key: cord-0756035-6soui290
authors: Kramer, Kevin J.; Wilfong, Erin M.; Voss, Kelsey; Barone, Sierra M.; Shiakolas, Andrea R.; Raju, Nagarajan; Roe, Caroline E.; Suryadevara, Naveenchandra; Walker, Lauren; Wall, Steven C.; Paulo, Ariana; Schaefer, Samuel; Dahunsi, Debolanle; Westlake, Camille S.; Crowe, James E.; Carnahan, Robert H.; Rathmell, Jeffrey C.; Bonami, Rachel H.; Georgiev, Ivelin S.; Irish, Jonathan M.
title: Single-Cell Profiling of the Antigen-Specific Response to BNT162b2 SARS-CoV-2 RNA Vaccine
date: 2021-07-28
journal: bioRxiv
DOI: 10.1101/2021.07.28.453981
sha: 76834ce157ac4e85131fd876899038675c90dadb
doc_id: 756035
cord_uid: 6soui290

RNA-based vaccines against SARS-CoV-2 are critical to limiting COVID-19 severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. We used single-cell technologies to identify and characterized antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 in longitudinal samples from a cohort of healthy donors. Mass cytometry and machine learning pinpointed a novel expanding, population of antigen-specific non-canonical memory CD4+ and CD8+ T cells. B cell sequencing suggested progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlated with eventual SARS-CoV-2 IgG and a donor lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms reveal an antigen-specific cellular basis of RNA vaccine-based immunity. ONE SENTENCE SUMMARY Single-cell profiling reveals the cellular basis of the antigen-specific response to the BNT162b2 SARS-CoV-2 RNA vaccine.

B-cell lymphoma 6 to date (1). B cells, T cells, and other leukocytes undergo significant shifts upon SARS-CoV-2 infection that may contribute to anti-viral immunity and protective antibodies (2) (3) (4) (5) (6) . The development of viral neutralizing antibodies following infection has been associated with Th1-like CXCR3+HLADR+PD1+ CD8 and CD4 cells and circulating CXCR3+ CD4 T follicular helper (cTfh) cells and CD4+ CD38+ HLA-DR+ T cell abundance (3, 4) . While therapies, such as dexamethasone (7), baracitinib (8) , tocilizumab (9) , and neutralizing monoclonal antibodies (10, 11) have emerged as treatments for severe COVID-19 disease, preventive measures to develop coronavirus immunity on a population-scale are of upmost importance . To address this need, vaccines formulated with the pre-fusion stabilized SARS-CoV-2 Spike (S) protein were developed to induce protection from COVID-19 infection or development of severe disease (12) (13) (14) (15) (16) . Globally, nearly 4 billion doses of various COVID-19

vaccines have been administered (1).

Messenger RNA (mRNA)-based vaccines represent a promising new class of vaccines that offer protection from COVID-19 as well as potentially a wide range of emerging infectious diseases (17, 18) . These vaccines introduce the minimal genetic information to express viral antigens of interest (17) and mimic natural infection of RNA viruses, such as SARS-CoV-2 (19) . Several groups have explored the immunologic response to SARS-CoV-2 mRNA vaccines using both systems (20) and T-cell centric approaches (21) (22) (23) . From these studies, elevated identified novel expanding and metabolically active S protein-specific, non-canonical memory CD4 and CD8 T cell populations following vaccination that were confirmed as antigen specific. In parallel, coronavirus S protein-binding B cells were characterized with single-cell LIBRA-seq and RNA-seq to establish the evolution of cross-reactive to antigen specific B cells with public antibody sequences over time. These antigen-specific T and B cells correlated and associated with a long-lasting IgG response that was lacking in a donor who subsequently experienced a breakthrough infection. These cell and antibody associations may drive further efforts to predict vaccine effectiveness and identify mechanisms of protection.

