Template-Choice Bias in Deep Sequencing of Antibody Phage Display Libraries

Background/Objectives: Deep sequencing of antibody phage-display selections commonly uses intracellular double-stranded phagemid DNA (dsDNA), although selected libraries are propagated as virions containing single-stranded phagemid DNA (ssDNA). Whether these two templates yield equivalent quantitative profiles remains uncertain. Methods: We sequenced matched intracellular dsDNA and virion ssDNA from a single-chain variable fragment library at the input and after each of four rounds of bio-panning. Results: A total of 1052 ssDNA-derived and 1093 dsDNA-derived unique heavy-chain complementarity-determining region 3 (HCDR3) clonotypes were analyzed, including 1003 shared clonotypes. The dsDNA and ssDNA profiles were closely concordant before strong enrichment, with Spearman correlations of 0.972–0.978 and Morisita–Horn overlap of 0.994–0.998 through round 2. Once a small number of clonotypes began to dominate the selection, however, the two profiles separated sharply: abundance-weighted overlap fell to 0.501 at round 3 and 0.379 at round 4, whereas rank order was preserved and the rank correlation remained high throughout (ρ ≥ 0.90). This divergence was concentrated among enriching trajectories, in which dominant clonotypes reached substantially higher relative abundance in virion ssDNA than in intracellular dsDNA. Antigen-reactive clonotypes were also preferentially shifted toward the ssDNA-biased fraction, with a median log10(ssDNA/dsDNA) ratio of 0.44 compared with −0.17 for non-reactive clonotypes at round 3 (p = 6.3 × 10−8) and round 4 (0.41 vs. −0.12, p = 1.1 × 10−6). Conclusions: dsDNA and ssDNA tell nearly the same story early in bio-panning but can provide markedly different accounts once selection becomes concentrated. In this selection campaign, template choice materially affected interpretation of antibody phage-display sequencing, suggesting that virion ssDNA or paired-template analysis may be preferable to dsDNA alone when late-round abundance is used for candidate prioritization. Because these results come from a single campaign against one antigen, they are descriptive of this dataset and require validation across antigens, scaffolds and display systems.