key: cord-0266044-85ajgt50
authors: Scheid, Johannes F.; Eraslan, Basak; Hudak, Andrew; Brown, Eric; Sergio, Dallis; Delorey, Toni; Phillips, Devan; Lefkovith, Ariel; Jess, Alison T.; Duck, Lennard W.; Elson, Charles O.; Vlamakis, Hera; Deguine, Jacques; Ananthakrishnan, Ashwin; Graham, Daniel B.; Regev, Aviv; Xavier, Ramnik J.
title: Remodeling of human colon plasma cell repertoire in ulcerative colitis
date: 2022-02-14
journal: bioRxiv
DOI: 10.1101/2022.02.14.480403
sha: c2ae90a8a1d851bb301cd20905302cadc16b878e
doc_id: 266044
cord_uid: 85ajgt50

Plasma cells (PCs) constitute a significant fraction of cells in colonic mucosa and contribute to inflammatory lymphocytic infiltrates in ulcerative colitis (UC). While gut PCs secrete 3-5 g of immunoglobulins daily, including IgA antibodies that target colitogenic bacteria, their role in UC is not known. Here, we combined B cell sorting with single-cell VDJ- and RNA-seq and monoclonal antibody (mAb) testing to characterize the colonic PC repertoire in healthy individuals and patients with UC. We show that a large fraction of B cell clones is shared between different colon regions and that inflammation in UC disrupts this landscape, causing clonal expansion and isotype skewing from IgA1 and IgA2 to IgG1. mAbs produced from expanded PC clones show low polyreactivity and autoreactivity and target specific bacterial strains. Expression profiles of individual PCs from inflamed and non-inflamed colon regions indicate that inflammation is associated with up-regulation of the unfolded protein response (UPR) and antigen presentation genes. Together, our results characterize the microbiome-specific PC response in the colon, its disruption in UC and how PCs might contribute to inflammation in UC.

Despite advances in ulcerative colitis (UC) treatment many patients with poorly controlled disease still have to undergo colectomy 1 and a better understanding of the disease is needed in order to identify new treatment targets. In particular, the role of microbiome-directed adaptive immunity in UC remains elusive 2 . Plasma cells (PCs) constitute a major fraction of lymphocytes in UC-associated inflammatory infiltrates and their expansion is linked to the risk of disease recurrence 3 . In addition, an Fc receptor variant (FcgR2) with decreased affinity to IgG1 is protective against UC in GWAS studies suggesting a role for IgG mAbs in the pathogenesis of UC 3, 4 . PCs in the human gut secrete grams of mAbs daily into the intestinal lumen 5 and some of these are thought to target colitogenic bacteria 6 . However, the composition of the PC repertoire throughout the human colon in health and UC remains elusive.

Here, we used single cell VDJ-and RNA-seq profiling (sc(VDJ+RNA)-Seq) of healthy control subjects (HCs), UC patients in remission and those with active inflammation, to conduct an indepth analysis of the mAb repertoire and PC expression states throughout the human colon. We show that inflammation is associated with a shift from IgA to IgG, disruption of PC clonal architecture, upregulation of the unfolded protein response (UPR) and antigen presentation genes in PCs, and that IgG1 mAbs from inflamed colon PCs specifically target pathogenic bacterial strains.

To understand the effect of UC on the colonic PC repertoire, we profiled by sc(VDJ+RNA)-Seq 181,124 PCs from 49 colon biopsies or mucosal resection samples from 8 UC patients with at least one area of mucosal inflammation (12 inflamed and 9 less inflamed samples from the same subjects), 5 UC patients in endoscopic and histologic remission (8 non-inflamed samples) and 8

HCs (20 healthy samples) (Fig. 1a, Methods) . The 21 subjects had equal representation of gender (10 women, 11 men) and ranged in age from 28 to 75 years; UC patients were diagnosed 1-20 years prior to enrollment and were on different therapeutic regimens (Extended Data Table 1 ). We digested colon samples to single cell suspensions, performed fluorescence-activated cell sorting (FACS) of CD27+CD38+ cells (5-20% of all cells, Fig. 1b ) and profiled sorted cells by 5' directed single-cell RNA-Seq (scRNA-seq) for both mRNA and paired VDJ profiling (sc(VDJ+RNA)-Seq), recovering matched single-cell VDJ and expression profiles, yielding 2,237-16,802 high quality cells per donor ( Fig. 1c and Extended Data Fig. 1a , Methods).

