Biology subjects

Yermanos, A.

Publications and source records attributed to Yermanos, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Contrastive learning for antibody-antigen sequence-to-specificity prediction

Predicting which antibodies bind to which antigens directly from primary amino acid sequences remains a major challenge, as no current method can reliably determine this specificity at both a repertoire and proteome scale. Structure-based protein design frameworks can propose antibody binders to specified antigenic epitopes, but they do not solve the "sequence-to-specificity" task of mapping antibodies to cognate epitopes, and vice versa. Here, we introduce CALM (Cross-attention Adaptive Immune Receptor-Antigen Language Model), a dual-encoder plus cross-attentive decoder architecture that treats antibody-antigen recognition as molecular translation. Using contrastive learning, antigen and antibody encoders learn a shared embedding space that aligns cognate epitope-paratope binding pairs. CALM-1.0 is trained and evaluated on 4,138 curated antibody-antigen pairs obtained from the PDB-derived structural antibody database (SAbDab). On a leakage-controlled test split drawn from sequence clusters at 80% identity and unseen during training, CALM-1.0 achieves a mean top-1 retrieval (R@1) of 7%, with consistent performance across both directions (Ab[->]Ag and Ag[->]Ab). CALM establishes a foundation for bidirectional antibody-antigen sequence-to-specificity prediction with the potential to unify retrieval and generative design.

immunology↗

bioRxiv2025

Dissecting serum polyclonal antibody escape to SARS-CoV-2 variants by deep mutational learning

The rapid emergence of SARS-CoV-2 variants harboring multiple receptor-binding domain (RBD) mutations continues to challenge the efficacy of vaccines and antibody therapeutics. While deep mutational scanning (DMS) has been instrumental in mapping single-mutation effects on antibody binding and immune escape, it remains limited in its ability to assess combinatorial mutational landscapes. Here, we extend deep mutational learning (DML), a method integrating combinatorial mutagenesis, yeast surface display, deep sequencing, and machine learning, to analyze serum polyclonal antibody escape. Human sera from COVID-19-vaccinated individuals were screened against diverse RBD variant libraries, validating 300 serum-variant interactions across 10 individuals by comparing predicted and observed binding to 30 RBD variants, confirming accurate mapping of binding and escape profiles. Model performance remained consistent across machine learning architectures, suggesting that serum binding and escape are governed by distinct, localized RBD sequence features. Notably, escape profiles were highly individualized, whereas binding signatures were more conserved, reflecting convergent epitope targeting. This approach highlights the potential of DML to generalize beyond observed RBD variants, assess cohort-specific immune breadth, and inform vaccine and therapeutic design in the face of viral evolution.

immunology↗

bioRxiv2025

Delineating inter- and intra-antibody repertoire evolution with AntibodyForests

MotivationThe rapid advancements in immune repertoire sequencing, powered by single-cell technologies and artificial intelligence, have created unprecedented opportunities to study B cell evolution at a novel scale and resolution. However, fully leveraging these data requires specialized software capable of performing inter- and intra-repertoire analyses to unravel the complex dynamics of B cell repertoire evolution during immune responses. ResultsHere, we present AntibodyForests, software to infer B cell lineages, quantify inter- and intra-antibody repertoire evolution, and analyze somatic hypermutation using protein language models and protein structure. Availability and implementationThis R package is available on CRAN (1) and Github at https://github.com/alexyermanos/AntibodyForests, a vignette is available at https://cran.case.edu/web/packages/AntibodyForests/vignettes/AntibodyForests_vignette.html

bioinformatics↗

bioRxiv2024

Protein language model pseudolikelihoods capture features of in vivo B cell selection and evolution

