsil B cells in the overall expression of 26 genes
associated with DN cells (Supplementary Fig. [124]3a)^[125]37,[126]38.
We also examined the HIF-1 pathway, which is upregulated in situ in
both human and murine lupus nephritis^[127]39,[128]40. However,
HIF-family genes were not upregulated in intrarenal B cells compared
with tonsil B cells (Supplementary Fig. [129]3b–f). Overall, these data
demonstrate that class-switched intrarenal B cells in allograft
rejection have a unique transcriptional profile reminiscent of innate
B1 cells.
Consistent with this innate-like transcriptional phenotype,
class-switched intrarenal B cells also preferentially expressed the
innate cytokine IL-15 (Fig. [130]4a). To assess if differential mRNA
expression was reflected in the protein abundance, we stained tonsil
and renal allograft rejection tissue with anti-IL15 antibodies as well
as antibodies specific for the IL-15 receptor chain, IL-15RA. There was
no detectable IL-15 expression in tonsil germinal centers whereas
expression was readily detected in infiltrating B and other immune
cells in rejected renal allografts (Fig. [131]4b). IL-15RA was
moderately expressed in both tonsil and renal graft tissue. However, it
was more abundant in renal tubular cells than infiltrating immune
cells, suggesting that IL-15 secreted by B cells might be captured by
tubular cells for presentation to immune cells^[132]41.
Fig. 4. Differential expression of innate and localization receptors.
[133]Fig. 4
[134]Open in a new tab
a A violin plot showing the expression level of IL15. Cells were
grouped by their tissue source and color indicates Ig class-switch
state. b Staining images of nuclei (Hoechst), CD19, IL-15, and IL-15RA
in rejected renal allograft and tonsil tissues. The merged CD19/IL-15
panels were a magnification of the yellow box in merge panels. Scale
bars indicate 100 μm. Staining has been tested on tissues from two
patients and a representative result is shown. c Violin plots of
indicated genes that were differentially expressed in intrarenal B
cells compared to class-switched tonsil B cells.
Compared to Ig switched tonsil cells, intrarenal B cells differentially
expressed several migration and adhesion-related genes including CD24,
CD44, CD55, ITGA4, COL4A4, CCR6, VIM, SIPR1, and SIPR2 (Fig. [135]4c).
The differential expression of these molecules suggests that intrarenal
B cells respond to different localization signals than GC B cells. For
example, S1PR1 controls B cell egress from lymphoid organs while S1PR2
coordinates GC B cell migration^[136]42,[137]43. The relatively high
expression of S1PR1 and low expression of S1PR2, which is the inverse
of what is observed in GCs, is consistent with fundamentally different
mechanisms of retention in inflamed renal tissue.
Serum DSAs are not predictive of intrarenal B cell phenotype
In renal allograft rejection, the presence of serum DSAs predicts a
worse clinical outcome^[138]18–[139]20. We examined if the presence of
serum DSAs was reflected in differences in the transcriptional programs
of intrarenal B cells. Since our initial cohort had only one
DSA-positive patient, we obtained three additional renal biopsies (two
from DSA-positive patients and one from a DSA-negative patient) as well
as three additional tonsillectomy samples. From these samples,
activated B cells were sorted and subjected to scRNA-seq (Supplementary
Table [140]1).
Across the integrated dataset, seven of eight biopsies demonstrated
mild or moderate interstitial fibrosis and tubular atrophy
(Supplementary Table [141]1). All had chronic and/or active AMR. Only
one patient rapidly progressed to renal failure within a month of
biopsy. Therefore, the biopsies were comparable and largely reflected
rejection in functioning, and not end-stage, renal grafts.
After filtering for both gene coverage and Ig expression, we obtained
an additional 513 cells to integrate with those of the first cohort
(Supplementary Fig. [142]4a). The second cohort had a lower sequence
coverage compared to the first cohort (Supplementary Fig. [143]4b).
Since normalizing scRNA-seq data with different sequencing depths by a
single-scaling factor (e.g. total mapped reads) could introduce bias,
we first normalized our data by SCTransform implemented in
Seurat^[144]44–[145]46. The data were then integrated by ComBat^[146]47
to negate batch effects between cohorts (Supplementary Fig. [147]4c,
d).
The resulting integrated data had a similar t-SNE projection to that of
the first dataset (Fig. [148]5a). As in the previous analysis, class
switched B cells were separate whereas unswitched cells clustered
together (Fig. [149]5b). Pearson correlation coefficients for
high-variant genes confirmed the relative population differences
observed in t-SNE plots (Supplementary Fig. [150]4e). Also, Pearson
correlation analysis between the integrated cohort and the first cohort
of five patients revealed strong similarities between comparable
populations (Supplementary Fig. [151]4f). These data reveal clear and
consistent differences between B cell populations across cohorts.
Fig. 5. B cells are similar between serum DSA-positive and negative patients.
[152]Fig. 5
[153]Open in a new tab
a–e t-SNE plots of the integrated data of the two cohorts. Shape
indicates tissue source, and color indicates patients (a), Ig
class-switch state (b), PRDM1 expression (c), clusters assigned by
Seurat (d), and serum DSA positivity (e). f Volcano plots showing
differential gene expression in non-plasma B cells, comparing
DSA-positive and negative patients. Shown on the right side of the
plots are genes upregulated in B cells from DSA-positive patients.
Genes above the significance threshold were colored in red, and names
of top-hit genes were labeled.
In addition to the cell populations observed in the first patient
cohort, there was a new discrete cluster which highly expressed PRDM1
indicative of plasma cells (Fig. [154]5c, d). This population was
mostly derived from kidney patient 6 and, to a lesser degree, patient
7. A few plasma cells were also detected in biopsies from patients 2,
5, and 8. Notably, plasma cells were observed in biopsies from two
DSA-positive and three DSA-negative patients. Therefore, intrarenal
plasma cells were not a unique feature of DSA-positive patients.
