# High-throughput mapping of antigen specificity to B-cell-receptor sequence for characterizing antibody responses in HIV-vaccinated and infected individuals

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $857,109

## Abstract

Project Summary. The search for an effective HIV-1 vaccine remains a top priority, and a deeper
understanding of how the immune system recognizes HIV-1 can help inform vaccine design. Lately, much
effort has focused on understanding antibody responses to HIV-1 infection and vaccination, since antibodies
have proven useful in therapy and prevention, and as templates for antibody-specific vaccine design. While
antibody responses to HIV-1 are polyclonal and complex, advances in next-generation sequencing (NGS)
technologies enable us to see such polyclonal responses at an unprecedented resolution, as a collection of
individual monoclonal antibody sequences. Sequence identification is typically followed by functional antibody
characterization, a primary component of which is the mapping of antigen/epitope specificity.
 A major challenge with the standard antibody analysis pipeline is that the sequence identification and
functional characterization processes for antibodies are generally decoupled. This prevents truly high-
throughput mapping of antibody-antigen specificity, providing only limited information for a small subset of
selected antibody sequences from any given sample. To address this challenge, here we propose to develop a
technology that, for a given sample, will enable the mapping of antibody sequence to antigen specificity from a
single high-throughput experiment. The technology, LIBRA-seq (LInking B-cell Receptor to Antigen specificity
through sequencing), involves physically mixing a B-cell sample with a (theoretically unlimited) pool of
barcoded antigens, thus enabling the simultaneous recovery of: (i) paired heavy-light chain BCR sequences
and (ii) antigen specificity for a given B cell. In particular, this technology development project will broadly focus
on two specific aims: In Specific Aim 1, we will evaluate the effect of different antigen barcoding strategies and
other assay variables on LIBRA-seq accuracy and performance. The goal in this aim is to optimize the LIBRA-
seq ability to accurately detect BCR sequence and antigen specificity from a sequencing experiment. In
Specific Aim 2, we will aim to simultaneously map the target epitope of a given HIV-specific B cell, by
screening a cocktail of antigens with epitope-knockout mutations along with the wildtype antigens. These
efforts will not only lead to the identification of HIV-specific B cells, but will also provide residue-level
information about the specific epitope target on the antigen from the same high-throughput experiment.
 Ultimately, for a given infection or vaccination sample, the LIBRA-seq technology will provide the ability
to recover antibody sequence and antigen specificity for tens to hundreds of thousands of B cells at the single-
cell level. To demonstrate the utility of LIBRA-seq, we will characterize samples from HIV-1 infection and
vaccination cohorts. More generally, LIBRA-seq will be an integral tool for efficient and accurate B-cell
analysis, with the...

## Key facts

- **NIH application ID:** 10478203
- **Project number:** 5R01AI152693-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Ivelin Georgiev
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $857,109
- **Award type:** 5
- **Project period:** 2020-09-02 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478203

## Citation

> US National Institutes of Health, RePORTER application 10478203, High-throughput mapping of antigen specificity to B-cell-receptor sequence for characterizing antibody responses in HIV-vaccinated and infected individuals (5R01AI152693-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10478203. Licensed CC0.

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