Unknown

Dataset Information

0

Single-cell transcriptome analysis identifies skin-specific T-cell responses in systemic sclerosis.


ABSTRACT:

Objectives

Although T cells have been implicated in the pathogenesis of systemic sclerosis (SSc), a comprehensive study of T-cell-mediated immune responses in the affected skin of patients with progressive SSc is lacking. Droplet-based single-cell transcriptome analysis of SSc skin biopsies opens avenues for dissecting patient-specific T-cell heterogeneity, providing a basis for identifying novel gene expression related to functional pathways associated with severity of SSc skin disease.

Methods

Single-cell RNA sequencing was performed by droplet-based sequencing (10x Genomics), focusing on 3729 CD3+ lymphocytes (867 cells from normal and 2862 cells from SSc skin samples) from skin biopsies of 27 patients with active SSc and 10 healthy donors. Confocal immunofluorescence microscopy of progressive SSc skin samples validated transcriptional results and visualised spatial localisations of T-cell subsets.

Results

We identified several subsets of recirculating and tissue-resident T cells in healthy and SSc skin that were associated with distinct signalling pathways. While most clusters shared a common gene expression signature between patients and controls, we identified a unique cluster of recirculating CXCL13+ T cells in SSc skin which expressed a T helper follicular-like gene expression signature and that appears to be poised to promote B-cell responses within the inflamed skin of patients.

Conclusions

Current available therapies to reverse or even slow progression of SSc lead to broad killing of immune cells and consequent toxicities, including death. Identifying the precise immune mechanism(s) driving SSc pathogenesis could lead to innovative therapies that selectively target the aberrant immune response, resulting in better efficacy and less toxicity.

SUBMITTER: Gaydosik AM 

PROVIDER: S-EPMC8516708 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6174292 | biostudies-literature
2020-10-20 | GSE99702 | GEO
| S-EPMC4102619 | biostudies-literature
| S-EPMC8289865 | biostudies-literature
| S-EPMC3882906 | biostudies-literature
| S-EPMC5385608 | biostudies-literature
| S-EPMC4776325 | biostudies-literature
| S-EPMC8786804 | biostudies-literature
2021-05-20 | GSE138669 | GEO
| S-EPMC8005427 | biostudies-literature