The other B cell populations did not cluster depending on patients’
serum DSA positivity (Fig. [155]5e). Comparing gene expression of the
non-plasma cells from the DSA-positive and DSA-negative patients, we
could identify only three genes differentially expressed
(Fig. [156]5f). These results suggest that there are no substantial
transcriptional differences in graft-infiltrating B cells from
DSA-positive and DSA-negative patients.
In situ immunoglobulin repertoire in renal allograft rejection
A central question is whether intra-graft B cells are selected for
alloreactivity. Therefore, we first used nested polymerase chain
reaction (PCR) to amplify immunoglobulin gene variable regions from the
cDNA of single B cells isolated from seven kidney biopsies and two
tonsil samples^[157]48. Obtained sequences were aligned to IMGT
reference using IMGT/HighV-QUEST and analyzed for somatic mutations. In
total, we identified full-length Ig heavy chain variable regions from
457 B cells in seven rejection patients and 77 in two tonsillectomy
patients (Table [158]1)^[159]49,[160]50. Overall, immunoglobulin
mutation burden was similar between rejection and tonsillectomy samples
(Fig. [161]6a). This was true for both B cells expressing unswitched
and switched immunoglobulin genes. In general, unswitched B cells had
less of a mutation burden than switched cells. However, in intrarenal B
cells there were a small fraction of both unswitched and switched cells
that had very high (>60) frequencies of mutations.
Table 1.
Distribution of sequenced antibody heavy chains across patients.
Kidney 1 Kidney 2 Kidney 3 Kidney 4 Kidney 5 Kidney 6 Kidney 7 Tonsil 1
Tonsil 2 Total
Switched 11 42 31 7 28 92 17 7 20 255
Unswitched 5 90 23 51 41 11 8 32 18 279
Total sequenced mAb 16 132 54 58 69 103 25 39 38 534
Expanded clonal families 0 1 2 1 0 15 2 0 0 21
[162]Open in a new tab
Class-switching states and the number of expanded clonal families in
sequenced antibody heavy chains are shown.
Fig. 6. Antibodies generated by intrarenal B cells were not selected for
allo-HLA reactivity.
[163]Fig. 6
[164]Open in a new tab
a A violin plot showing distribution of mutations in the variable
region of immunoglobulin heavy chains, grouped by tissue source and the
Ig class-switch state. b t-SNE plots as in Fig. 6, with data points
labeled with their clonal family. “-“ means antibody genes were not
sequenced; “0” means the cells did not share their clonotype with
others; and 1-22 indicate that the cells shared a clonotype with other
B cells with the same number. c A violin plot showing distribution of
mutations in recombinantly expressed antibodies, grouped by tissue
source and Ig class-switch state. d A heatmap showing the HLA-binding
assay result. Each column represents each antibody, and maximum NBG
ratio within class-1 or class-2 beads are shown. The header label
indicates patients from whom the antibodies were derived, serum DSA
positivity, and whether the observed reactivity was above the
positivity threshold. e A plot showing the change in HLA binding when
the antibodies were tested in negative control human serum. All the
positive intrarenal antibodies in (d) were tested, and data points of
the same antibodies were connected by lines.
Next, we assessed clonal relationships among the sequenced antibodies.
We tested whether the B cells were clonally related, defined as sharing
the same variable (V), diversity (D), joining (J) segments and
complementarity-determining region (CDR) 3 length. We found only a
limited number of shared clonal families in most patients
(Fig. [165]6b). In contrast, many of the plasma cells were clonally
related, comprising 15 clonal families in patient 6, and 2 clonal
families in patient 7.
Many of the clones identified in plasma cells were also present in the
corresponding B cell populations. This was true for eight clones in
patient 6 and one clone in patient 7. The clonal relatedness between
Bin cells and plasma cells indicates that local self-antigens can drive
in situ selection and differentiation into antibody secreting cells.
In order to characterize their immunoreactivity, we next expressed the
cloned immunoglobulin genes as recombinant antibodies with a FLAG tag
at the heavy-chain C-terminus^[166]51. In total, we expressed 105
antibodies (74 IgG/A and 31 IgM) from intrarenal B cells isolated from
7 patients (Table [167]2, Supplementary Data [168]3). This included 11
out of the 21 expanded IgG plasma cell clones. We also expressed 19
antibodies from the two tonsillectomy patients (9 IgG and 10 IgM). As
expected, the mutation rate was higher in IgG/IgA tonsil antibodies
than IgM (Fig. [169]6c).
Table 2.
Isotypes of recombinantly produced antibodies.
Kidney 1 Kidney 2 Kidney 3 Kidney 4 Kidney 5 Kidney 6 Kidney 7 Tonsil 1
Tonsil 2 Total
IgG 5 13 10 4 3 21 4 2 7 69
IgA 2 2 5 2 2 0 1 0 0 14
IgM 1 6 6 8 3 0 7 5 5 41
Total 8 21 21 14 8 21 12 7 12 124
[170]Open in a new tab
In situ Bin cells are not selected for alloreactivity
We first tested reactivity of in situ expressed antibodies to HLA
antigens using a Luminex-based assay in which beads were coated with a
mixture of HLA class-I or class-II antigens^[171]52. Antibody binding
was evaluated by the normalized background (NBG) ratio, which is
fold-increase binding over negative control serum. Binding was positive
if it was equal to or higher than 2.2. When diluted in
phosphate-buffered saline (PBS), 17% (18/105) bound to screening HLA
beads (Fig. [172]6d). In serum DSA-positive patients, 9 of 47 (19%)
expressed monoclonal antibodies (mAbs) were HLA-binding, while 16%
(9/58) of mAbs from DSA-negative patients bound HLA (p = 0.82,
chi-squared test). However, we observed 21% (4/19) positivity in tonsil
antibodies. Collectively, these data suggested that HLA reactivity was
not a specific feature of in situ expressed antibodies from
DSA-positive patients.