The effect of BNT162b2 immunization was first explored on recipient T cell populations in a cohort of ten healthy donors who had not been previously infected with SARS-CoV-2. Donors had an average age of 41.8 ± 6.3 years. Six donors were male, and nine donors identified as having Caucasian ancestry. By collecting longitudinal peripheral blood samples before immunization, one week following booster immunization (day 28), and at an additional time point approximately three months later (day 105), we captured signatures of the initial immune response to BNT162b2 as well as lasting immunity (Supplemental Figure 1A) . T cell populations in the pre-vaccination and post-boost samples were initially examined by mass cytometry using a Helios cytometry by time-of-flight mass spectrometry (CyTOF) instrument and a panel of antibodies focused on T cell immune and metabolic phenotypes (Supplemental Table 1 ). Cell density plots of concatenated data of T cells from the pre-vaccination and postboost samples were first visualized using t-SNE dimensionality reduction ( Figure 1A) . Because MHC-peptide tetramer staining reagents were not available to directly identify expanding antigen specific S protein-reactive CD4 and CD8 T cells, data were analyzed using the recently developed T-REX machine learning algorithm (24) . By comparing changes in small k-nearest neighbor cell groupings, T-REX specifically identifies populations of cells with the greatest degrees of expansion or contraction from pre-to post-vaccination. In the case of viral infections, these expanding populations were preferentially enriched for virus-specific cells (24) .

This approach sets aside the majority of peripheral blood T cells, which were unchanged, to instead focus on those populations of T cells in phenotypically distinct regions whose abundance increased or decreased by ≥95% in the initial 7 days following vaccination.

T-REX revealed related populations of CD4 and CD8 T cells that expanded by ≥95%

following vaccination and one population each that contracted by ≥95% ( Figure 1B) . Across the donor cohort, changes in abundance of these cell populations were widely, but not universally, shared in T-REX of individual donor samples (Supplemental Figure 2A) . Marker Enrichment Modeling (MEM) and specific antibody staining patterns established the protein marker expression patterns characteristic of each population ( Figure 1C, Figure 1D ). The most expanded populations were CCR7-CD45R0+ CD4 and CD8 T cell populations that were negative for CXCR5, positive for PD-1, and highly co-expressed CD38 and ICOS ( Figure   1E ). Consistent with extensive expansion, these cells were the most proliferative cell subset based on Ki67 positivity, and were highly metabolically active, based on co-expression of transporters for glucose (GLUT1), amino acids (CD98), and lipids (CPT1a). These cells, thus, reflect non-canonical activated memory T cells. The T cell populations with decreasing abundance, in contrast, were CD45RA+ ICOS-and phenotypically characterized as naïve.

The selective enrichment of PD1+ICOS+CD38+CXCR5-CD4 and CD8 T cells following vaccination suggested these cells may specifically recognize viral S protein. To test SARS-CoV-2 specificity and further characterize the CD38+ICOS+ memory T cell populations, fluorescence flow cytometry and FACS were used to characterize cell subsets. Similar to mass cytometry, ICOS+CD38+ CD4 and CD8 cells were present in pre-immunized samples as approximately 1-2% of each T cell subset (Figure 2A) . Importantly, these cell populations To evaluate the antigen specific T cell response to SARS-CoV-2 S antigen, PBMCs from each time point were incubated with recombinant SARS-CoV-2 S protein or T cell activation beads and cytokine responses were measured in culture supernatant. Interestingly, post-boost samples clustered into responders and non-responders for IFN-γ and TNFα production (Supplemental Figure 3A, B) . IL-2, IL-4 and IL-17a were unaffected by stimulation of T cells with S protein (Supplemental Figure 3C-E) . Furthermore, a subset of samples from all three time points produced IL-6 in response to S protein, suggesting cross-reactivity with prior coronavirus exposures (Supplemental Figure 3F ). Especially notable were the late collection time points in which three donor samples produced more IL-6 when incubated with S protein than positive control polyclonal CD3/2/28 bead stimulated samples. Samples from day 105 also showed an increase in the frequency of IL-2 and IFN-γ producing CD8 T cells when restimulated with S protein after 11 days of culture (Supplemental Figure 3G ).