B cell selection and evolution play crucial roles in dictating successful immune responses. Recent advancements in sequencing technologies and deep-learning strategies have paved the way for generating and exploiting an ever-growing wealth of antibody repertoire data. The self-supervised nature of protein language models (PLMs) has demonstrated the ability to learn complex representations of antibody sequences and has been leveraged for a wide range of applications including diagnostics, structural modeling, and antigen-specificity predictions. PLM-derived likelihoods have been used to improve antibody affinities in vitro, raising the question of whether PLMs can capture and predict features of B cell selection in vivo. Here, we explore how general and antibody-specific PLM-generated sequence pseudolikelihoods (SPs) relate to features of in vivo B cell selection such as expansion, isotype usage, and somatic hypermutation (SHM) at single-cell resolution. Our results demonstrate that the type of PLM and the region of the antibody input sequence significantly affect the generated SP. Contrary to previous in vitro reports, we observe a negative correlation between SPs and binding affinity, whereas repertoire features such as SHM and isotype usage were strongly correlated with SPs. By constructing evolutionary lineage trees of B cell clones from human and mouse repertoires, we observe that SHMs are routinely among the most likely mutations suggested by PLMs and that mutating residues have lower absolute likelihoods than conserved residues. Our findings highlight the potential of PLMs to predict features of antibody selection and further suggest their potential to assist in antibody discovery and engineering. Key points- In contrast to previous in vitro work (Hie et al., 2024), we observe a negative correlation between PLM-generated SP and binding affinity. This contrast can be explained by the inherent antibody germline bias posed by PLM training data and the difference between in vivo and in vitro settings. - Our findings also reveal a considerable correlation between SPs and repertoire features such as the V-gene family, isotype, and the amount of SHM. Moreover, labeled antigen-binding data suggested that SP is consistent with antigen-specificity and binding affinity. - By reconstructing B cell lineage evolutionary trajectories, we detected predictable features of SHM using PLMs. We observe that SHMs are routinely among the most likely mutations suggested by PLMs and that mutating residues have lower absolute likelihoods than conserved residues. - We demonstrate that the region of antibody sequence (CDR3 or full V(D)J) provided as input to the model, as well as the type of PLM used, influence the resulting SPs.

bioinformatics↗

bioRxiv2024

Clonally expanded IgG antibody-secreting cells preferentially target influenza nucleoprotein following homologous and heterologous infections

Infection with influenza virus remains a significant global health concern due to its ability to acquire mutations at key antigenic sites to escape antibody recognition. While germinal center (GC) and memory B cells have been well studied following influenza infection, the clonal dynamics of antibody secreting cells (ASCs), particularly those within the bone marrow (BM) niche that are responsible for serum immune protection, remain poorly understood. Here, we combine single-cell RNA (scRNA) and B cell receptor (BCR) sequencing to characterize individual ASCs following various Influenza exposure histories. We find that BM repertories are populated by highly expanded and class-switched ASCs following Influenza infection with similar transcriptional and repertoire characteristics regardless of homologous or heterologous infection histories. By combining single-cell analysis with monoclonal antibody expression and characterization, we find that a large proportion of the expanded IgG-, but not IgA-, ASC repertoire demonstrates specificity to influenza nucleoprotein (NP). Together, our data reveal the complex relationship between BM ASC repertoires, mucosal humoral immune responses, and BCR antigen specificity during influenza infection.

immunology↗

bioRxiv2023

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Blocking inhibitory receptors like PD-1 and CTLA-4 has revolutionized cancer treatment in recent years. However, despite major successes in melanoma and lung cancer, the majority of cancer types are not responsive to these immunotherapies. As such, there is an ongoing need for the identification of novel inhibitory receptors as drug targets. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies have estimated that our genome contains over 1600 ITIM- bearing transmembrane proteins. However, further testing and development of this large number of candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. To assist in the selection of novel inhibitory receptor as therapeutic targets, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify 51 known and 390 putative inhibitory receptors. Using publicly available transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across a wide variety of immune cells, and we found cell type-specific patterns in expression of these receptors. We used our pipeline to study inhibitory receptor expression patterns in single cell transcriptomics data of tumour infiltrating T cells. We determined that putative immune inhibitory receptors were expressed differentially in CD4+ and CD8+ T cell subsets, including exhausted CD8+ T cells and CD4+ memory T cells, which could allow for subset-specific targeting. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