To further delineate the specificity of HLA binding, mAbs that bound to
the screening HLA beads were tested on single HLA-antigen beads (SAB).
Out of 18 tested antibodies, 17 antibodies showed trimmed mean
fluorescent intensity (MFI) higher than 1,000. Unexpectedly, 15 of the
17 antibodies bound to HLA-C, with HLA-Cw*06:02 being most common
(Supplementary Fig. [173]5a and Supplementary Data [174]4).
Furthermore, 16 of 17 antibodies bound to multiple HLA antigens. These
HLA alleles recognized by the mAbs were not donor-specific HLAs.
Indeed, except for 6-2D3, all bound most strongly to
recipient-expressed HLAs (Supplementary Fig. [175]5b).
We next examined whether shared eplets (discrete epitopes) between
multiple HLA alleles could explain the unexpected broad HLA
specificity^[176]53. However, 6-2D3 mAb had reactivity with several
HLA-A and C antigens (Supplementary Fig. [177]5c) that did not share
any common eplets. Indeed, the majority of mAbs (11 out of 17) that
bound multiple HLA Class I alleles did not share an eplet among all
their top-10 hits (Supplementary Fig. [178]5d). For the remaining 6
mAbs, the top-10 hits did share an eplet for bound HLA-C antigens
(Supplementary Fig. [179]5e and Supplementary
Table [180]4)^[181]54–[182]56. Thus, shared eplets could not explain
the broad reactivity of most mAbs.
Another possibility is that the broad reactivity of our mAbs represents
low-affinity polyreactivity. If so, adding a non-specific blocking
reagent should abrogate binding^[183]57. Therefore, we next retested
binding of the above antibodies to the screening HLA beads in the
presence of negative control human serum. Strikingly, in the presence
of serum, all antibodies lost their binding to HLA antigens
(Fig. [184]6e). In toto, our results indicate that allo-reactive
antibodies are not commonly selected in situ during acute renal
allograft rejection.
Polyreactivity can be associated with autoreactivity^[185]58.
Therefore, we assayed the binding of the mAbs to human epithelial
type-2 (HEp-2) cells by immunofluorescence microscopy. For the mAbs
generated from non-plasma Bin cells, the frequency of HEp-2-reactive
clones was 18% (15/84) (Fig. [186]7a), which is similar to that
reported for naive or tonsil GC repertoires^[187]59,[188]60. HEp-2
reactivity was slightly more common in HLA cross-reactive antibodies
(27%, 4/15 vs. 16%, 11/69), but this difference was not statistically
significant (p = 0.54, chi-squared test). These results suggest these
antibodies are not selected for reactivity to ubiquitous self-antigens.
Fig. 7. Plasma cells produce antibodies specific for nucleolar proteins
including Ki-67.
[189]Fig. 7
[190]Open in a new tab
a Pie charts showing the frequency of HEp-2-reactive antibodies in
indicated patients. b, c HEp-2 staining images of the three antibodies
used for the IP/mass spectrometry (b) and their top 10 preferentially
bound antigens (c). Log2 fold changes of signal intensity compared with
the mean of the two negative control antibodies are shown. The scale
bar indicates 50 µm. d Staining images showing signal colocalization
between the antinucleolar antibodies and a commercial anti-Ki-67
antibody on human tonsil tissue. Scale bars indicate 50 μm. For (b, d),
staining has been repeated twice and a representative result is shown.
In marked contrast, when we examined mAbs from clonally expanded plasma
cells from patient 6, 76% of (16/21) antibodies had HEp-2 reactivity
(Fig. [191]7a and Supplementary Fig. [192]6a). Reactivity was broadly
distributed across eight different clonal families with many binding
the nucleolus (Fig. [193]7b and Supplementary Fig. [194]6a). Those
multiple clones had remarkably similar patterns on staining suggested
that different in situ clonally expanded plasma cells were targeting
the same or similar antigens.
In situ selection by organ-restricted or inflammation-associated antigens
To identify potential antigens, we selected three antinuclear
antibodies (6-2A4, 6-2B9, and 6-1B5) from different clonal families
(Fig. [195]7b): 6-A4 represented the most expanded clonal family; 6-2B9
showed the strongest signal in HEp-2 staining; and 6-1B5 showed the
most specific nucleoli binding. Of the three antibodies tested, 6-2A4
and 6-2B9 showed similar broad immunoreactivity to HEp-2 nuclear
antigens with relative molecular weight >50 kDa (Supplementary Fig.
[196]6b). The 6-1B5 mAb did not detectably bind.
Next, we performed immunoprecipitations (IP) with the three mAbs from
the lysates of HEp-2 nuclear fractions. Two negative control mAbs were
included: 7-1A5 and 7-1E3 which did not bind to HEp-2 cells.
Immunoprecipitations were resolved by sodium dodecyl
sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and regions above
IgG heavy chain (> 50 kDa) were excised and subjected to tandem mass
spectrometry. Detected peptides were mapped to the human proteome
according to the UniProt database^[197]61. Fold changes of signal
intensity (normalized to median) were calculated for the three
antinucleolar antibodies over the mean intensity of the two negative
control antibodies.
For two of the three positive mAbs tested (6-2A4 and 6-2B9), the
nucleolar antigen Ki-67 was the top hit (Fig. [198]7c). For the other
antibody, 6-1B5, another nucleolar antigen HEATR1 was the top hit.
Interestingly, HEATR1 was also found in the top 10 hits of 6-2B9, and
thus it is possible that these antibodies were selected for the same
protein complex.
We next examined if 6-2A4 and 6-2B9 directly recognized Ki-67. As Ki-67
is large (3,256 amino acids), we expressed seven recombinant fragments
in total covering the whole length of Ki-67 in E. coli. Whole bacterial
lysates were resolved by SDS-PAGE and tested for antibody binding by
western blot. A commercial anti-Ki67 antibody preferentially bound
fragment 3 (aa 994-1489) with less binding to other fragments
(Supplementary Fig. [199]6 c). In contrast, both 6-2A4 and 6-2B9 bound
to fragment 6 (aa 2446-2940) with 6-2A4 also binding to fragment 7 (aa
2927-3256). These data demonstrate that both in situ selected
antibodies directly bound similar Ki-67 domains.