To directly test antigen specificity and further characterize the expanding T cells identified by T-REX, ICOS+CD38+ and ICOS lo CD38 lo cells were isolated from four donor samples using FACS and protein markers from T-REX populations. Cells were then labeled with CellTrace Violet and stimulated with CD3-depleted autologous PBMCs with or without SARS-CoV-2 S peptide pool (Supplemental Figure 3H ). ICOS+CD38+ CD4 T cells produced IFN-γ, IL-2, and TNFα in response to SARS-CoV-2 S peptide stimulation, while peptidestimulated ICOS lo CD38 lo T cells from the same sample failed to produce cytokines ( Figure 2D ).

CD8 ICOS+CD38+ T cells also produced significant IL-2 in response to peptide stimulation, but had decreased Granzyme B, suggesting potential post-activation degranulation. Both CD4 and CD8 ICOS+CD38+ populations showed evidence of metabolic reprogramming, as mTORC1 pathway activity as shown by levels of phospho-S6 trended higher in each donor (Supplemental Figure 3I) . Expression of the glucose transporter GLUT1, however, was unchanged or modestly reduced (Supplemental Figure 3J) . Although the ICOS+CD38+ CD4 and CD8 T cells lacked CXCR5, the expanding S protein-specific cells shared some characteristics with circulating T follicular helper cells (cTfh) as a portion of these cells expressed the Tfh characteristic transcription factor BCL6 ( Figure 2E ).

The B cell response to BNT162b2 was next examined using a similar approach as Figure 5A ). These patterns were generally consistent across the S proteins from circulating variants of concern (VOCs). Interestingly, an IgA vaccine response was also evident in most donors. Most donors also displayed signatures of preexisting immunity to the coronaviruses HCoV-OC43 and HCoV-HKU1, consistent with their endemic nature among the human population (27) . Antibody levels to these endemic strains, however, did not change following BNT162b2 vaccination. Next, using a replication-competent (Figures 4C and 4D) . Therefore, pre-existing coronavirus antibody did not appear to be a major determinant for defining the antibody response to 

CoV-2 and the closely related SARS-CoV were substantially increased at days 14 and 42.

Interestingly, while the levels of SARS-CoV-2-reactive B cells peaked at day 14 for both the IgG and IgA isotypes, the IgA levels were reduced to near baseline at day 42, while IgG levels at day 42 were decreased but clearly present. Changes in B cell cross-reactivity and isotype abundance were also evident when analyzed at the single-cell level using antigen-specific LIBRA-seq scores as a metric for comparing antigen specificity evolution ( Figure 5E ). These results suggest an evolution in SARS-CoV-2 antibody specificity and cross-reactivity in response to BNT162b2 vaccination, supporting a model of immune progression from early prevalence of endemic cross-reactive IgM to more highly selective SARS-CoV-2 IgG production over time.

The LIBRA-seq B cell receptor sequence and antigen specificity data was further Figure 6F ). These plasmablasts did not have significant LIBRA-seq cross-reactivity scores for endemic coronaviruses but included plasmablasts selective for SARS-CoV-2 or with dual selectivity for SARS-CoV-2 and SARS-CoV ( Figure 6G ).