Consistent with early immunohistochemistry analyses 7 and prior single-cell atlases of the colon 8 , a significant skewing from IgA1 and IgA2 toward IgG1 occurred in inflamed samples, and to a smaller degree in less inflamed and non-inflamed samples, when compared to HCs (Fig. 1d , Methods, tested with both multivariate test accounting for compositional dependencies and onesided non-parametric Wilcoxon rank-sum test, P-value < 10 -4 ). IgG1 was the most frequent isotype in PCs from inflamed samples, but IgG2 and IgG3 were also significantly elevated when compared to PCs from HCs ( Fig. 1d and Extended Data Fig. 1b , Methods, tested with both multivariate test accounting for compositional dependencies and one-sided non-parametric Wilcoxon rank-sum test, P-value < 10 -4 ). Thus, PCs in UC are significantly skewed toward IgG1 when compared to HCs and this difference is most pronounced in inflamed tissue samples.

Most (56-86%) B cells from the 21 subjects belonged to clones of three or more cells based on their VDJ transcripts (Extended Data Fig. 1c) . As an indication of significant clonal expansion Shannon entropy and Simpson index decreased in PCs from inflamed biopsies compared to all other disease states (HCs, uninflamed biopsies from remission, and less inflamed biopsies from subjects with active disease) (Fig. 1e, Extended Data Fig. 1c Estimating clonal overlap between randomly selected sets of 100 cells from the same sample or different colon regions within a patient (Methods), the largest overlap was found among PCs within the same sample, followed by separate colon regions with the same inflammation state, where overlap was significantly higher than for regions with different inflammation states (Fig.   1f , g and Extended Data Fig. 2a, b , Methods, one tailed t-test, P-value <= 10 -3 ). This held true when expanded clones were collapsed to one cell to correct for different levels of clonal expansion (Extended Data Fig. 2c, Methods) . Identical clonal members (defined as a clonal member carrying the same heavy chain nucleotide sequence) were also significantly expanded in inflamed samples (Fig. 1h) . Although PCs expressing Igk light chains were more prevalent than Igl light chains in all samples, there was a significant shift from Igk to Igl usage in UC samples (inflamed, less inflamed or non-inflamed) compared to HCs (Extended Data Fig. 3a , one-sided non-parametric Wilcoxon rank-sum test, P-value < 10 -4 ). VH and VL gene repertoires were consistent across antibody isotypes and disease states (Extended Data Fig. 3b-d, Methods) .

Other antibody features such as CDRH3 length, charge and hydrophobicity, or inferred selection pressure (Methods) differed across antibody isotypes and disease states, some reaching statistical significance (Extended Data Fig. 3e-h, Methods) . Taken together, inflammation in UC is associated with increased clonal PC expansion and clonal overlap between separate regions with shared inflammation status.

The scRNA-seq profiles of the 181,124 sorted PCs partitioned into four main clusters (TCs), with one cluster (cluster 0) shared across all subjects ( Fig. 2a and Extended Data Fig. 4a-c) .

Marker genes for this cluster include NR4A1 and EZR which are both involved in the response to B cell receptor-antigen interactions 9,10 (Extended Data Fig. 4b) . Cluster 1, with marker genes HSPA1A and HSPB1 that are upregulated in cellular stress 11 , and cluster 3, with marker genes DERL3 and SLC3A2 that are involved in the UPR 12, 13 , were specific to a small number of subjects (c, Extended Data Fig. 4c) ; cluster 2 was enriched in cells in cell cycle phases G2 and S (Fig. 2b) . We observed an apparent separation between cells from inflamed vs. all other samples (Fig. 2c) , even when restricting only to cells from subjects with matched inflamed and less inflamed samples (Extended Data Fig. 4d, e) . Consistent with our PC isotype analysis above, IgG was enriched in clusters associated with inflammation ( Fig. 2d) .