Since Ki-67 was detected by two independent clones, we searched for
other anti-Ki-67 antibodies by staining tonsil tissue. We detected a
total of six antibodies that showed colocalization with Ki-67,
including 6-2A4 and 6-2B9 (Fig. [200]7d). These additional four
antibodies belonged to the same clonotypes as either 6-2A4 or 6-2B9
indicating their clonal selection for Ki-67 reactivity. These results
suggest that breach of self-tolerance and strong selection for
self-antigens can occur in the kidney of renal allografts during
rejection.
The clonal relatedness between B cells and plasma cells in patient 6
indicated that self-reactivity can occur in the local B cell pool.
Furthermore, these clonally related B cell precursors expressed highly
mutated antibodies suggesting that selection might occur before
detectable clonal expansion. We postulated that these putative
selecting antigens would be locally expressed in the inflamed kidney.
Therefore, we expressed 28 representative highly mutated antibodies
expressed by intrarenal B cells from patients 1–5 and 7 (4–5 antibodies
per patient) (Supplementary Data [201]3). We then assessed if they
bound inflamed allograft or normal kidney. The epitope-tagged
immunoglobulin heavy chains allowed us to discern between monoclonal
antibody binding and endogenous immune complexes.
Out of 28 tested antibodies, six antibodies, from five different
patients, showed detectable binding to inflamed allograft renal tissue
(Fig. [202]8). All antibodies showed specific distributions of staining
with nuclear or perinuclear binding being common. However, only 4-2C3
showed nearly ubiquitous staining with the other antibodies only
binding some cell types, often tubules. These observations suggest
selection by specific intrarenal expressed antigens.
Fig. 8. Bin cells generate IgG antibodies that bind renal antigens.
Fig. 8
[203]Open in a new tab
Indicated Flag-tagged antibodies were used to probe inflamed allograft
or normal renal tissue. Antibodies were also assayed for HEp-2
immunoreactivity. Images are representative (n = 2). The white scale
bar for tissue and red scale bar for HEp-2 cells both indicate 50 µm.
Four of the six antibodies (3-1E12, 4-2E2, 5-1C3, and 7-1B2) were
HEp-2-reactive with three having predominately cytoplasmic staining.
However, none of the HEp-2 patterns were predictive of binding to renal
tissue. This was striking for 3-1E12 which bound specifically to a
subset of renal cells yet gave a diffuse cytoplasmic HEp-2 pattern, and
4-2C3 which bound renal nuclei diffusely yet was HEp-2 negative. These
data indicate that the HEp-2 could be misleading in evaluating
tissue-specific autoantibodies. Furthermore, these data are consistent
with in situ immunity directed against renal, and non-ubiquitous,
expressed antigens.
We next examined if targeted antigens were preferentially expressed in
inflamed kidney. Indeed, three antibodies (4-2E2, 5-1C3, and 7-1B2) did
not bind normal kidney. However, the other three antibodies
demonstrated similar binding patterns in inflamed and normal renal
tissue. Collectively, these data suggest a broad loss of tolerance in
the intrarenal B cell compartment and selection by renal-restricted or
inflammation-associated antigens.
Discussion
Herein, we demonstrate that in allograft rejection, intrarenal B cells
have a unique gene expression profile that most closely resembles the
B1 innate B cells that have been described in mice but not in
humans^[204]62,[205]63. In contrast to murine innate B cell
populations, which are constitutively present, Bin cells were
associated with inflammation and overall transcriptional differences
were most prominent in those cells expressing mutated IgG
antibodies^[206]62. These data suggest that in humans, B cell
innate-like features are induced by specific pathways of activation and
selection.
Remarkably, within those infiltrating Bin cells expressing highly
mutated class-switched antibodies, specificity for organ and
inflammation-restricted antigens was frequent. This was observed across
several patients who had no history of autoimmune disease. In contrast
to ubiquitous self-antigens, central tolerance does not necessarily
deplete the B cell repertoire of sequestered or peripherally restricted
antigen reactivity^[207]64–[208]69. Furthermore, organ-restricted
specificities that arise during the germinal center response are not
efficiently eliminated^[209]70. Rather, such specificities are
tolerized by mechanisms, including anergy, which allow for persistence
in the periphery^[210]71. In mice, it is clear that innate signals and
patterns are sufficient to subvert peripheral tolerance and induce
systemic autoimmunity^[211]23–[212]26. Herein, we demonstrate that in
human allograft rejection such breaches in tolerance can occur locally
at inflammatory sites rich in innate signaling networks.
One of the major differences between B cell populations in humans
compared to mice is the lack of cells with an innate B1 cell
transcriptional signature. While innate-like functions have been
ascribed to human B cell subpopulations, extensive scRNA-seq has failed
to confirm their existence^[213]63,[214]72. Indeed, it was difficult to
map Bin cells to known populations of human B cells. Rather, to
characterize these cells we relied on transcriptional profiles of
murine B1 innate cells.
Using this approach, we identified AHNAK and AHNAK-covariant genes as a
defining signature of Bin cells. While its role in B cells is unclear,
AHNAK has diverse functions including mediating T-cell-receptor induced
intracellular calcium mobilization^[215]73. It also modulates
TGF-β/Smad signaling, which is important for several B cell
functions^[216]74,[217]75.
The proinflammatory cytokine IL-15 was expressed by Bin cells. A
component of its receptor complex, IL-15RA, was also expressed in
rejected renal allograft tissue. IL-15 can act directly or via
trans-presentation by IL-15RA^[218]76. The abundance of IL-15 in
rejected renal allografts has been long known and antagonization of
IL-15 improves graft survival^[219]77,[220]78. However, this is the
first report to suggest that intrarenal B cells are a significant
source of IL-15 in allograft rejection.