To test associations of the antigen-specific CD4, CD8 T and B cell plasmablast populations and the antibody response to BNT162b2, the abundance of ICOS+CD38+ CD4 or CD8 and plasmablast cell populations were compared across donors ( Figure 7A ). The CD4, CD8, and plasmablast populations were associated with each other to support a coordinated response. Further, plasmablast frequencies showed a trend towards significance and ICOS+CD38+ CD8 T cells correlated with eventual levels of SARS-CoV-2 specific IgG. The three donors with the lowest levels of neutralizing antibodies (red and pink) also had the lowest numbers of ICOS+CD38+ CD8 cells. Intriguingly, donor 17 (red) had the lowest abundance of ICOS+CD38+ CD8 cells and plasmablasts and was subsequently infected with SARS-CoV-2. While donor 17 represents a single case, the observation supports the idea that BNT162b2induced expansion of non-canonical memory ICOS+CD38+ CD4 and CD8 T cells may be critical to drive plasmablast expansion, antibody production, and protective immunity to SARS-

We next examined the antibody sequence relationships of B cells elicited by vaccination from the longitudinally collected donor. Interestingly, a cluster of antibody heavy chain sequences, referred to as cluster 720, was found to exhibit high similarity with public antibody sequences from the CoV AbDab database (35) appeared as IgG and IgA isotypes ( Figure 7B ). To characterize individual antibodies from cluster 720, we expressed heavy-and light-chain pairs as recombinant IgG for two members from diverse branches in the tree, 720-3 (using IGKV3-15) and 720-17 (using IGKV3-20), and tested for reactivity against coronavirus S proteins. Consistent with their LIBRA-seq scores, these antibodies were cross-reactive to SARS-CoV-2 and the closely related SARS-CoV but showed no reactivity to the endemic coronaviruses HCoV-HKU1 and HCoV-OC43 ( Figure 7D ).

Epitope mapping experiments revealed that these two antibodies were specific to the S2 domain of spike ( Figure 7E, Supplemental Figure 9 ).

Here Antigen-specific CD4 and CD8 T cells that drive the vaccine response were phenotypically distinct from canonical peripheral blood T cell populations. SARS-CoV-2 S protein specific CD4+ PD1+CD38+ICOS+CXCR5-did resemble CD38+ICOS+CXCR5-T cells previously identified following SARS-CoV-2 infection (4) and a prior study of BNT162b2 vaccination (37), but had several previously undescribed characteristics. A subset of these antigen specific cells did express the Tfh transcription factor BCL6, and PD1+ICOS+ cTfh cells have been previously associated with vaccine responses (38) (39) (40) . However, they lacked CXCR5 that is characteristic of Tfh and CD4 T cells produced IL-2 and TNFα when antigen-stimulated.

The CD38+ICOS+ antigen specific population resemble rhinovirus-specific tissue homing memory CD4+ T cells that express CCR5 and CD38 and are associated with transcription factor TBET (41) and may reflect extrafollicular PD-1+CXCR5-CD4+ T cells observed following viral infections (42) . Alternatively, these cells may include memory Tfh or IFN-γ-producing Thf1 populations that have down regulated CXCR5 in circulation as they enter a memory pool (43) (44) (45) . CD38+ICOS+CXCR5-cells thus appear as antigen-experienced memory T cell that may be tissue homing and arise from multiple T cell subtypes, such as Th1 and Tfh CD4 T cells to be poised for long-lived anti-viral memory responses.

In addition to induction of SARS-CoV-2 neutralizing antibodies (46), cytotoxic T cell responses also contribute to vaccine-mediated protection. CD8+ T cells support life-long immunity against influenza (47), EBV (48) , and CMV (49), and induction of a robust CD8+ T cell response is an emerging focus in vaccine development (50, 51) . Here, we identified antigenspecific CD8+ PD1+CD38+ICOS+CXCR5-cytotoxic memory T cell population capable of cytokine production in response to S antigenic peptides. Similar CD8+ T cells are induced by yellow-fever vaccination (52) Optical density at 450 nm (y-axis) is depicted as a function of antibody concentration (x-axis). 

Human Subjects Information: IRB approval was obtained (VUMC 191562) . Informed consent was obtained, and a baseline health questionnaire was also completed. Simple phlebotomy was performed either pre-vaccine (day 0), day 28-30, and day 95-100 OR pre-vaccine and days 8, 14, and 42 after initial BNT162b2 vaccination using sodium citrate mononuclear cell preparation (CPT) tubes. All participants received two doses of vaccine 21 days apart.

