# Immune Data Science Core (IDS Core)

> **NIH NIH P01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $377,918

## Abstract

ABSTRACT – IMMUNE DATA SCIENCE (IDS) CORE
The Immune Data Science (IDS) Core will be responsible for analysis of high-dimensional immune data sets
generated by Projects 1-3 and the Model Systems (MODS) Core. The objectives of the analysis are to
address 1) viral factors contributing to transmission, 2) immune factors contributing to blocking transmission, 3)
efficacy of passive immunization on blocking transmission, 4) efficacy of specific vaccines on blocking
transmission, 5) immunogenicity of specific vaccines, and 6) biomarkers associated with outcomes.
To address these objectives, we need to consider the data, outcomes, and experimental context. Data comes
in several categories that require different processing and analytic approaches: bulk (multiplexed cytokines,
antibody concentration, antibody function), single cell (flow cytometry, scRNA-seq), antigen receptor repertoire
(scTCR-seq), and spatial (spatial proteomics, spatial transcriptomics). The primary outcome is viral
transmission, defined as detectable CMV by qPCR on ൒ 2 technical replicates, but there are several other
outcomes of interest: maternal plasma viral load over time, shedding in urine and saliva, and dissemination
across multiple tissues and organ systems. Each set of data and outcomes comes with a specific context: viral
strain, immunocompetent or CD4 T cell-depleted Rhesus macaques (RM), and treatment (none, specific
vaccine, or HIG) administered.
The first aim (SA1) will develop computational biology pipelines for the processing, quality control, and
exploratory analysis of immune data sets only. SA1 will focus on 1) single cell data science applied to flow
cytometry and scRNA-seq data sets to identify immune cell types and their gene expression profiles, 2) antigen
receptor diversity analysis to identify abundant clonotypes related to their gene expression profiles and
immunodominant CMV epitopes, and 3) spatial biology, especially co-localization of virus and immune cells.
The computational biology pipelines will also generate informative features from these immune assays to be
used for downstream analysis. The second aim (SA2) will apply or develop statistical models to address the six
analytic objectives, which require linking data, context, and outcomes. In addition to standard statistical
analysis described in Projects 1-3, but centrally executed by the IDS Core for consistency, SA2 will focus on
1) developing a hierarchical Bayesian model to model the complex conditional dependency structure of viral
dissemination experiments, 2) extension of our powerful COMPASS framework for identification of cellular
immune correlates to include bulk data, and 3) hypothesis testing to rigorously evaluate spatial patterns of
variation and co-localization that inform us about viral transmission and vaccine mechanisms.
Overall, the IDS Core will serve as the quantitative hub of the overall Program, providing the quantitative
expertise and resources to analyze and interpret the com...

## Key facts

- **NIH application ID:** 10874237
- **Project number:** 2P01AI129859-07
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Cliburn C Chan
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $377,918
- **Award type:** 2
- **Project period:** 2019-07-24 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874237

## Citation

> US National Institutes of Health, RePORTER application 10874237, Immune Data Science Core (IDS Core) (2P01AI129859-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10874237. Licensed CC0.

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