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

Abstract

B cell receptor (BCR) sequencing is a powerful tool for interrogating immune responses to infection and vaccination, but it provides limited information about the antigen specificity of the sequenced BCRs. Here, we present LIBRA-seq (linking B cell receptor to antigen specificity through sequencing), a technology for high-throughput mapping of paired heavy- and light-chain BCR sequences to their cognate antigen specificities. B cells are mixed with a panel of DNA-barcoded antigens so that both the antigen barcode(s) and BCR sequence are recovered via single-cell next-generation sequencing. Using LIBRA-seq, we mapped the antigen specificity of thousands of B cells from two HIV-infected subjects. The predicted specificities were confirmed for a number of HIV- and influenza-specific antibodies, including known and novel broadly neutralizing antibodies. LIBRA-seq will be an integral tool for antibody discovery and vaccine development efforts against a wide range of antigen targets.

Keywords: B cells; HIV; antibodies; antibody discovery; antibody repertoire; broadly neutralizing antibody; influenza; single cell immunology; systems immunology.

Figure 1.. LIBRA-seq assay schematic and validation.

(A.) Schematic of LIBRA-seq assay. (Top left) Fluorescently-labelled, DNA-barcoded antigens are used to (top right) sort antigen-positive B cells before (bottom) co-encapsulation of single B cells with bead-delivered oligos using droplet microfluidics. Bead-delivered oligos index both cellular BCR transcripts and antigen barcodes during reverse transcription, enabling direct mapping of BCR sequence to antigen specificity following sequencing. Note: elements of the depiction are not shown to scale, and the number and placement of oligonucleotides on each antigen can vary. (B.) The assay was initially validated on Ramos B-cell lines expressing BCR sequences of known neutralizing antibodies VRC01 and Fe53 with a three-antigen screening library: BG505, CZA97 and H1 A/New Caledonia/20/99. (C.) Between the minimum (y axis, top) and maximum (y axis, bottom) LIBRA-seq score for each antigen, different cutoffs were tested for their ability to classify each VRC01 cell and Fe53 cell as antigen-positive or -negative, where antigen-positive is defined as having a LIBRA-seq score greater than or equal to the cutoff being evaluated, and antigen-negative is defined as having a LIBRA-seq score below the cutoff. A series of 100 cutoff thresholds between the respective minimum and maximum antigen-specific LIBRA-seq scores were evaluated. At each cutoff, the percent of total VRC01 cells (left column of each antigen subpanel) and percent of total Fe53 cells (right columns) that were classified as positive for a given antigen is represented on a white (0%) to dark purple (100%) color scale. (D.) For each B cell, the LIBRA-seq scores for each pair of antigens was plotted. Each axis represents a range of LIBRA-seq scores for each antigen. Density of total cells is shown, with purple to yellow indicating lowest to highest number of cells, respectively. (E.) The LIBRA-seq score for BG505 (y-axis) and CZA97 (x-axis) for each VRC01 B cell was plotted. Each axis represents a range of LIBRA-seq scores for each antigen. Density of total cells is shown, with purple to yellow indicating lowest to highest number of cells, respectively. See also Figure S1 and S6.

Figure 2.. LIBRA-seq applied to a human B cell sample from HIV-infected donor NIAID45.

(A.) LIBRA-seq experiment setup consisted of three antigens in the screening library: BG505, CZA97, and H1 A/New Caledonia/20/99, and the cellular input was donor NIAID45 PBMCs. (B.) After bioinformatic processing and filtering of cells recovered from single-cell sequencing, the LIBRA-seq score for each antigen was plotted (total, 866 cells). Each axis represents a range of LIBRA- seq scores for each antigen. Density of total cells is shown, with purple to yellow indicating lowest to highest number of cells, respectively. (C.) 29 VRC01 lineage B cells were identified and examined for phylogenetic relatedness to known lineage members and for sequence features, with phylogenetic tree showing relatedness of previously identified VRC01 lineage members (black) and members newly identified using LIBRA-seq (red). Each row represents an antibody. Sequences were aligned using clustalW and a maximum likelihood tree was inferred using maximum likelihood inference. The resulting tree was visualized using an inferred VRC01 unmutated common ancestor (UCA) (accession MK032222) as the root. For each antibody isolated from LIBRA-seq, a heatmap of the LIBRA-seq scores for each HIV antigen (BG505 and CZA97) is shown; a scale of tan-white-purple represents LIBRA-seq scores from −2 to 0 to 2; in this heatmap, scores lower or higher than that range are shown as −2 and 2, respectively. Levels of somatic hypermutation (SHM) at the nucleotide level for the heavy and light chain variable genes as reported by IMGT are displayed as bars, with the numerical percentage value listed to the right of the bar; length of the bar corresponds to level of SHM. Amino acid sequences of the complementarity determining region 3 for the heavy chain (CDRH3) and the light chain (CDRL3) for each antibody are displayed. The tree was visualized and annotated using iTol (Letunic and Bork, 2019). See also Figure S1, S2, and S6.

Figure 3.. Characterization of LIBRA-seq-identified antibodies from donor NIAID45.