CPT tubes were spun at 1600 RCF for 20 minutes. The plasma layer was carefully removed, transferred to a conical vial, spun at 600g for 10 minutes, and the supernatant transferred to microtubes in 1 mL aliquots. Plasma was stored at -80°C until further use. Buffy coat was divided amongst two clean conical tubes. CPT tubes were rinsed with 1 mL PBS (Gibco), and the total volume in the conical was increased to 15cc. Cells were pelleted at 600g for 10 minutes. Cell pellets were combined and washed with 10cc PBS, and then cells were pelleted again at 600g for 10 minutes. PBS was discarded, and the pellet was re-suspended in 3mL ACK buffer (Gibco) for 5 minutes. 10 mL of PBS was added to the ACK cell suspension, and cells were pelleted for 10 minutes at 600g. Cells were resuspended in PBS, strained through a cell strainer (Falcon) and counted using an ACT Diff hematology analyzer (Beckman Coulter).

Cells were pelleted by centrifugation at 600g for 10 minutes and resuspended in heatinactivated FBS (Gibco) containing 10% DMSO (Sigma-Aldrich) at a concentration of 5 million cells/mL in cryovials (Nalgene). Cryovials were frozen overnight to -80°C using Mr. Frosty freezing containers (Nalgene) and then transferred to liquid nitrogen for long-term storage.

Metal-tagged antibodies were purchased from Fluidigm. Cell labeling and mass cytometry analysis were performed as previously described (1, 2) . Briefly, cells were incubated with a viability reagent (Cell ID Intercalator-Rh; Fluidigm), per the product literature. Then cells were washed in PBS without calcium or magnesium (Gibco, Thermo Fisher Scientific) containing 1% BSA (Thermo Fisher Scientific) and stained in 50 μL PBS and BSA 1%-containing antibody cocktail for extracellular targets. Cells were stained for 30 minutes at room temperature using the antibodies listed in Supplemental Table 3 Cytokines measured from PBMC supernatants were collected after 4 days of incubation with S protein and concentrations were predicted using a standard curve in the LEGENDplex assay (Miltenyi Biotec 741028).

Group comparisons were performed using GraphPad Prism version 9.0. Populations were compared using Mann-Whitney U tests. P values less than 0.05 were considered statistically significant.

Single cell profiling of antigen specific B cells

Plasmids encoding residues 1-1208 of the SARS-CoV-2 spike with a mutated S1/S2 cleavage site, proline substitutions at positions 817, 892, 899, 942, 986 and 987, and a C-terminal T4fibritin trimerization motif, an 8x HisTag, and a TwinStrepTag (SARS-CoV-2 S HP); 1-1208 of the SARS-CoV-2 spike with a mutated S1/S2 cleavage site, proline substitutions at positions

Constructs containing an Avi-tag (ZM197 Env and HA NC99) were biotinylated using the sitespecific biotinylation kit according to manufacturer instructions (Avidity LLC.) All other antigens not containing an Avi-tag were non-specifically biotinylated using the EZ-Link Sulfo-NHS-Biotin kit at a 50:1 biotin:protein molar ratio.

We used oligos that possess 15 bp antigen barcode, a sequence capable of annealing to the template switch oligo that is part of the 10X bead-delivered oligos, and contain truncated For each antigen, a unique DNA barcode was directly conjugated to the antigen itself. In particular, 5'amino-oligonucleotides were conjugated directly to each antigen using the Solulink Protein-Oligonucleotide Conjugation Kit (TriLink cat no. S-9011) according to manufacturer's instructions. Briefly, the oligo and protein were desalted, and then the amino-oligo was modified with the 4FB crosslinker, and the biotinylated antigen protein was modified with S-HyNic. Then, the 4FB-oligo and the HyNic-antigen were mixed together. This causes a stable bond to form between the protein and the oligonucleotide. The concentration of the antigen-oligo conjugates was determined by a BCA assay, and the HyNic molar substitution ratio of the antigen-oligo conjugates was analyzed using the NanoDrop according to the Solulink protocol guidelines.