Genes differentially upregulated in PCs from inflamed compared to healthy colon areas across all cells (Extended Data Fig. 5a Table 1) . Several genes involved in antigen uptake and presentation (CD79A, CD79B and several MHCII genes) were upregulated in PCs from inflamed areas (Fig. 2f , pseudobulk DE analysis, FDR < 0.05, Extended Data Fig. 5b,c) , possibly mirroring the role of PCs in celiac disease as antigen presenting cells 22 . CXCR4, encoding an important chemokine receptor for PC homing and survival, was also differentially expressed only in IgG+ PCs from inflamed regions, consistent with prior findings in gut PCs from HIV and Crohn's Disease 23 (Extended Data Fig. 5a,d) . Non-negative matrix factorization (NMF) of the scRNA-seq profiles of all 14 donors identified 10 programs (latent factors) underlying the variation across the cells (Methods), which we annotated based on high loading genes (Extended Data Fig. 6a, Supplementary Tables 2, 3) . The programs for cell division (Program 1), cellular biosynthetic processes and oxidative stress (3), UPR response (4) and MHC-IIexpression (7) increased in cells from inflamed regions (Fig. 2g, Extended Data Fig. 6b) .

Multiple genes associated with inflammatory bowel disease (IBD) or UC through GWAS 23, 24, 25, 26 are highly expressed in IgG+ PCs from UC inflamed tissue, including genes related to oxidative stress (GPX1, PRDX5, PARK7) 15, 16 and ER processes (KDELR2, SDF2L1) 27,28 (Extended Data Fig. 7a , b, Methods, pseudobulk DE analysis, FDR < 0.05). SDF2L1 is upregulated during the acute UPR and functions as an ER chaperone to facilitate protein cargo secretion 29 . Overall, genes associated with elevated antibody secretion, surface BCR expression, antigen presentation and cell division are upregulated in PCs from inflamed colon areas.

Examining the expression profiles of all cells from 6 representative PC clones from 3 donors with both inflamed and less inflamed colon areas (Fig. 3a,b, Supplementary Table 4 ), we found that PCs of the same clone separated mostly based on the inflammation status of their corresponding sample type. In all subjects with inflamed/less inflamed samples, the pairwise cosine distances of PC expression profiles was significantly larger between clonal members across inflammation states compared to clonal members within inflamed or less inflamed regions ( Fig. 3c , one tailed t-test, P-value <= 10 -4 , Methods). With one exception (UC10), this was the case for each individual subject with matched inflamed and less inflamed samples (Extended Data Fig. 7c , one tailed t-test, P-val <= 10 -4 , Methods). Thus, expression differences between PCs from inflamed and less inflamed areas of the colon are apparent even for members of the same PC clone, suggesting that the local tissue environment is a dominant factor impacting their expression profiles.

Polyreactivity, defined as non-specific binding to unrelated antigens, is selected against throughout B cell development 30 , but can arise during antibody affinity maturation 31 To evaluate polyreactivity and autoreactivity in mAbs from colon PCs in UC patients, we selected 152 mAbs from expanded clones from 5 UC patients and 2 HCs (Fig. 4a,   Supplementary Table 4) , and produced them as IgG1 mAbs irrespective of their original isotype in order to directly compare Fab reactivity. We defined polyreactivity as reactivity against at least two of the following antigens: single stranded DNA (ssDNA), double stranded DNA (dsDNA), lipopolysaccharide (LPS), insulin (INS) or cardiolipin (CAR) 31, 34 (Fig. 4b) . We defined autoreactivity as binding against HEP-2 cells (Fig. 4c) . We detected polyreactivity only in 6 of 89 (6.7%) antibodies from inflamed colon areas, 2 of 39 (5%) antibodies from less inflamed colon areas and 0 of 15 (0%) antibodies from HCs; the frequency of polyreactive antibodies was not significantly different between PCs from inflamed colon compared to HCs (p=0.3 ꭓ 2 test) (Fig. 4b, Extended Data Fig. 8 ). Polyreactivity rates were lower than those in mAbs isolated from HC human memory B cells 31 (overall 22.7%, p<0.005, ꭓ 2 test) or in mouse colon-derived antibodies 32 (29%, p<0.005, ꭓ 2 test). Consistent with the low polyreactivity, only 1 of 152 mAbs (UC10NINF9) showed reactivity against HEP-2 cells (Fig. 4c) .