Bin cells also expressed the type-I IFN signature^[221]79,[222]80.
IFN-α treatment can induce rejection of renal allograft and the IFN
pathway is upregulated in rejecting renal allografts^[223]81,[224]82.
IFN signaling induces TLR expression and indeed, TLR2 and TLR7 were
upregulated in Bin cells. In mice, recipient expression of TLR2 and
TLR4 is critical for renal allograft rejection^[225]83. The IFN pathway
likely reflects activation mechanisms independent of the AHNAK program
as there was not a correlation between the IFN and AHNAK signatures in
single Bin cells. Therefore, multiple activation pathways likely
contribute to the molecular state of intrarenal Bin cells.
Renal allograft rejection is associated with the presence of serum
DSAs. However, despite an extensive analysis, we could not identify
even one Bin cell that expressed antibodies specific for HLA, be they
donor-specific or otherwise. This indicates that B cells expressing
DSAs are rarely selected for in the kidney^[226]21. Rather, it is
likely that DSAs are a manifestation of systemic allo-immunity
associated with endovascular immune complex deposits^[227]18.
Bin cells expressing organ-specific antibodies were observed in the
absence of discernable clonal expansion. This suggests some aspects of
selection might occur peripherally with Bin cells then homing to the
inflamed organ. Alternatively, selection could be entirely local and
our inability to detect clonality reflects the small sample size
intrinsic to clinical samples. Regardless, our data demonstrate that
observing clonal expansion is an insensitive surrogate for local
antigen selection.
In the two patients in which we captured substantial plasma cell
populations, there was clonal relatedness with Bin cells indicating
that local antigens can drive both selection and differentiation in
situ. Extensive characterization of multiple plasma cell antibodies
from one patient (patient 6) revealed that they bound nucleolar
antigens with two, from different clonal trees, binding directly to
Ki-67. Ki-67 is associated with cell proliferation and therefore would
be preferentially expressed in inflamed but not normal kidney in which
the vast majority of cells are quiescent.
Serum autoantibodies are associated with allograft rejection. Many of
the targeted antigens are cryptic and only exposed after
ischemia-reperfusion injury^[228]22. These serum autoantibodies can
mediate vascular injury and accelerate graft rejection^[229]84.
However, they are present prior to transplantation and therefore are
likely natural antibodies^[230]85. In contrast, intrarenal Bin cells
expressed highly mutated IgG autoantibodies that did not bind vascular
endothelium. Therefore, both natural and acquired B cell autoimmunity,
targeting different antigens and compartments, are features of renal
allograft rejection.
While renal reactive B cells were both present and selected in the
kidney, they only constituted a minority of all infiltrating B cells.
Most in situ B cells expressed unmutated or pauci-mutated IgM
antibodies and therefore are unlikely to have been strongly selected by
antigen. These cells could reflect non-specific trapping of
cells^[231]86. Another possibility is that there is preferential
selection for B cells expressing low-affinity anti-renal allograft
antibodies. Consistent with this, Bin cells expressed CCR6 which is a
marker of low-affinity GC memory B cell precursors^[232]87.
In summary, we demonstrate that in renal allograft rejection,
infiltrating B cells have a unique transcriptional state that suggests
that they are driven by, and likely contribute to, specific innate
signaling pathways and networks. Furthermore, our observations, and
studies in mice, suggest that this innate state of activation permits
the breaking of peripheral tolerance to organ-restricted antigens and
molecular patterns of inflammation^[233]23–[234]26. Because of their
inflammatory state, and the specificity of the antibodies they express,
Bin cells bridge and integrate innate and adaptive immunity to drive
local inflammation.
Methods
Clinical sample collection
The protocol for patient sample collection has been approved by
Institutional Review Board at the University of Chicago. All the
patients signed written informed consent. Kidney biopsies were
performed as an additional biopsy core from consenting patients. The
presence of antibody-mediated rejection was clinically confirmed for
all the sequenced transplant patients. Tonsil samples were deidentified
and collected from tonsillectomy cases. All the clinical samples were
collected on the day of biopsy at the University of Chicago Hospital,
and approved by the Internal Review Board at the University of Chicago.
Cell sorting
Within 5 h after collection, tissues were minced and digested with
Liberase TL (Sigma-Aldrich, 5401020001) for 15 min at 37 °C. Cells were
washed and stained for 30 min at 4 °C with Calcein AM (ThermoFisher
Scientific, 65-0853-78) and antibodies: PE-CD19 (ThermoFisher
Scientific, clone: SJ25C1, 12-0198-42), APC-CD38 (BD Biosciences,
clone: HIT2, 555462), PE-Cy7-CD45 (ThermoFisher Scientific, clone:
HI30, 25-0459-42). Stained cells were washed, and DAPI (Thermo Fisher
Scientific, D1306) was added to the single-cell suspension immediately
before the samples were subjected to BD FACSAria Fusion with FACSDiva
8.0.1 for sorting. Doublets were excluded by FSC-A/FSC-H gating, and
CD45 + Calcein+ DAPI- CD19 + CD38 + activated B cells were single-cell
sorted into 96-well plates with catching buffer (RLT lysis buffer
(Qiagen, 79216) with 1% 2-mercaptoethanol (Sigma-Aldrich,
63689-25ML-F)). Sorted cells were immediately spun down and stored at
-80 °C until being processed for scRNA-seq. Data were analyzed with
FlowJo 10.7.1.
scRNA-seq
scRNA-seq was performed following Smart-seq 2 protocol^[235]28. mRNA
was purified from sorted cell lysates using SPRI beads (Beckman
Coulter, A63987), and reverse transcribed to cDNA with ERCC spike-in
controls (Thermo Fisher Scientific, 4456740). cDNA was amplified for 20
cycles using KAPA HiFi HotStart ReadyMix PCR Kit (Kapa Biosystems,
KK2602). Aliquots of the amplified cDNA were also used for antibody
cloning later. cDNA library was generated using Nextera XT DNA Library
Preparation Kit (Illumina, FC-131-1096), pooled and sequenced with
Illumina sequencer. Data were analyzed with Python 3.7.1 and R 3.6.3 as
detailed below.