immunology↗

bioRxiv2022

Asymmetric cell division safeguards memory CD8 T cell development

The strength of T cell receptor (TCR) stimulation and asymmetric distribution of fate determinants are both implied to affect T cell differentiation. Here, we uncovered asymmetric cell division (ACD) as a safeguard mechanism for memory CD8 T cell generation specifically upon strong TCR stimulation. Using live imaging approaches, we found that strong TCR stimulation induced elevated ACD rates and subsequent single cell derived colonies comprised both effector and memory precursor cells. The abundance of memory precursor cells emerging from a single activated T cell positively correlated with first mitosis ACD. Accordingly, preventing ACD by inhibition of PKC{zeta} during the first mitosis upon strong TCR stimulation markedly curtailed the formation of memory precursor cells. Conversely, no effect of ACD on fate commitment was observed upon weak TCR stimulation. Our data provide new mechanistic insights into the role of ACD for CD8 T cell fate regulation upon different activation conditions.

immunology↗

bioRxiv2022

ePlatypus: an ecosystem for computational analysis of immunogenomics data

The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is a web-based platform and provides programming tutorials and an integrative database that elucidates selection patterns of adaptive immunity. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Accessibilityhttps://alexyermanos.github.io/Platypus/index.html

immunology↗

bioRxiv2022

Persistent virus-specific and clonally expanded antibody secreting cells respond to induced self antigen in the CNS

B cells contribute to the pathogenesis of both cellular- and humoral-mediated central nervous system (CNS) inflammatory diseases through a variety of mechanisms. In such conditions, B cells may enter the CNS parenchyma and contribute to local tissue destruction. It remains unexplored, however, how infection and autoimmunity drive transcriptional phenotypes, repertoire features, and antibody functionality. Here, we profiled B cells from the CNS of murine models of intracranial (i.c.) viral infections and autoimmunity. We identified a population of clonally expanded, antibody secreting cells (ASCs) that had undergone class-switch recombination and extensive somatic hypermutation following i.c. infection with attenuated lymphocytic choriomeningitis virus (rLCMV). Recombinant expression and characterisation of these antibodies revealed specificity to viral antigens (LCMV glycoprotein GP), correlating with ASC persistence in the brain weeks after resolved infection. Furthermore, these virus-specific ASCs upregulated proliferation and expansion programs in response to the conditional and transient induction of the LCMV GP as a neo-self antigen by astrocytes. This class-switched, clonally expanded, and mutated population persisted and was even more pronounced when peripheral B cells were depleted prior to autoantigen induction in the CNS. In contrast, the most expanded B cell clones in mice with persistent expression of LCMV GP in the CNS did not exhibit neo-self antigen specificity, potentially a consequence of local tolerance induction. Finally, a comparable population of clonally expanded, class-switched, proliferating ASCs was detected in the cerebrospinal fluid of multiple sclerosis patients. Taken together, our findings support the existence of B cells that populate the CNS and are capable of responding to locally encountered autoantigens. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗

bioRxiv2022

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

Adaptive immune repertoires are composed by the ensemble of B and T cell receptors (BCR, TCR) within an individual and reflect both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Such high-dimensional datasets enable the molecular quantification of clonal selection of B and T cells across a wide variety of conditions such as infection and disease. Due to costs, time required for the analysis and current practices of academic publishing, small-scale sequencing studies are often not made publicly available, despite having informative potential to elucidate immunological principles and guide future-studies. Here, we performed single-cell sequencing of B and T cells to profile clonal selection across murine models of viral infection and autoimmune disease. Specifically, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following acute and chronic viral infection, CD8+ T cells with binding specificity restricted to two distinct peptides of lymphocytic choriomeningitis virus, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate repertoire features such as clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T cell inflation, and regulation. Together, this dataset provides a resource for experimental and computational immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets.