AKTA FPLC was used to remove excess oligonucleotide from the protein-oligo conjugates, which were also verified using SDS-PAGE with a silver stain. Antigen-oligo conjugates were also used in flow cytometry titration experiments. 

Single-cell suspensions were loaded onto the Chromium Controller microfluidics device (10X Genomics) and processed using the B-cell Single Cell V(D)J solution according to manufacturer's suggestions for a target capture of 10,000 B cells per 1/8 10X cassette, with minor modifications in order to intercept, amplify and purify the antigen barcode libraries as previously described (9) .

We utilized our previously described pipeline to use paired-end FASTQ files of oligo libraries as input, process and annotate reads for cell barcode, UMI, and antigen barcode, and generate a cell barcode -antigen barcode UMI count matrix (10) . BCR contigs were processed using Cell Ranger (10X Genomics) using GRCh38 as reference. Antigen barcode libraries were also processed using Cell Ranger (10X Genomics). The overlapping cell barcodes between the two libraries were used as the basis of the subsequent analysis. We removed cell barcodes that had only non-functional heavy chain sequences as well as cells with multiple functional heavy chain sequences. Additionally, we aligned the BCR contigs (filtered_contigs.fasta file output by Cell Ranger, 10X Genomics) to IMGT reference genes using HighV-Quest (11) . The output of HighV-Quest was parsed using Change-O (12) . and merged with an antigen barcode UMI count matrix. Finally, we determined the LIBRA-seq score for each antigen in the library for every cell as previously described (9) .

Cells were filtered based on multiple criteria for further analysis. Cells were only included if the sum of all antigen UMI counts for a particular cell barcode was greater than 4. All cells that met these criteria from pre, day 8, day 14, and day 42 time points were combined. Then, we removed cells from the dataset that had multiple heavy chains or multiple light chains associated with a single cell barcode. Then, LIBRA-seq scores were generated (8) . Briefly, a pseudocount of 1 was added to each antigen UMI count, and then centered-log ratios (CLR) were calculated for each antigen UMI count for each cell. Then, an antigen-wise z-score transformation was applied. After performing the LIBRA-seq score calculation, cells were filtered out if they fulfilled any of the following criteria: (1) Max UMI among all antigens less than or equal to 30, (2) ZM197 UMI counts greater than or equal to 30, (3) HA UMI counts greater than or equal to 30, (4) MERS UMI counts greater than 10 times the max UMI of other CoV in that cell, or (5) 10 times ZM197 UMI counts greater than the max UMI of non-MERS CoV in that cell. Supplemental Figure 6B and Figure 7B were generated using the pre-filtered data. Figure 7 and Figure 5C -E used post-filtered data.

Using pre-filtered data, cells from multiple timepoints were combined together to identity highly similar antibody sequences between timepoints. Single-linkage clustering was performed using Change-O (12) with the criteria of same VH-and JH-gene usage, same junction and CDRH3 length and 80% CDRH3 nucleotide sequence identity.

For the selected cluster 720, phylogenetic analysis was performed to understand the heavy chain sequence similarities. The sequences from cluster 720 used IGHV3-30-3 and IGHJ4 genes and included 18 members from 3 different post-vaccination timepoints. The cluster 720 heavy chain sequences were compared to sequences from the Coronavirus antibody database (CoV-AbDab) to identify antibodies with high sequence similarity (based on same VH-and JHgene usage and 70% CDRH3 amino acid sequence identity) to at least one member of cluster 720. This resulted in the identification of 14 antibody sequences that fulfilled the similarity criteria with the cluster 720 sequences. To generate a phylogenetic tree of these public sequences, the tree was rooted on a putative germline sequence that was generated using the IGHV3-30-3 gene, a consensus CDRH3 sequence based on the cluster 720 members with 100% germline gene identity, and the IGHJ4 J-gene for framework 4. The tree was visualized using the Dendroscope tool (13) .