Read alignment, quality control, and data integration
For mapping sequencing reads, a human transcriptome (GRCh38) was
obtained from the Ensembl database. Low-complexity regions were masked
from the transcriptome using RepeatMasker 4.1.0 with “-noint -norna
-qq” options^[236]88. The masked transcriptome was used for
pseudoalignment by kallisto 0.46.1^[237]89. For the first cohort,
poor-quality cells were excluded from the analyses if they were
expressing less than 3,000 genes or more than 15,000 genes.
Furthermore, to exclude cells which could be non-B cells, cells were
filtered out if a sum of log-count per million (cpm) of immunoglobulin
heavy chain constant region genes was below 5. Batch effects were
corrected by normalizing counts to ERCC using the RUVSeq 1.16.1 with
“k = 2” option^[238]29. For the second cohort, read alignment and QC
was done in the same manner except that 1,000 genes were used for gene
count cut off. In order to normalize the difference in sequencing
depths between the first and second cohorts, SCTransform in Seurat R
package 3.1.1 was applied^[239]45,[240]46. The normalized data were
further processed by ComBat in sva R package 3.32.1 to remove batch
effects between the two cohorts^[241]47.
t-SNE projection and cell cluster assignment for the differential gene
expression analysis
Gene expression similarity among single cells was visualized by t-SNE
plots, whose coordinates were calculated by Rtsne package 0.15.
Expressed Ig isotypes were identified from the scRNA-seq data by
assigning the most highly expressed Ig constant region gene. Cells were
categorized as “unswitched” if their isotype was IgM or IgD, and
categorized as “switched” otherwise. For the first cohort, the
ERCC-normalized data were scaled by log2 cpm before making t-SNE plots.
For the integrated data, clusters were assigned by Seurat, and the
plasma cell cluster was identified by PRDM1 expression. Plasma cells
were removed from differential expression analyses.
Differential gene expression analysis
Differential expression was tested on genes expressed by at least 10%
of each category to be compared. For the first cohort, raw pseudocounts
were rounded and subjected to edgeR 3.26.8 with unwanted variables
calculated by RUVSeq in the design matrix^[242]90. For the integrated
data, independent two-sided t-tests were applied to expression values
after ComBat. For both analyses, false discovery rate (FDR) was
calculated by adjusting p values for multiple testing by the
Benjamin-Hochberg method. Genes with FDR ≤ 0.05 and log2 fold change ≥
1 were categorized as differentially expressed.
Hierarchical clustering of DEG
For the first cohort, differential expression was tested in four
comparisons (class-switched renal vs. tonsil, unswitched renal vs.
tonsil, renal class-switched vs. unswitched, and tonsil class-switched
vs. unswitched). Mean expression values of identified 2855
differentially expressed genes (DEGs) were calculated in four
populations (renal switched, renal unswitched, tonsil switched, tonsil
unswitched). Then hierarchical clustering was performed based on their
expression pattern across the four population means, identifying six
gene clusters. A heatmap was produced using pheatmap R package 1.0.12
based on the clustering and Z-scores calculated from the mean values.
Pathway enrichment analysis
GO and KEGG enrichment was tested using clusterProfiler 3.12.0 and
org.Hs.eg.db annotation database 3.8.2. FDR 0.05 was used for
significance cutoff^[243]91. When there were more than 10 significantly
enriched GO terms, redundant terms were removed using “simplify”
function in clusterProfiler library with its default setting.
Enrichment analysis of AHNAK-covariant genes
Gene expression in mouse B-cell subsets in the spleen or peritoneal
cavity were fetched from Immgen^[244]36. Genes were identified as
Ahnak-covariant genes, when their expression pattern within the
peripheral B-cell subsets had a correlation coefficient ≥ 0.8 with
Ahnak. The Ahnak-covariant genes were converted to their human
orthologs as AHNAK-covariant genes using Ensembl database^[245]92. Then
enrichment of the AHNAK-covariant genes in DEG clusters was tested by
hypergeometric test. For the background frequency, we used the
frequency of the Ahnak-covariant genes within all the mouse genes
detected in Immgen microarray data.
Correlation test of gene expression
For Supplementary Fig. [246]4e, f, plasma cells were removed from the
analysis. For the cells of interest, 1,000 most variant genes were
identified. For Supplementary Fig [247]4f, we used 736 genes which were
within top 1,000 high-variant genes in both the first cohort dataset
and integrated dataset. Mean expression values within each cell
population were calculated for the selected genes, and their Pearson
correlation between populations was tested.
Calculation of gene expression scores
Geneset-based scores were calculated as a sum of scaled expression
values of genes present in each geneset. For DEG cluster scores in
mouse B-cell subsets, DEGs in each gene cluster were converted to mouse
orthologs in the same manner described above. Then, scores for the
mouse genes were calculated for each replicate in Immgen data. A mean
score was calculated for each B-cell subset, scaled to Z-scores and
visualized as a heatmap. For innate immune genes, genes tagged to
“innate immune response” GO term were identified in the DEG clusters,
and used to calculate a score. For DN-associated genes, 17
DN-upregulated genes and 9 DN-downregulated genes were defined
according to Arazi et al., 2019^[248]37. Then the difference between a
scaled sum of expression of DN-upregulated and downregulated genes was
used as the score.
