{"doi":"10.1002/cyto.a.24341","title":"OMIP 074: Phenotypic analysis of <scp>IgG</scp> and <scp>IgA</scp> subclasses on human B cells","abstract":"This panel was designed and optimized to characterize the phenotypic diversity of circulating human memory B cells with an emphasis on discriminating IgA and IgG subclasses. Markers to detect all human immunoglobulin isotypes (IgA, IgD, IgE, IgG, and IgM), subclasses (IgA1, IgA2, IgG1, IgG2, IgG3, and IgG4), and light chains (IgKappa and IgLambda) were used in combination with markers to more precisely delineate memory B cell populations based on CD21 and CD27. This panel has been tested on fresh and cryopreserved peripheral blood mononuclear cells (PBMC) from multiple healthy subjects (Table 1). Antibody discovery research has been instrumental in facilitating the isolation and characterization of therapeutic monoclonal antibodies (mAbs) for cancer [1], autoimmune diseases [2], and infectious diseases, such as influenza and human immunodeficiency virus-1 (HIV-1) [3, 4]. The approaches to human antibody isolation include B cell immortalization, yeast or phage display, single B cell culture, and antigen-specific single B cell sorting [5]. The latter two methods rely on single-cell sorting of memory B cells and typically employ a dump/exclusion gate to remove contaminating T cells (CD3−CD4−CD8−), monocytes/macrophages (CD14−) and dead cells (viability dye), and a staining panel to include B cell markers (CD19+CD20+) and class-switched memory B cell markers (IgD−IgM−IgG+CD27+). Unfortunately, none of these antibody isolation methods are designed to capture B cell immunoglobulin subclass information which is typically assessed by separate analysis of serum or plasma samples by enzyme-linked immunosorbent assay (ELISA). Likewise, flow-based methods to provide a simultaneous readout of both antibody specificity and subclass rely on assaying soluble antibodies present in serum or plasma samples rather than phenotyping B cells directly [6]. Despite relatively minor differences in amino acid sequence, each immunoglobulin subclass has important functional differences with respect to antigen binding and stimulation of Fc receptor-mediated phagocytosis, antibody-dependent cell-mediated cytotoxicity (ADCC), and complement activation [7]. Thus, with growing interest in mining the antibody repertoire to identify functional antigen-specific mAbs from individual B cells, we designed and optimized a panel to capture antibody subclass information at the single-cell level (Figure 1, Table 2, Figures S1 and S2). This panel was developed as part of an antibody isolation pipeline and is primarily used for index sorting of single class-switched or antigen-specific B cells enabling deeper phenotyping of B cells identified to express antigen-specific BCR. We verified the performance of the panel on a sorter (FACSAria III, configuration in Table S1) and an analyzer (FACSymphony, configuration in Table S2) as shown in Figure S3. Therefore, the panel can be used on different platforms equipped with distinct hardware. To maintain compatibility with the analyzer and cell sorter laser and filter configurations, three blue laser detectors, and all ultraviolet laser detectors were excluded from consideration in panel development (Tables S1 and S2). B cells can be defined by expression of the pan B cell markers CD19 and CD20 with the combination facilitating discrimination of plasmablasts (which downregulate CD20 but retain CD19 expression) from all other B cell subsets in the periphery (Figure 1B). While pre-gating of class-switched IgD-IgM- cells is sufficient for further analysis of IgA and IgG subclasses, we also included CD21 and CD27 to delineate additional memory B cell populations, including activated memory (CD21lowCD27+), resting memory (CD21highCD27+), and exhausted memory (CD21lowCD27−) B cells [8] (Figure 1C). We briefly considered the inclusion of markers against T cells, natural killer (NK) cells, and monocytes to increase the purity of cells analyzed and sorted. However, in order to include antibodies against all immunoglobulin isotypes as well ","journal":"Cytometry Part A","year":2021,"id":206646,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":257000,"name":"Reid Ballard","orcid":null,"position":1,"is_corresponding":false},{"id":332024,"name":"Thomas Liechti","orcid":"0000-0001-8738-6679","position":2,"is_corresponding":false},{"id":254837,"name":"Rosemarie D. Mason","orcid":"0000-0001-7123-4902","position":3,"is_corresponding":false},{"id":791060,"name":"Leonard Nettey","orcid":"0000-0002-0387-9327","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-18T23:51:41.571653Z","pmid":"33939254","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}