Evidence map›Paper›PMID 42146107›Full record

ArticleFrontiers in virology (Lausanne, Switzerland)2026

Iterative immunoprecipitation and phage pre-wash dramatically improve epitope-resolved serology by VirScan.

Lily Kjendal, Chase Whelihan, Olivia Garvin, Benjamin Will, Ian Lee, Zachary D Miller, William Dowell, Jacob Dearborn, Sylvester Languon, Tylar Kirch and 4 more

Abstract read
In one paragraph

Article in Frontiers in virology (Lausanne, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Lily KjendalDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Chase WhelihanDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Olivia GarvinDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Benjamin WillDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Ian LeeDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Zachary D MillerDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
William DowellDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Jacob DearbornDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Sylvester LanguonDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Tylar KirchDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Zachary R MillerDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Sophie RoyDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Olivia EvansDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Devdoot MajumdarDepartment of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, United States.

Funding

Using Dengue Controlled Human Infection Model to Identify Adaptive Immune Correlates of ProtectionP20GM125498 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Kristen Pierce · 2018 to 2026
$24.9M
Vermont Immunology/Infectious Diseases Training GrantT32AI055402 · NIAID · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI GARY E WARD · 2005 to 2026
$2.7M
NIAID NIH HHS T32 AI055402NIGMS NIH HHS P20 GM125498
6 · The paper itself

Abstract

Accurate mapping of antibody epitope repertoires is essential for understanding infection, vaccination, and immune history. Phage immunoprecipitation sequencing (PhIP-seq), including the widely used VirScan platform, offers single - peptide resolution across the human virome; however, such measurements are sometimes beset with limitations stemming from weak signal-to-noise ratio, non-specific phage binding, and inconsistent peptide enrichment. With such methods, it is imperative to differentiate true antibody-antigen interactions from background noise. Here, we systematically evaluate the impact of key experimental variables on assay performance and identify two synergistic modifications that markedly improve epitope-level viral serology: (1) iterative rounds of immunoprecipitation and (2) serum pre-washing with wild-type phage. Across healthy donor sera, pooled HIV-seropositive sera, and influenza-vaccinated rabbit sera, the optimized workflow produces a substantial expansion of the enriched peptide population, significantly higher normalized peptide counts, and improved separation of viral epitopes from background noise while preserving global library representation. The protocol enables quantitative detection of HIV epitopes across a 100-fold dilution series, resolving immunodominant gp160 regions, and identifies strain-specific hemagglutinin epitopes elicited by influenza vaccination, including expected public stem-directed linear epitopes. These results provide a reproducible, generalizable workflow that enhances viral epitope discovery, serosurveillance sensitivity, and vaccine-response mapping. This optimized PhIP-seq framework strengthens the utility of VirScan in systems virology by enabling more accurate and quantitative inference of viral antibody repertoires.

Indexed as

antibody repertoireepitope mappingiterative immunoprecipitationphage pre-washPhIP-seqsystems virologyviral serologyVirScan

Identifiers

PMID42146107
PMCPMC13175123

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.