Sequencing of IgA-covered microbiota revealed preferential targeting of colitogenic bacteria 6 and PCs isolated from the small intestine of healthy subjects and Crohn's disease patients show both specific and cross-species reactivity 34 . However, evaluating specific antibody-microbiota interactions is technically challenging because of the differences between stool samples, due to non-specific interactions such as polyreactivity and binding to B cell superantigens 35 .

We tested each of the 152 mAbs for their binding to single strains of a 32-strain bacterial panel that we assembled based on common members of the human microbiome and strains enriched in the microbiome of IBD patients [36] [37] [38] [39] [40] [41] (Fig. 4d) . Consistent with prior reports of VH3-encoded heavy chains binding to a superantigen on Coprococcus comes as well as Ruminococcus gnavus 35 , 54 of 57 VH3-encoded antibodies showed strong binding against at least one of these strains (Fig. 4d) and 54 of 60 antibodies with strong binding carried a VH3 heavy chain. Crossstrain reactivity with strong binding (>10% binding in FACS) to two or more bacterial strains (not counting non-specific superantigen binding as described above) was only detected in 4 of 152 antibodies (Fig. 4d) , none of which were polyreactive. Aside from R. Gnavus and C. Comes binding, 5 of 152 antibodies showed strong binding to a single bacterial strain, Klebsiella pneumoniae (Fig. 4d, e) . All K. pneumoniae-binding mAbs were isolated from one UC patient (UC18, Fig. 4d , e, Extended Data Table 1 ) and each of these mAbs represents an expanded PC clone that spans multiple colon regions with preferential expansion in inflamed regions (Fig. 4a, 

To test if our 152 mAbs bind to stool bacteria, we grouped them into 12 mAb pools, including pools of VH3 and non-VH3 mAbs from inflamed, non-inflamed and HC colon-derived PCs, as well as one mAb pool with all polyreactive mAbs, (Supplementary Table 5 ), and tested for binding against stool from C57BL/6J and RAG-1-deficient mice (to avoid cross-detection of IgG-bound bacteria in human stool samples). mAB binding frequencies were low (0.3%-4.1%, Fig. 9a) , consistent with the observed low level of polyreactivity and crossstrain reactivity (above). Fig. 9b, Supplementary Table 6 ).

None of these binding mAbs were polyreactive (Extended Data Fig. 8, 9b) . Finally, none of the 152 antibodies strongly bound common enteric viruses, including cytomegalovirus (CMV), Epstein-Barr virus (EBV) and rotavirus in ELISA (Extended Data Fig. 8 ). We conclude that mAb-microbiota interactions from local PCs include specific interactions with K. pneumoniae as well as non-specific superantigen interactions with C. comes and R. gnavus and that broad crossstrain reactivity is rare.

A significant fraction of microbiota in stool from mice and humans is bound by endogenously produced IgA 6 , but how much of that binding activity reflects specific Fab interactions with microbial surface antigens vs. non-specific binding is unknown.

To test if isotype switching changes the binding activity of antibodies to microbial targets, we chose two 'orthogonal' mAbs with known specificity to SARS-CoV-2 43 and tested their IgG1 and IgA1 forms for binding to stool from C57BL/6J and RAG-1-deficient mice (Extended Data   Fig. 9c) . While IgG1 forms of both mAbs showed no binding, the same antibodies expressed as IgA1 showed significant binding to all stool samples tested, ranging from 4.9-6.9% (Extended Data Fig. 9c ). To test if increased binding of microbiota to IgA1 mAbs is observed across different bacterial strains, we tested the same two antibody pairs as well as three additional SARS-CoV-2-specific IgG1/IgA1 pairs against our panel of 32 bacterial strains. While we detected minimal binding of IgG1 mAbs (Fig. 4f) , 15 out of the 34 strains that were neither R. gnavus nor C. comes were bound by at least two of the IgA1 mAbs. We conclude that a significant fraction of microbiota show non-specific binding to unrelated IgA1 molecules and that those interactions likely involve residues outside of the antigen binding site.

