# Automating the Discovery of Clinically-Relevant Intracellular Signaling Responses in Immune Cell-Types

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $219,336

## Abstract

Project Summary
Single-cell immune profiling technologies, such as cytometry by time of flight (CyTOF) enable broad and
comprehensive characterization of diverse immune cell-types. Moreover, such technologies are being
increasingly applied in clinical settings to gain a holistic view of the immune system. Ex vivo stimulation is a
common perturbation applied to immune cells and assayed through CyTOF, which elicits functional responses
that may be clinically predictive. Such experiments generate single-cell measurements for a large number of
cells, causing manual analysis to become time-consuming and biased towards studying immune cell-types
and their functional responses that have already been well-characterized. Existing bioinformatics approaches
for automating manual analysis are limited in that they 1) primarily focus only on partitioning cells into cohesive
cell-populations, 2) need to be run independently per stimulation and 3) produce several immunological
features encoding cell-type specific functional responses to stimulation that are not indicative of canonical
immune signaling pathways. In this proposal, we introduce a fully automated approach for automating the
analysis of multi-sample, multi-stimulation immune profiling data. In particular, we shall develop algorithms to
efficiently identify clinically-predictive functional responses to stimulation in a scalable manner to enable
analysis of large clinical cohorts under several stimulation conditions. Uncovered functional responses that are
clinically predictive can be used to develop diagnostic tests or to design vaccines to elicit particular cellular
responses.

## Key facts

- **NIH application ID:** 10898838
- **Project number:** 5R21AI171745-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Natalie M Stanley
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $219,336
- **Award type:** 5
- **Project period:** 2023-08-03 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898838

## Citation

> US National Institutes of Health, RePORTER application 10898838, Automating the Discovery of Clinically-Relevant Intracellular Signaling Responses in Immune Cell-Types (5R21AI171745-02). Retrieved via AI Analytics 2026-06-23 from https://api.ai-analytics.org/grant/nih/10898838. Licensed CC0.

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