# Uncovering the dynamics of human pathogens using high throughput multiplexed serological tools

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $1

## Abstract

PROJECT SUMMARY
Understanding the distribution and dynamics of human pathogens is fundamental to control infectious
diseases by guiding where and when to target resources. Until now, however, efforts to understand
the dynamics of most pathogens have been conducted in isolation, focusing on one specific disease
at a time.
Over the past years, high throughput multiplexed serological assays have become available, allowing
to simultaneously quantify antibody responses against 100s and even 1000s of antigens, from a
single sample. These novel technologies offer unprecedented opportunities to (1) study the dynamics
of multiple pathogens simultaneously, (2) characterize immune profiles of populations, and (3)
evaluate hypotheses around immunological interactions within and between pathogen groups.
Numerous methodological and computational challenges remain in terms of how to make meaningful
epidemiological inferences from these new tools.
We have been developing methods to analyze and estimate key transmission parameters from
multiple sources of data, with a focus on serological assays. This proposal will expand these lines of
work, developing tools and analytic methods necessary to translate the promise of high throughput
serology into mechanistic insights about pathogen dynamics. We will leverage existing collaborations
with researchers at the forefront of these technologies, as well as detailed population studies
(including longitudinal birth cohorts and cross-sectional studies) that have collected and characterized
serum samples to apply and evaluate these platforms in over 10 countries. We aim to develop and
validate a suite of robust computational and analytical pipelines with the goal of (1) reconstructing the
longitudinal evolution of antibody repertoires in individuals within and across pathogen groups as a
function of infection history, and (2) characterizing transmission histories and immune profiles of
populations. In developing our analytical pipelines, we will perform extensive simulation studies to
evaluate the performance of different models under multiple assumptions of how the data is
generated. We will then perform data analysis and inference to reconstruct transmission histories,
susceptibility profiles of populations to multiple pathogens, estimate attack rates, and characterize
longitudinal evolution of antibody responses. This will be the most comprehensive assessment, and
validation, of population and individual serological responses across pathogen groups, performed to
date. Through these projects, we will also develop a suite of open-source software/tools to
disseminate our methods and allow other researchers to analyze their data and gain insight into
immune landscapes population risk profiles.
This project will create tools that use high-throughput multiplexed serological data to answer
fundamental questions on the dynamics and interactions of different pathogen groups and provide
insights that can be used by policymakers to ...

## Key facts

- **NIH application ID:** 10691499
- **Project number:** 5R35GM138361-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Isabel Rodríguez-Barraquer
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2020-09-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10691499

## Citation

> US National Institutes of Health, RePORTER application 10691499, Uncovering the dynamics of human pathogens using high throughput multiplexed serological tools (5R35GM138361-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10691499. Licensed CC0.

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