Plasma Serology ELISAs 100 µL of antigen was plated at a concentration of 500 ng/µL in PBS overnight at 4ºC. The following day plates are washed with 1X PBS and 0.1% Tween-20 (PBS-T) and then blocked with 5% non-fat dry milk (NFDM). After an hour blocking at 1 hour at RT, plates are washed 3X with PBS-T and plasma is diluted in 1% NFDM PBS-T at a top concentration of 1:67 followed by 7 3-fold dilutions. The plates were incubated at RT for 1 hour and then washed three times in PBST. The secondary antibody was added in 1% NFDM in PBS-T to the plates, which were incubated for one hour at RT. Plates were washed three times with PBS-T and then developed by adding TMB substrate to each well. The plates were incubated at room temperature for ten minutes, and then 1N sulfuric acid was added to stop the reaction. Plates were read at 450 nm.

In brief, 100uL of plasma samples are heat inactivated at 570C for 1hr and starting at 1:25 dilution eight 2-fold serial dilutions were made with DMEM supplemented with 2% FBS. To determine neutralizing activity of plasma/serum , we used real-time cell analysis (RTCA) assay on an xCELLigence RTCA MP Analyzer (ACEA Biosciences Inc.) that measures virus-induced cytopathic effect (CPE). Briefly, 50 μL of cell culture medium (DMEM supplemented with 2% FBS) was added to each well of a 96-well E-plate using a ViaFlo384 liquid handler (Integra Biosciences) to obtain background reading. A suspension of 18,000 Vero cells in 50 μL of cell culture medium was seeded in each well, and the plate was placed on the analyzer.

Measurements were taken automatically every 15 min, and the sensograms were visualized using RTCA software version 2.1.0 (ACEA Biosciences Inc). VSV-SARS-CoV-2 (0.01 MOI, ~120 PFU per well) was mixed 1:1 with a dilution of plasma/serum in a total volume of 100 μL using DMEM supplemented with 2% FBS as a diluent and incubated for 1 h at 37°C in 5% CO2.

At 16 h after seeding the cells, the virus-plasma/serum mixtures were added in replicates to the cells in 96-well E-plates. Triplicate wells containing virus only (maximal CPE in the absence of mAb) and wells containing only Vero cells in medium (no-CPE wells) were included as controls.

Plates were measured continuously (every 15 min) for 48 h to assess virus neutralization.

Normalized cellular index (CI) values at the endpoint (48 h after incubation with the virus) were determined using the RTCA software version 2.1.0 (ACEA Biosciences Inc.). Results are expressed as percent neutralization in a presence of respective plasma/serum relative to control wells with no CPE minus CI values from control wells with maximum CPE. RTCA IC50 values were determined by nonlinear regression analysis using Prism software.

Single-cell analysis was performed using Seurat v4.0.0 (14) . Cells with fewer than 200 RNA features that contained greater than 10% mitochondrial genes were removed. Immunoglobulin VH, Vκ, and Vλ genes were removed prior to UMAP clustering of RNAseq data to prevent them from driving transcriptionally-defined clusters. LIBRA-seq scores were used to identify SARS-CoV-2-binding B cells. B cell subset identities were assigned to clusters based on transcriptional profiles that were consistent with other studies defining these populations. The Seurat FindMarkers function, which uses a non-parametric Wilcoxon rank sum test, was used to identify differentially expressed genes between SARS-CoV-2-binding B cells and non-SARS-CoV-2-binding B cells within each transcriptionally-defined cluster. VDJ data were processed via CellRanger, IMGT/HighV-QUEST, and CHANGE-O, as outlined above to assign V genes, isotypes, and calculate percent VH somatic hypermutation.

