# Predicting TCR and BCR specificity to microbiomes by massively mining RNA-seq samples

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2022 · $58,761

## Abstract

T cells and B cells play important roles in our immune system by recognizing various antigens, including 
bacteria and viruses, through their diverse receptors T-cell receptors and B-cell receptors (TCRs and 
BCRs). The total set of TCRs and BCRs in a person is called immune repertoire, and analyzing immune 
repertoire can reveal valuable health information and guide treatment strategies. One of the key steps for 
understanding immune repertoire is to identify the binding target of each TCR and BCR. Researchers have 
developed experimental techniques to capture the receptors recognizing the input antigens. However, the 
profiled antigens on these platforms are very scarce, even when compared with the microbiome species 
with known genome sequences. Thanks to the development of sequencing technology, we can investigate 
the genomic or RNA information at bulk or single-cell level for a large number of samples. In a series of our 
previous works, we demonstrated the ability to extract micro 
biome information and immune repertoire information from sequencing data. With these methods, each 
sample can give a glimpse of the immune repertoire and microbiome interactions, suggesting that we may 
associate the TCRs and BCRs with their binding targets by inspecting sufficient samples. In order to 
efficiently process huge amounts of raw sequencing data sets, we will develop novel computational 
methods that can remarkably reduce the computational overhead. Additionally, we will extend these 
methods to work on a broader scope of sequencing platforms to incorporate more samples in this study. 
After obtaining the immune repertoire and microbiome data across the samples, we will curate the 
resources into a database and develop computational and statistical tools to annotate the user-input TCRs 
and BCRs with their microbiome binding targets.

## Key facts

- **NIH application ID:** 10869852
- **Project number:** 5P20GM130454-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Li Song
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $58,761
- **Award type:** 5
- **Project period:** 2023-05-02 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10869852

## Citation

> US National Institutes of Health, RePORTER application 10869852, Predicting TCR and BCR specificity to microbiomes by massively mining RNA-seq samples (5P20GM130454-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10869852. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*