Immune responses to the microbiome play an important role in UC 2 , and the protective role of Beyond specific interactions, we also found significant non-specific VH-3 binding to R. gnavus and C. comes, and demonstrated that an isotype switch from IgG1 to IgA1 in unrelated SARS CoV-2-specific mAbs can lead to nonspecific binding against selected bacterial strains. These findings underscore the multitude of mAb-microbiome interactions and the importance of investigations at the monoclonal level to understand the role of specific B cell responses in UC.

Certainly, the upregulation of MHCII-related genes in PCs from inflamed areas suggests that such specific B cell responses could also impact inflammatory T cell responses. Further studies of these interactions and the antigens involved are needed.

All work with human samples was performed in accordance with approved Institutional Review Board (IRB) protocols which were reviewed by the IRB at Massachusetts General Hospital (MGH), Boston. HCs as well as UC patients were recruited through a patient cohort that has been created at MGH under IRB protocol 2004P001067, "Prospective Registry in Inflammatory Bowel Disease Study at Massachusetts General Hospital". HCs were overall healthy individuals without any history of IBD, other autoimmune disease, infectious colitis or colon cancer. UC patients were included based on carrying a clinical diagnosis of ulcerative colitis. UC patients were determined to either have active disease or to be in remission based on macroscopic and histopathologic evaluation of the mucosa in the setting of endoscopic evaluation or of a resection sample (Extended Data Table 1 ). None of the subjects had any known history of infectious colitis. Resection samples were obtained in the setting of total colectomies.

Biopsy bites or mucosal resection samples were immediately processed and mucosal samples placed into cryovials containing Advanced DMEM F-12 media (ThermoFisher Scientific) and placed on ice for transport. Single-cell suspensions from collected mucosal samples were obtained using a modified version of a previously published protocol 47 as detailed below.

Mucosal samples were first rinsed in 30 mL of ice-cold PBS. Each individual sample was then transferred to 5mL of enzymatic digestion mix (Base: RPMI1640, 100 U/ml penicillin (ThermoFisher), 100 μg/mL streptomycin (ThermoFisher), 10 mM HEPES (ThermoFisher), 2% FCS (ThermoFisher), and 50 μg/mL gentamicin (ThermoFisher), freshly supplemented immediately before with 100 mg/mL of Liberase TM (Roche) and 100 μg/mL of DNase I (Roche), and incubated at 37°C with 120 rpm rotation for 30 minutes. After 30 minutes, the enzymatic dissociation of the lamina propria was quenched by addition of 1ml of 100% FCS (ThermoFisher) and 80 μL of 0.5M EDTA and placed on ice for five minutes. Samples were typically fully dissociated at this step and after gentle trituration with a P1000 pipette filtered through a 40mM cell strainer into a new 50 mL conical tube and rinsed with PBS to 30 mL total volume. This tube was spun down at 700g for 10 minutes and resuspended in 500ul of PBS with 5% fetal bovine serum (FBS). Cell suspensions were then stained with anti-human CD27 PE (BD), CD38 FITC (BD) and CD19 APC (BD) and incubated for 20 minutes at 4°C before they were washed and resuspended in PBS with 5% FBS. PCs were then sorted using a Sony MA900 cell sorter by gating on live cells in the forward scatter and side scatter and on CD38-FITC and CD27-PE double-positive cells (Fig. 1b) . After sorting, cells were washed and counted using a hemocytometer and microscopy, before resuspending up to 10,000 cells in a volume of 32μl for 5' single-cell RNA-Seq (see below).

Cells were separated into droplet emulsions using the Chromium Next GEM Single-cell 5′ (ThermoFisher) or IgG specific ELISA as previously described 48 . mAb reactivities to EBV, CMV and rotavirus were determined using IgG detection kits against the respective viruses:

EBV and CMV (abcam), rotavirus (amsbio) and the concentration of mAb used was 1μg/ml. Polyreactivity ELISAs against antigens ssDNA, dsDNA, LPS and insulin and ELISA against cardiolipin (Sigma) were performed as previously described 48, 49 . Similar to prior studies, polyreactivity was defined as reactivity to two or more antigens including single stranded DNA, double stranded DNA, lipopolysaccharide (LPS), insulin or cardiolipin 34, 48 . As previously IgG secondary antibody labeled with Dylight 650 (Invitrogen) was applied at 1 to 1000 (~0.5μg/ml) for 1 hour in SuperBlock. The pads were then washed three times with PBS-tween, distilled water, air-dried and scanned using a GenePix 4000B imager (Axion).

