# Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $254,250

## Abstract

Project Summary
Influenza virus infection is a recurrent health and economic burden. It cycles between the human
population and the animal reservoir, causing millions of hospitalizations and thousands of deaths
each year, especially in high-risk groups, such as young children, pregnant women, obese,
individuals with compromised immune system and indigenous populations. Disease morbidity and
mortality increase when a new influenza strain reasserts or jumps the host, and becomes capable of
infecting humans. In this case, there is no (or minimal) pre-existing antibody-mediated immunity to the
new viral strain at the population level, leading to millions of infections and a rapid global spread of
the virus. In the absence of antibodies, the severity of the disease can be ameliorated by broadly
cross-reactive cellular immunity. But, the precise mechanism of how immune cells mediate recovery
in some individuals, but not others, is far from clear. However, a diverse and rich collection of
datasets are available in the public domain that have already addressed specific aspects of these
concerns. Expression profiles from human cohorts and animal studies in GEO/SRA, immunological
profiles in ImmPort or influenza strain data and interaction with immune epitopes in the Influenza
Research Database (IRD), a Bioinformatics Resource Center (BRC) of NIAID, are examples of such
resources. In particular, high-resolution single-cell RNA-seq data enables us to study relevant
processes during influenza infection in great detail. The combination of multiple previously collected
datasets, in particular across biological scales, single cell and bulk data, is a central goal in this
research. The overarching hypothesis that guides our proposed work is that diversity in influenza
virus strains, genetic immune epitopes and in the responding immune cell population contributes to
the diverse outcome after influenza infection. In detail we will address the questions about
determinants of influenza infections, and key processes that impede any replication, on the one hand,
or contribute to a weak immune response, on the other hand. Sex as biological factor will be
addressed whenever appropriate data with sufficient sample-size is available. We will further develop
an approach to increase the resolution of bulk data by guidance of single cell data. For this purpose,
we will not only develop multi-scale models of high resolution and detail but also develop the
appropriate tools to facilitate and enable such precision modeling.

## Key facts

- **NIH application ID:** 10057816
- **Project number:** 1R21AI149013-01A1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** CHRISTIAN FORST
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $254,250
- **Award type:** 1
- **Project period:** 2020-07-10 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057816

## Citation

> US National Institutes of Health, RePORTER application 10057816, Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections (1R21AI149013-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10057816. Licensed CC0.

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