# Unbiased visualization of human ASC subsets

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $221,755

## Abstract

Abstract:
Plasma cells (PC) are responsible for maintaining serum antibody levels to pathogen. However, a
comprehensive spatial map of PC subsets across tissue compartments in the human body is lacking. As a
consequence, our understanding of global PC population structure across different tissues within individuals
and also between different individuals is murky. Using unique and rare-to-access human tissue resources
along with high-dimensional flow cytometry, will test the hypothesis that PC subset composition can be
predicted by tissue site and that long-lived PC (LLPC) engage survival programs that are tailored to each site
of origin. In Aim 1, we will identify known and novel PC subsets in lymphoid and mucosal tissues from normal
human donors using complex flow cytometry and unbiased computational algorithms. We will integrate
phenotype with signaling status and establish PC functional capabilities. In Aim 2, we will perform Next
Generation RNA-Sequencing and functional assays on long-lived PCs (LLPC) derived from two distinct tissue
sites in order to uncover shared versus tissue-specific survival pathways. Results from this study will reveal the
global population structure of PC subsets responsible for human humoral immunity and provide insights into
how PCs control infections regionally and systemically.

## Key facts

- **NIH application ID:** 10073471
- **Project number:** 5R21AI142744-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Lisa Borghesi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $221,755
- **Award type:** 5
- **Project period:** 2020-01-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10073471

## Citation

> US National Institutes of Health, RePORTER application 10073471, Unbiased visualization of human ASC subsets (5R21AI142744-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10073471. Licensed CC0.

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