All bacterial strains used in this study are detailed in Fig. 4d . Bacteria were cultured and stored using the following protocol: bacteria were struck out and streaked from frozen bacteria stocks onto Cullen-Haiser Gut (CHG) or yeast casitone fatty acid agar with carbohydrates (YCFAC) media plates under anaerobic conditions. Plates were incubated at 37°C, under anaerobic conditions for 24-72h, until isolated bacterial colonies were obtained. Next, a single bacterial colony was inoculated into a new 15mL falcon tubes (Corning) containing CHG or YCFAC media. Liquid cultures were incubated at 37°C, under anaerobic conditions, for 24-72h, until optical density at 600nm (OD600) of 0.2 or higher were obtained. Liquid cultures were combined with 50% glycerol, 1X PBS solution (1:1 ratio) and aliquoted into cryovials. Cultured strains were verified via 16S rRNA v1-v9 sequencing and further aliquots were stored at -80°C for flow cytometry analysis at a later time. Verification of cultured strains via 16S sequencing was conducted as follows: 5μL harvested bacterial culture was aliquoted to a corresponding well in a sterile 96-well PCR plate (ThermoFisher) and added 50µL of hot-shot lysis buffer was added to each well containing bacterial aliquots, as well as three additional, empty reagent control wells. Plates were sealed and placed into PCR thermocycler (Bio-Rad) for 10 min at 95°C, followed by a holding stage at 12°C. 50μL neutralization buffer was added to each well, mixed gently and stored plates at -20°C. Plates with template DNA were thawed on ice and PCR reagents (1.5µL 10mM 16S rRNA forward prime (IDT), 1.5µL 10mM 16S rRNA reverse primer (IDT), 12.5µL OneTaq 2X MasterMix (NEB) and 5µL nuclease-free water) were added to new sterile 96-well PCR plate and mixed before addition of template DNA. Three wells of PCR reagents without template DNA were added as reagent controls. Plates were sealed and placed into PCR thermocycler for the following protocol: step 1, 94°C for 30 s; step 2, 94°C for 30 s; 

Verified bacteria were washed, diluted, and stained for downstream flow cytometry analyses as follows: bacteria were thawed on ice, resuspended, filtered through a sterile 50µm CellTrics filter (Sysmex America), transferred to a new 1.5-mL tube, and centrifuged at 8000 g for 5 min.

Supernatants were removed by aspiration, and pellets resuspended in 1.5 mL cold PBS 0.25% BSA (Sigma), and centrifuged at 8000 g for 5 min. Supernatants were removed by aspiration and pellets resuspended in 1.0 mL cold PBS 0.25% BSA. Optical densities were recorded at 600 nm (OD600) via NanoDrop 2000c (ThermoFisher) and 2.0 mL cuvette (Fisherbrand) to estimate bacterial culture densities. Suspensions were adjusted to OD600 of 0.1 to 0.2, 50 μL/well was aliquoted to a Nunc 96-well, V-bottom, polypropylene plate (ThermoFisher). Next, mAbs were diluted to a concentration of 2.0 µg/mL in cold PBS 0.25% BSA in a new 96-well plate. 50 µL of diluted mAbs were transferred to the 96-well plate containing 50 µL/well of bacterial suspension (final working mAb concentration is 1 µg/mL). mAb testing was performed for binding to bacteria as described below.

Fresh fecal pellets were collected from adult WT C57BL/6J (Jackson Laboratory, Stock #000664) and Rag1 tm1Mom (Jackson Laboratory, Stock #002216) mice, pooled by genotype, and stored at -80°C. Mouse fecal pellets were thawed and resuspended in 10 mL of BIOME-preserve anaerobic medium, were transferred to a gentleMACS C tube (Miltenyi), and homogenized via gentleMACS Dissociator (Miltenyi) for 3 cycles of 61 s on the 'intestine' setting. All stool homogenates were aliquoted into 2.0-mL cryovials (VWR) and stored at -80°C.

Homogenized stool samples were washed, diluted, and stained for downstream flow cytometry and FACS analyses as follows: stool samples were thawed on ice, resuspended, and transferred to a new 1.5-mL tube. Samples were centrifuged at 50 g for 20 min to pellet large debris.