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Inactivated influenza vaccine formulated with single-stranded RNAbased adjuvant confers mucosal immunity and cross-protection against influenza virus infection

BNT162b2 vaccination in heart transplant recipients: Clinical experience and antibody response

Impaired humoral and cellular immunity after SARS-CoV-2 BNT162b2 (tozinameran) prime-boost vaccination in kidney transplant recipients

Immunogenicity of SARS-CoV-2 BNT162b2 vaccine in solid organ transplant recipients

Association of clinical factors and recent anticancer therapy with COVID-19 severity among patients with cancer: a report from the COVID-19 and Cancer Consortium

Immunogenicity and safety of the BNT162b2 mRNA COVID-19 vaccine in adult patients with autoimmune inflammatory rheumatic diseases and in the general population: a multicentre study

The effect of methotrexate and targeted immunosuppression on humoral and cellular immune responses to the COVID-19 vaccine BNT162b2: a cohort study

Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks

SARS-CoV-2 S HP Beta); 1-1208 of the SARS-CoV-2 spike with a mutated S1/S2 cleavage site, proline substitutions at positions 817, 892, 899, 942, 986 and 987, as well as mutations 69-70del, Y144del, N501Y, A570D, P681H, and a C-terminal T4-fibritin trimerization motif, an 8x HisTag, and a TwinStrepTag (SARS-CoV-2 S HP Alpha); residues 1-1190 of the SARS-CoV spike with proline substitutions at positions 968 and 969, and a Cterminal T4-fibritin trimerization motif

residues 1-1291 of the MERS-CoV spike with a mutated S1/S2 cleavage site, proline substitutions at positions 1060 and 1061, and a C-terminal T4-fibritin trimerization motif

8x HisTag, and a TwinStrepTag

HCoV-HKU1 S-2P); residues 1-1278 of the HCoV-OC43 spike with proline substitutions at positions 1070 and 1071, and a C-terminal T4-fibritin trimerization motif, an 8x HisTag, and a TwinStrepTag (HCoV-OC43 S-2P); were transiently transfected into FreeStyle293F cells (Thermo Fisher) using polyethylenimine

Transfected supernatants were harvested after five days of expression

AviTag and recombinant NC99 HA protein consisting of the HA ectodomain with a point mutation at the sialic acid-binding site (Y98F) to abolish non-specific interactions, a T4 fibritin foldon trimerization domain, AviTag, and hexahistidine-tag, were expressed in Expi 293F cells using polyethylenimine transfection reagent and cultured. FreeStyle F17 expression medium supplemented with pluronic acid and glutamine was used. The cells were cultured at 37°C with 8% CO2 saturation and shaking. After 5-7 days, cultures were centrifuged and supernatant was filtered and run over an affinity column of agarose bound Galanthus nivalis lectin. The column was washed with PBS and antigens were eluted with 30 mL of 1M methyl-a-Dmannopyranoside

High-Dimensional Analysis of Acute Myeloid Leukemia Reveals Phenotypic Changes in Persistent Cells during Induction Therapy

Myelodysplastic Syndrome Revealed by Systems Immunology in a Melanoma Patient Undergoing Anti-PD-1 Therapy

Normalization of mass cytometry data with bead standards

B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression

Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy. bioRxiv

Characterizing cell subsets using marker enrichment modeling

Physiologic Medium Rewires Cellular Metabolism and Reveals Uric Acid as an Endogenous Inhibitor of UMP Synthase

High-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity

Cross-reactive coronavirus antibodies with diverse epitope specificities and Fc effector functions

IMGT((R)) tools for the nucleotide analysis of immunoglobulin (IG) and T cell receptor (TR) V-(D)-J repertoires, polymorphisms, and IG mutations: IMGT/V-QUEST and IMGT/HighV-QUEST for NGS

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks

Spatial reconstruction of single-cell gene expression data

We thank members of the Vanderbilt Center for Immunobiology for input and Summer