immunology↗

bioRxiv2021

Predictive profiling of SARS-CoV-2 variants by deep mutational learning

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1-4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

synthetic biology↗

bioRxiv2021

B cell clonal expansion is correlated with antigen-specificity in young but not old mice

Murine models of immunization have played a major role in discovering antibody candidates against therapeutic targets. It nevertheless remains time-consuming and expensive to identify antibodies with diverse binding modalities against druggable candidate molecules. Although new genomics-based pipelines have potential to augment antibody discovery, these methods remain in their infancy due to an incomplete understanding of the selection process that governs B cell clonal selection, expansion and antigen specificity. Furthermore, it remains unknown how factors such as aging and reduction of tolerance influence B cell selection in murine models of immunization. Here we perform single-cell sequencing of antibody repertoires and transcriptomes of B cells following immunizations with a model therapeutic antigen target (human Tumor necrosis factor receptor 2, TNFR2). We determine the relationship between antibody repertoires, gene expression signatures and antigen specificity across 100,000 B cells. Recombinant expression and characterization of 227 monoclonal antibodies revealed the existence of clonally expanded and class-switched antigen-specific B cells that were more frequent in young mice. Although integrating multiple repertoire features such as germline gene usage, somatic hypermutation, and transcriptional signatures failed to distinguish antigen-specific from non-specific B cells, other features such as IgG-subtype and sequence composition correlated with antigen-specificity. This work provides a single-cell resource for B cells relating antibody repertoires, transcriptomes and antigen specificity.

immunology↗

bioRxiv2021

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Single-cell sequencing now enables the recovery of full-length immune repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate a comprehensive ground truth single-cell dataset that incorporates both immune receptor repertoires and gene expression. Therefore, we developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution. Our simulation tool generates annotated single-cell sequencing data with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, and transcriptional phenotypes. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. Finally, we demonstrate the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/452792v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1c9930eorg.highwire.dtl.DTLVardef@d5a50eorg.highwire.dtl.DTLVardef@47584forg.highwire.dtl.DTLVardef@1f6bb17_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Phenotypic determinism and stochasticity in antibody repertoires of clonally expanded plasma cells

The capacity of humoral B cell-mediated immunity to effectively respond to and protect against pathogenic infections is largely driven by the presence of a diverse repertoire of polyclonal antibodies in the serum, which are produced by plasma cells (PCs).1,2 Recent studies have started to reveal the balance between deterministic mechanisms and stochasticity of antibody repertoires on a genotypic level (i.e., clonal diversity, somatic hypermutation, germline gene usage).3-8 However, it remains unclear if clonal selection and expansion of PCs follows any deterministic rules or is stochastic with regards to phenotypic antibody properties (i.e., antigen-binding, affinity, epitope specificity). Here we report on the in-depth genotypic and phenotypic characterization of clonally expanded PC antibody repertoires following protein immunization. We find that there is only a strong correlation with antigen-specificity among the most expanded clones (top ~ 10), whereas among the rest of the clonal repertoire antigen-specificity is stochastic. Furthermore, we report both on a polyclonal repertoire and clonal lineage level that antibody-antigen binding affinity does not correlate with clonal expansion or somatic hypermutation. Lastly, we provide evidence for convergence towards dominant epitopes despite clonal sequence diversity among the most expanded clones. Our results highlight the extent to which clonal expansion can be ascribed to antigen binding, affinity and epitope specificity and they have implications for the assessment of effective vaccines. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/452687v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@e907c1org.highwire.dtl.DTLVardef@fc45b6org.highwire.dtl.DTLVardef@44bd91org.highwire.dtl.DTLVardef@f98f3e_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Clonally expanded virus-specific CD8 T cells acquire diverse transcriptional phenotypes during acute, chronic, and latent infections