Supernatants were filtered through a sterile 50µm CellTrics filter (Sysmex America) and transferred to a new 1.5mL tube. Samples were centrifuged at 8000 g for 5 min and supernatants 

For the mRNA data integration, count normalization, dimensionality reduction, clustering, cell scoring, and cluster marker genes detection Seurat R package 45, 46 was employed.

Cells which either do not have 10x standard high-quality heavy and light chain V(D)J sequences, or have more than 10% of their transcriptome reads coming from mitochondrial genes were filtered out before the downstream transcriptome analysis. For the UMI count normalization step, gene expression counts for each cell were divided by the total counts for that cell and multiplied 

Dimensionality reduction was done with PCA identifying the first 50 principal components. For clustering of the cells into expression clusters, a k-nearest neighbor (kNN) graph of the cells was constructed (k=20) using the 50 principal components. Next, this kNN graph was used to generate the shared nearest neighbor (sNN) graph by calculating a Jaccard index between every cell and its k nearest neighbors. Then the Leiden algorithm 52 was used to find the clusters of the cells based on the generated sNN graph, with a resolution of 0.024 decided based on the identifiability of the marker genes. Expression levels of immunoglobulin genes were discarded during the clustering step. Uniform manifold approximation and projection (UMAP) 53 algorithm was run on the first 50 principal factors to obtain the 2D projections of the cells.

Module scores of the cell cycle genes and the antigen presentation genes were calculated as previously defined in [47] , where for each gene in the module gene-set, 100 genes were randomly selected as control genes .

Differentially expressed genes between UC inflamed and healthy samples were identified with pseudobulk differential expression analysis using the DESeq2 R package 54 , where counts of each gene were aggregated at the sample level. Multiple testing correction was performed with the Benjamini-Hochberg procedure. Genes that had FDR < 0.05 were accepted as significantly differentially expressed between UC inflamed and healthy samples.

Non-negative matrix factorization of the integrated single cell count matrix was done by utilizing the Consensus Non-negative Matrix factorization (cNMF) 55 Python package. Number of high variance genes that were used for running the factorization was set to 3000 and the loss function for NMF was frobenius. Optimal number of latent factors was decided based on the identifiability of the independent pathways verified by the gene set enrichment analysis. The genes with significantly high loadings per factor were defined to have loadings greater than 3 interquartile ranges higher than the 75th percentile. Gene set enrichment of the selected genes was performed with the clusterProfiler R package 56 .

The V(D)J contig assembly algorithm from 10x Genomics inspecting the bimodal distribution of the distance between each sequence in the data and its nearest-neighbor.

For isotype distribution comparison analysis, for each biopsy sample one representative member of each clone with unique heavy and light chain sequences was selected. Next, the percentage of the IgA1, IgA2, IgG1, IgG2, IgG3, IgG4 and IgM isotypes within the selected cells was calculated for each sample. Statistical significance of the differences between the distributions of the isotype percentages between samples with different disease status were tested with both non-parametric wilcoxon rank sum test and Dirichlet-multinomial regression, implemented as DirichReg function in DirichletReg R package 58 , to account for the fact that the percentage values of all isotypes within a sample sum up to 100.

Replacement and silent mutation inference based on the scRNA-seq VDJ sequences of the donor 

To compare the clonal repertoire between two samples, 100 cells were randomly sampled from each sample 1,000 times, and for each pair of random selections we calculated the percentage of cells that belong to clones that have members in each of the two random samples. The distribution of these 1,000 clonal overlaps is taken as a measure of repertoire similarity between the two compared samples. The same testing schema is repeated with one clonal representative cell per sample to control for the bias that might have been introduced due to the greater number of cells in the expanded clones.

Dominant isotype conservation across inflammation status in the expanded clones (n > 9) that span both inflamed and less-inflamed regions of the donor was tested by calculating the Spearman's correlation coefficient between the clonal percentage differences of the IgG and IgA cells coming from the inflamed samples (column 4 minus column 6 of Extended Data Fig. 1d) versus the clonal percentage differences of the IgG and IgA cells from the less-inflamed samples (column 3 minus column 5 of Extended Data Fig. 1d) .