Tissue staining
Paraffin-embedded formalin-fixed tissue blocks were sectioned by 3 μm
thickness. Tissue sections were deparaffinized with xylene and ethanol,
and subjected to antigen retrieval with 10 mM citrate buffer pH 6.0
(ThermoFisher Scientific, 005000). Tissue sections were blocked with
Tris-buffered saline (TBS) containing 10% normal donkey serum (Jackson
ImmunoResearch Laboratories, 017-000-121), and incubated with a
combination of primary antibodies: rat or rabbit anti-CD19 (Invitrogen,
clone: 6OMP31, 14-0194-82, or abcam, clone: EPR5906, ab134114,
respectively, 1:100), rabbit anti-AHNAK (Proteintech, 16637-1-AP,
1:100), mouse anti-IL15 (abcam, ab55276, 1:50), and rabbit anti-Ki-67
(abcam, clone: EPR3610, ab92742, 1:100). Antibody binding was detected
by 1:1000-diluted fluorophore-conjugated highly cross-adsorbed
secondary antibodies from ThermoFisher Scientific (Alexa Fluor 488
donkey anti-rat IgG, A21208; Alexa Fluor 488 donkey anti-rabbit IgG,
A21206; Alexa Fluor 594 donkey anti-mouse IgG, A21203; Alexa Fluor 647
donkey anti-rabbit IgG, [249]A31573; Alexa Fluor 647 Plus donkey
anti-goat IgG, A32849), and nuclei were stained with Hoechst 33342
(ThermoFisher Scientific, H3570, 1:500). For staining of FLAG-tagged
recombinant antibodies cloned from rejection patients, rat anti-FLAG
(BioLegend, clone: L5, 637301, 1:200) was used as the secondary
antibody, which was then detected with fluorophore-conjugated anti-rat
IgG antibodies. Stained sections were mounted in ProLong Gold Antifade
Mountant (ThermoFisher Scientific, [250]P36934) and imaged on SP8
confocal microscopy (Leica). Data were analyzed by ImageJ 2.0.0.
Antibody cloning and recombinant expression
Variable regions of antibody heavy and light chain genes were amplified
from cDNA using nested PCR^[251]48. PCR products were Sanger-sequenced
and mapped to IMGT reference. Results of heavy-chain genes were
analyzed for clonality and mutation frequency. Clonal families were
defined by VDJ gene usage and CDR3 length. Next, heavy and light chain
variable regions were cloned into an IgG expression vector. The vector
had a FLAG tag at the C terminus of IgG constant region to enable
tissue staining in the presence of IgG-expressing cells or IgG
deposition. A pair of cloned heavy and light chain vectors were
transfected to HEK293 cells, and expressed IgG was purified using
Protein A agarose beads (ThermoFisher Scientific, 20334), eluted in
0.1 M glycine-HCl pH 2.8, neutralized with 1 M Tris buffer pH 9.0 and
stored in PBS with 0.05% sodium azide.
HLA-binding assay
Antibodies were diluted at 150 μg/mL in PBS and tested on LAB Screen
Mixed (OneLambda, LSM12) according to the manufacturer’s protocol. To
test the binding in the presence of blocking, positive antibodies from
intrarenal B cells were retested in the presence of human serum
proteins. Antibodies were prepared in PBS, then diluted 1:1 in PBS or
negative control serum included in the kit, and subjected to the assay.
NBG ratio was calculated as the experimental readout according to the
manufacturer’s protocol:
[MATH: NBGratioi=Si−S0<
/msub>Ni
−N0 :MATH]
1
S[i] and S[0] are sample signals from the ith antigen-coated beads and
negative beads, and N[i] and N[0] are negative serum signals from the
ith antigen-coated beads and negative beads. NBG ratio ≥ 2.2 was used
as the positivity threshold. To plot log2-transformed values, NBG ratio
less than 1 was replaced with 1. For SAB assay, differences in trimmed
MFI between antibodies and negative control serum were used as a
readout. Eplets information was fetched from HLA Epitope
Registry^[252]93,
HEp-2 cell staining
Antibodies were diluted at 50 μg/mL in PBS, and tested on NOVA Lite
HEp-2 ANA kit (Inova Diagnostics, 708100) according to the
manufacturer’s protocol. Antibody binding was detected on SP8 confocal
microscopy by fluorescent signal from fluorescein isothiocyanate
(FITC)-conjugated polyclonal anti-human IgG secondary antibody included
in the kit.
Western blot
HEp-2 cells (ATCC, CCL-23) were cultured and harvested. A nuclear
fraction was prepared using Nuclear Extraction Kit (Abcam, ab113474)
following the manufacturer’s protocol. Before the final centrifugation
step, lysates were sonicated with three times of a 10-second pulse on
ice. Lysates were boiled in Laemmli buffer at 95 °C for 5 min, and
resolved by SDS-PAGE. Proteins on the gel were transferred to a
polyvinylidene fluoride membrane, blocked by 5 % bovine serum
albumin-containing TBS, and incubated with 10 μg/mL antibody diluted in
the blocking buffer at 4 °C overnight. The membrane was washed and
incubated with a horseradish peroxidase-conjugated anti-human IgG
antibody (ThermoFisher Scientific, 31413, 1:10,000), and binding was
detected with Pierce ECL Western Blotting Substrate (ThermoFisher
Scientific, 32209).
Expression of Ki-67 fragments
Seven fragments were designed to cover the whole Ki-67 protein.
Fragments 1 and 2 were cloned by PCR from cDNA of HEp-2 cells. DNA
encoding the other fragments were purchased from Bio Basic Inc. Primers
used and DNA fragments ordered are listed in Supplementary Data [253]5.
DNA encoding each fragment were digested with NdeI and XhoI restriction
enzymes (NEB, R0111S, and R0146S) and cloned into pET-24b (+) vector.
Rosetta (DE3) E. coli (Millipore, 70954-3) were transformed with the
expression vectors. Overnight culture was diluted in 1:5 in LB media,
and IPTG was inoculated at 0.5 mM. After 4 h of culture, 1 mL of
culture was centrifuged, and cell pellet was resuspended in RIPA lysis
buffer. The lysate was used for western blot.