CD8+ T cells play a crucial role in the control and resolution of viral infections and can adopt a wide range of phenotypes and effector functions depending on the inflammatory context and the duration and extent of antigen exposure. Similarly, viral infections can exert diverse selective pressures on populations of clonally related T cells. Technical limitations have nevertheless made it challenging to investigate the relationship between clonal selection and transcriptional phenotypes of virus-specific T cells. We therefore performed single-cell T cell receptor (TCR) repertoire and transcriptome sequencing of virus-specific CD8 T cells in murine models of acute, chronic and latent infection. We observed clear infection-specific populations corresponding to memory, effector, exhausted, and inflationary phenotypes. We further uncovered a mouse-specific and polyclonal T cell response, despite all T cells sharing specificity to a single viral epitope, which was accompanied by stereotypic TCR germline gene usage in all three infection types. Persistent antigen exposure during chronic and latent viral infections resulted in a higher proportion of clonally expanded T cells relative to acute infection. We furthermore observed a relationship between transcriptional heterogeneity and clonal expansion for all three infections, with highly expanded clones having distinct transcriptional phenotypes relative to lowly expanded clones. Finally, we developed and utilized a bioinformatic pipeline integrating pseudotime and clonality, termed Clonotyme, to further support a model in which expanded virus-specific CD8+ T cells adopt heterogenic, yet preferentially, effector-like phenotypes. Together our work relates clonal selection to gene expression in the context of viral infection and further provides a dataset and accompanying software for the immunological community.

immunology↗

bioRxiv2021

Discovery and validation of novel human genomic safe harbor sites for gene and cell therapies

Existing approaches for the integration and expression of genes of interest in a desired human cellular context are marred by the safety concerns related to either the random nature of viral-mediated integration or unpredictable pattern of gene expression in currently employed targeted genomic integration sites. Disadvantages of these methods lead to their limited use in clinical practice, thus encouraging future research in identifying novel human genomic sites that allow for predictable and safe expression of genes of interest. We conducted a bioinformatic search followed by experimental validation of novel genomic sites and identified two that demonstrated stable expression of integrated reporter and therapeutic genes without detrimental changes to cellular transcriptome. The cell-type agnostic criteria used in our bioinformatic search suggest wide-scale applicability of our sites for engineering of a diverse range of tissues for therapeutic as well as enhancement purposes, including modified T-cells for cancer therapy and engineered skin to ameliorate inherited diseases and aging. Additionally, the stable and robust levels of gene expression from identified sites allow for their use in industry-scale biomanufacturing of desired proteins in human cells.

genomics↗

bioRxiv2021

A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8+ T cell population. Highly expanded CD8+ and CD4+ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

immunology↗

bioRxiv2021

Single-cell sequencing of plasma cells from COVID-19 patients reveals highly expanded clonal lineages produce specific and neutralizing antibodies to SARS-CoV-2

Isolation and characterization of antibodies in COVID-19 patients has largely focused on memory B cells, however it is the antibody-secreting plasma cells that are directly responsible for the production of serum antibodies, which play a critical role in controlling and resolving SARS-CoV-2 infection. To date there is little known about the specificity of plasma cells in COVID-19 patients. This is largely because plasma cells lack surface antibody expression, which complicates their screening. Here, we describe a technology pipeline that integrates single-cell antibody repertoire sequencing and high-throughput mammalian display screening to interrogate the specificity of plasma cells from 16 convalescent COVID-19 patients. Single-cell sequencing allows us to profile antibody repertoire features in these patients and identify highly expanded clonal lineages. Mammalian display screening is employed to reveal that 37 antibodies (out of 132 candidates) derived from expanded plasma cell clonal lineages are specific for SARS-CoV-2 antigens, including antibodies that target the receptor binding domain (RBD) with high affinity and exhibit potent neutralization of SARS-CoV-2. One Sentence SummarySingle-cell antibody repertoire sequencing and high-throughput screening identifies highly expanded plasma cells from convalescent COVID-19 patients that produce SARS-CoV-2-specific antibodies capable of potent neutralization.

immunology↗