Back

Single-cell RNA sequencing identifies progenitor dysfunction, inflammation and premature aging in ex vivo airway epithelium-derived from transplant recipients

bondeelle, l.; bezrukov, f.; berra, g.; chalandon, y.; gensous, c.; loison, s.; giannotti, f.; messe, r.; Le Goff, J.; clement, s.; Tapparel, C.; bergeron, a.

2025-12-26 molecular biology
10.64898/2025.12.26.696372 bioRxiv
Show abstract

Pulmonary dysfunction is a common complication following hematopoietic stem cell transplantation (HSCT) or lung transplantation (LT). Bronchiolitis obliterans syndrome (BOS), an alloimmune complication of transplantation, with complex pathophysiology, contributes substantially to morbidity and mortality in these settings. We aimed to identify common epithelial features predisposing to BOS by comparing epithelia from HSCT and LT without BOS and non-transplant (NT) individuals. We developed an ex vivo model of human airway epithelia (HAE) reconstituted from bronchial biopsies from 6 patients of each group. Using single-cell RNA sequencing, we identified two distinct epithelial profiles among transplant recipients: one resembling NT epithelium and another displaying altered cellular composition and transcriptional signatures across basal, suprabasal, club and submucosal basal duct cells, which were even more pronounced in a patient who subsequently developed BOS. This latter subgroup exhibited dysregulation of epithelial-mesenchymal transition, TNF/NFB signaling and inflammatory pathways, suggesting impaired epithelial function. Moreover, these HAE demonstrated premature epithelial aging and increased expression of genes encoding damage associated molecular patterns. Together, these findings indicate that epithelial abnormalities specific to certain transplant recipients may contribute to BOS development. Although causality cannot yet be definitively established, our data highlight the airway epithelium as a site of sustained post-transplant injury and reveal potential molecular mechanisms underlying BOS pathogenesis.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.