(A.) Antigen specificity as predicted by LIBRA-seq was validated by ELISA for a subset of monoclonal antibodies belonging to the VRC01 lineage. Data are represented as mean ± SEM for one ELISA experiment. ELISA data are representative of at least two independent experiments. (B.) Neutralization of Tier 1, Tier 2, and control viruses by VRC01 and newly identified VRC01 lineage members, 2723–3131, 2723–4186, and 2723–3055. IC50 values are shown from high potency (0.0001 μg/ml, red) to low potency (50 μg/ml, green). Lack of neutralization IC50 for concentrations tested is displayed as white. (C.) Sequence characteristics and antigen specificity of newly identified antibodies from donor NIAID45. Percent identity is calculated at the nucleotide level, and CDRH3 and CDRL3 lengths and sequences are noted at the amino acid level. LIBRA-seq scores for each antigen are displayed as a heatmap with a LIBRA-seq score of −2 displayed as light yellow, 0 as white, and a LIBRA-seq score of 2 as purple; in this heatmap, scores lower or higher than that range are shown as −2 and 2, respectively. ELISA binding data against BG505, CZA97, and H1 A/New Caledonia/20/99 is displayed as a heatmap of the AUC analysis calculated from the data in Supplemental Figure 3A with AUC of 0 displayed as light yellow, 50% max as white, and maximum AUC as purple. ELISA data are representative from at least two independent experiments. See also Figure S2 and S3.

Figure 4.. LIBRA-seq applied to a sample from NIAID donor N90.

(A.) LIBRA-seq experiment setup consisted of nine antigens in the screening library: 5 HIV-1 Env (KNH1144, BG505, ZM197, ZM106.9, B41), and 4 influenza HA (H1 A/New Caledonia/20/99, H1 A/Michigan/45/2015, H5 Indonesia/5/2005, H7 Anhui/1/2013), and the cellular input was donor N90 PBMCs. (B.) 18 VRC38 lineage B cells were identified and examined for phylogenetic relatedness to known lineage members as well as for sequence features, with phylogenetic tree showing relatedness of previously identified VRC38 lineage members (black) and members newly identified using LIBRA-seq (red). Each row represents an antibody. Sequences were aligned using clustalW and a maximum likelihood tree was inferred using maximum likelihood inference. The resulting tree was visualized, with a germline-reverted antibody from lineage VRC38 (Methods) as the root. For each antibody isolated from LIBRA-seq, a heat map of the LIBRA-seq scores for each HIV antigen is shown; tan-white-purple represents LIBRA-seq scores from −2 to 0 to 2; in this heatmap, scores lower or higher than that range are shown as −2 and 2, respectively. Levels of somatic hypermutation (SHM) at the nucleotide level for the heavy and light chain variable genes as reported by IMGT are displayed as bars, with the numerical percentage value listed to the right of the bar; length of the bar corresponds to level of SHM. Amino acid sequences of the complementarity determining region 3 for the heavy chain (CDRH3) and the light chain (CDRL3) for each antibody are displayed. The tree was visualized and annotated using iTol (Letunic and Bork, 2019). (C-D) For each combination of (C.) influenza hemagglutinins or (D.) HIV SOSIPs, the number of B cells with high LIBRA-seq scores (>= 1) is displayed as a bar graph. The combinations of antigens are displayed by filled circles, showing which antigens are part of a given combination. Each combination is mutually exclusive. The total number of B cells with high LIBRA-seq scores for each antigen is indicated as a horizontal bar on the bottom left of each subpanel. See also Figure S1, S4, S5, and S6.

Figure 5.. Characterization of LIBRA-seq-identified antibodies from donor NIAID N90.

(A.) Sequence characteristics and antigen specificity of newly identified antibodies from donor N90. Percent identity is calculated at the nucleotide level, and CDR length and sequences are noted at the amino acid level. LIBRA-seq scores for each antigen are displayed as a heatmap with a LIBRA-seq score of −2 displayed as light yellow, 0 as white, and a LIBRA-seq score of 2 as purple; in this heatmap, scores lower or higher than that range are shown as −2 and 2, respectively. ELISA binding data is displayed as a heatmap of the AUC analysis calculated from the data in Supplemental Figure 4B with AUC of 0 displayed as light yellow, 50% max as white, and maximum AUC as purple. ELISA data are representative from at least two independent experiments. (B.) Neutralization of Tier 2 and control viruses by newly identified antibody 3602–870. IC50 values are shown from high potency (0.0001 μg/ml, red) to low potency (50 μg/ml, green). Lack of neutralization IC50 for concentrations tested is displayed as white. (C.) Inhibition of BG505 DS-SOSIP/293F binding to 3602–870 IgG in presence of VRC34 Fab (diamond), PGT145 Fab (square) and VRC01 Fab (triangle). See also Figure S4 and S5.

Figure 6.. Sequence properties of the antigen-specific B cell repertoire.

(A.) IGHV gene identity (y-axis) is plotted for cells with high (>=1) LIBRA-seq scores for any combination of 1 through 5 HIV-1 SOSIP antigens (x-axis). Each distribution is displayed as a kernel density estimation, where wider sections of a given distribution represent a higher probability that B cells possess a given germline identity percentage. The median of each distribution is displayed as a white dot, the interquartile range is displayed as a thick bar, and a thin line extends to 1.5x the interquartile range. The violin ranges were limited to the observed data. Included are cells with IgG or IgA constant heavy genes as determined by Cell Ranger. (B.) Each dot represents an IGHV germline gene, plotted based on the number of B cells reactive to only 1 HIV-1 SOSIP antigen (x axis) and the number of B cells reactive to 3 or more HIV-1 SOSIP antigens (y axis) that are assigned to that respective IGHV germline gene. Only B cells with high (>=1) LIBRA-seq scores for any HIV-1 antigen and with IgG or IgA constant heavy genes as determined by Cell Ranger are shown. See also Figure S5.