# Single cells and TRUST4 reveal immunological features of the HFRS transcriptome

**Authors:** Ran Xiao, Mu Lin, Mubo Liu, Qingqing Ma

PMC · DOI: 10.3389/fmed.2024.1403335 · Frontiers in Medicine · 2024-05-13

## TL;DR

This study uses TRUST4 and single-cell RNA-seq to explore immune features in HFRS, revealing changes in immune cell subsets and communication.

## Contribution

The study introduces a cost-effective method for analyzing TCR and BCR libraries in HFRS using TRUST4 and RNA-seq data.

## Key findings

- TRUST4 enabled the analysis of TCR and BCR clonality and diversity in HFRS patients.
- CellChat identified key cytokine families involved in immune cell communication during HFRS.
- hdWGCNA and trajectory analysis highlighted core genes and developmental stages in immune cells linked to HFRS.

## Abstract

The etiology of hemorrhagic fever with renal syndrome (HFRS) is significantly impacted by a variety of immune cells. Nevertheless, the existing techniques for sequencing peripheral blood T cell receptor (TCR) or B cell receptor (BCR) libraries in HFRS are constrained by both limitations and high costs. In this investigation, we utilized the computational tool TRUST4 to generate TCR and BCR libraries utilizing comprehensive RNA-seq data from peripheral blood specimens of HFRS patients. This facilitated the examination of clonality and diversity within immune libraries linked to the condition. Despite previous research on immune cell function, the underlying mechanisms remain intricate, and differential gene expression across immune cell types and cell-to-cell interactions within immune cell clusters have not been thoroughly explored. To address this gap, we performed clustering analysis on 11 cell subsets derived from raw single-cell RNA-seq data, elucidating characteristic changes in cell subset proportions under disease conditions. Additionally, we utilized CellChat, a tool for cell–cell communication analysis, to investigate the impact of MIF family, CD70 family, and GALECTIN family cytokines—known to be involved in cell communication—on immune cell subsets. Furthermore, hdWGCNA analysis identified core genes implicated in HFRS pathogenesis within T cells and B cells. Trajectory analysis revealed that most cell subsets were in a developmental stage, with high expression of transcription factors such as NFKB and JUN in Effector CD8+ T cells, as well as in Naive CD4+ T cells and Naive B cells. Our findings provide a comprehensive understanding of the dynamic changes in immune cells during HFRS pathogenesis, identifying specific V genes and J genes in TCR and BCR that contribute to advancing our knowledge of HFRS. These insights offer potential implications for the diagnosis and treatment of this autoimmune disease.

## Linked entities

- **Genes:** NFKB1 (nuclear factor kappa B subunit 1) [NCBI Gene 4790], JUN (Jun proto-oncogene, AP-1 transcription factor subunit) [NCBI Gene 3725]
- **Diseases:** HFRS (MONDO:0005784)

## Full-text entities

- **Genes:** MIF (macrophage migration inhibitory factor) [NCBI Gene 4282] {aka GIF, GLIF, MMIF}, CD8A (CD8 subunit alpha) [NCBI Gene 925] {aka CD8, CD8alpha, IMD116, Leu2, p32}, TRBV20OR9-2 (T cell receptor beta variable 20/OR9-2 (non-functional)) [NCBI Gene 6962] {aka CDR3, TCRBV20S2, TCRBV2O, TCRBV2S2O}, CD4 (CD4 molecule) [NCBI Gene 920] {aka CD4mut, IMD79, Leu-3, OKT4D, T4}, CD70 (CD70 molecule) [NCBI Gene 970] {aka CD27-L, CD27L, CD27LG, LPFS3, TNFSF7, TNLG8A}, JUN (Jun proto-oncogene, AP-1 transcription factor subunit) [NCBI Gene 3725] {aka AP-1, AP1, c-Jun, cJUN, p39}
- **Diseases:** autoimmune disease (MESH:D001327), HFRS (MESH:D006480)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

14 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11128564/full.md

## References

33 references — full list in the complete paper: https://tomesphere.com/paper/PMC11128564/full.md

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Source: https://tomesphere.com/paper/PMC11128564