IP from a nuclear fraction of HEp-2 cell lysates
HEp-2 nuclear lysates were precleared with Protein A Agarose beads, and
incubated with 5 μg of antibodies at 4 °C overnight. The beads were
washed with Tween20-containing PBS, and captured antibody-antigen
complexes were eluted and resolved by SDS-PAGE. Gels were stained with
InstantBlue Protein Stain (Abcam, ab119211) at 4 °C overnight. Stained
gels were destained in deionized water, excised leaving molecular
weight higher than IgG heavy chain, and used for mass spectrometry.
Sample preparation for mass spectrometry
Gel Samples were excised by sterile razor blade and chopped into
~1 mm^3 pieces. Each section was washed in distilled water and
destained using 100 mM NH[4]HCO[3] pH 7.5 in 50% HPLC-grade
acetonitrile (VWR, BJ015-4). Samples were reduced by adding 100 μL
50 mM NH[4]HCO[3] pH 7.5 and 10 μL of 200 mM tris (2-carboxyethyl)
phosphine HCl and incubating at 37 °C for 30 min. The proteins were
alkylated by adding 100 μL of 50 mM iodoacetamide freshly prepared in
50 mM NH[4]HCO[3] pH 7.5 buffer and incubated in the dark at 20 °C for
30 min. Gel sections were washed in water and acetonitrile, and vacuum
dried. Samples were digested by sequencing-grade modified trypsin
(Promega, V5111) in 50 mM NH[4]HCO[3] pH 7.5, and 20 mM CaCl[2] at with
1:50–1:100 enzyme–protein ratio for overnight at 37 °C. Peptides were
extracted first with 5 % formic acid, then with 75 % ACN:5% formic
acid, combined and vacuum dried. Digested peptides were cleaned up on a
C18 column (PicoFrit column blanks (New Objective, PF360-100-15-N-5)
self-packed with Agilent Poroshell 120, EC-C18, 2.7 µm (Agilent
696975-902)), speed vacuumed and sent for liquid chromatography–tandem
mass spectrometry (LC-MS/MS) to the Proteomics Core at Mayo Clinic.
High-performance liquid chromatography for mass spectrometry
All samples were resuspended in Honeywell Burdick & Jackson HPLC-grade
water (VWR, BJ365-4) containing 0.2 % formic acid (ThermoFisher
Scientific, PI28905), 0.1 % TFA (ThermoFisher Scientific, PI28904), and
0.002 % Zwittergent 3-16 (Calbiochem, 603023). This sulfobetaine
detergent contributes to the following distinct peaks at the end of
chromatograms: MH^+ at 392, and in-source dimer [2 M + H^+] at 783, and
some minor impurities of Zwittergent 3-12 seen as MH^+ at 336. Peptide
samples were loaded to a 100 µm × 40 cm PicoFrit column self-packed
with 2.7 µm Agilent Poroshell 120, EC-C18, washed, then switched
in-line with a 0.33 µL Optimize EXP2 Stem Traps spray tip nano column
packed with Halo 2.7 µm Pep ES-C18 (Optimize Technologies, 15-04001-HN)
for a 2-step gradient. Mobile phase A was water/acetonitrile/formic
acid (98/2/0.2) and mobile phase B was
acetonitrile/isopropanol/water/formic acid (80/10/10/0.2). Using a flow
rate of 350 nL/min, a 90-min 2-step LC gradient was run from 5% B to
50 % B in 60 min, followed by 50–95% B over the next 10 min, hold
10 min at 95 % B, back to starting conditions and re-equilibrated.
LC-MS/MS data acquisition and analysis
The samples were analyzed by data-dependent electrospray tandem mass
spectrometry (LC-MS/MS) on a Thermo Q-Exactive Orbitrap mass
spectrometer, using a 70,000 RP survey scan in profile mode, m/z
360–2,000 Da, with lockmasses, followed by 20 HCD fragmentation scans
at 17,500 resolution on doubly and triply charged precursors. Single
charged ions were excluded, and ions selected for MS/MS were placed on
an exclusion list for 60 s.
All LC-MS/MS *.raw Data files were analyzed with MaxQuant 1.5.2.8,
searching against the UniProt Human database (Download on 9/16/2019
with isoforms, 192,928 entries) *.fasta sequence, using the following
criteria: LFQ was selected for Quantitation with a minimum of 1 high
confidence peptide to assign LFQ Intensities. Trypsin was selected as
the protease with maximum missing cleavage set to 2. Carbamidomethyl
(C) was selected as a fixed modification. Variable modifications were
set to Oxidization (M), Formylation (N-term), Deamidation (NQ).
Orbitrap mass spectrometer was selected using an MS error of 20 ppm and
a MS/MS error of 0.5 Da. 1 % FDR cutoff was selected for peptide,
protein, and site identifications. Ratios were reported based on the
LFQ Intensities of protein peak areas identified by MaxQuant and
reported in the proteinGroups.txt. The proteingroups.txt file was
processed in Perseus 1.6.7. Proteins were removed from this results
file if they were flagged by MaxQuant as “Contaminants”, “Reverse” or
“Only identified by site”. Three biological replicates were performed.
Samples were filtered to require hits to have been seen in at least two
replicates per condition. Intensities were normalized by median
intensity within each sample. Then, log2 fold changes over the means of
negative controls were obtained for the three antinucleolar antibodies.
Reporting summary
Further information on research design is available in the [254]Nature
Research Reporting Summary linked to this article.
Supplementary information
[255]Supplementary Information^ (17.6MB, pdf)
[256]Peer Review File^ (174.8KB, pdf)
[257]41467_2021_24615_MOESM3_ESM.pdf^ (92.9KB, pdf)
Description of Additional Supplementary Files
[258]Supplementary Data 1^ (101.3KB, xlsx)
[259]Supplementary Data 2^ (16.3KB, xlsx)
[260]Supplementary Data 3^ (20.7KB, xlsx)
[261]Supplementary Data 4^ (18.8KB, xlsx)
[262]Supplementary Data 5^ (11.4KB, xlsx)
[263]Reporting Summary^ (148.9KB, pdf)
Acknowledgements