To test the partitioning of the clones on the expression landscape, for each clone which has members from both inflamed and less inflamed regions, the pairwise cosine distance was 

R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics. A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. Since 1 August 2020, A.R. has been an employee of Genentech. Figure 1 : Patient selection, PC sorting, isotype analysis and clonal landscape. a, Three patient categories recruited in this study with the number of subjects indicated in the center, respectively. Left: UC patients with inflammation, center: UC patients in remission and right: HCs. The number of cells included in transcriptome analysis is indicated for each sample type. b, Representative FACS plots with the gating strategy for sorting of colon PCs. Cells isolated from digestion of colon biopsies and resection samples were gated on live cells based on their appearance in side scatter (BSC) and forward scatter (FSC). Of these, CD38-FITC and CD27-PE double-positive cells were selected for sorting and sequencing. c, Pie charts show the expansion of differently sized PC clones for all samples grouped based on their inflammation status. Numbers in the center of the pie charts stand for the total number of cells analyzed in that particular plot. d, Box-plots displaying the distribution of the percentage of immunoglobulin isotypes (y-axis) across samples grouped by their inflammation status (x-axis). Brackets indicate statistical significance using a one-sided non-parametric Wilcoxon test with *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001 and n/s indicating no statistical significance. e, Box plots displaying the distribution of the Shannon entropy values of the samples stratified by their inflammation status as indicated. Shannon entropy is a measure of population diversity which is reversely related with the clonal expansion (Methods). Brackets indicate statistical significance using a one-sided t-test with ****P ≤ 0.0001 and n/s indicating no statistical significance. f, Violin plots displaying the distribution of the percentage of the shared clones between randomly sampled sets of PCs (Methods). Here the two random samples of a specific donor that are to be evaluated for clonal overlap can belong to i) same tissue sample (left), ii) different colon region with same inflammation status (center) and iii) different colon region with different inflammation status (right). The overlap between random samples that belong to different colon regions with the same inflammation status is significantly smaller (P value <= 0.0001, one-sided t-test) than the random samples that belong to the same colon regions for all healthy, noninflamed, less inflamed and inflamed groups. Table 4 ). PCs from inflamed and less inflamed colon areas from the respective subject are highlighted in blue and yellow, respectively. Red dots indicate clonal members belonging to the selected clone from inflamed colon areas and green dots from less inflamed colon areas. b) Phylogenetic trees summarize the clonal relationship of all members within the selected clones. Trees are rooted on a theoretical germline member (black node), uncolored nodes indicate inferred intermediates and yellow and orange node colors indicate clonal members from less inflamed or inflamed colon areas, respectively. Node borders indicate the mAb isotype of the majority of cells in each node and the number in the center of each node indicates the number of cells represented in each node. Numbers on the connecting lines indicate the number of heavy chain mutations separating two nodes. c) Comparison of the pairwise cosine distances between PCs based on their transcriptome. Groups from left to right display the PC pair distance distributions between i) inflamed clone members, ii) less-inflamed clone members iii) inflamed and less-inflamed clone members iv) inflamed clone members and 100 randomly selected PCs of the donor that are inflamed and are not clone members v) clone members of less-inflamed samples and 100 randomly selected PCs of the donor that are less-inflamed and are not clone members. There is a significant difference between transcriptional distances between clone members based on their inflammation type. Table 4 ) as well as its expansion in inflamed (red bar) or less inflamed (grey bar) colon areas. mAbs are grouped based on the donor and colon area they were isolated from. The numbers below each clone correspond to the antibody names in Supplementary Table 4 

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KDEL Receptors Are Differentially Regulated to Maintain the ER Proteome under Calcium Deficiency

QRICH1 dictates the outcome of ER stress through transcriptional control of proteostasis

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The majority of intestinal IgA+ and IgG+ plasmablasts in the human gut are antigen-specific

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Cumulus provides cloud-based data analysis for large-scale single-cell and singlenucleus RNA-seq

Adjusting batch effects in microarray expression data using empirical Bayes methods

UMAP: Uniform Manifold Approximation and Projection

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clusterProfiler: an R package for comparing biological themes among gene clusters

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Dirichlet Regression for Compositional Data in R

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Supplementary information is available for this paper.