Chronic Lung Allograft Dysfunction (CLAD) is a critical post-transplant complication that predominantly determines the long-term survival rates and quality of life of patients undergoing lung transplantation. The limited efficacy of current immunosuppressive strategies underscores our incomplete understanding of the immunological aspects of CLAD. Hence, there is an urgent need for more comprehensive and targeted research to unravel the complex interplay of immune cells in the development and progression of CLAD. This study conducts an in-depth analysis of the immune environment in CLAD. By examining the gene expression profiles of T cells, natural killer cells, B cells, macrophages, and monocytes, we have elucidated a unique immunological landscape in CLAD compared to healthy controls. We highlight the heterogeneity within the immune populations and provide a comprehensive understanding of the immune mechanisms driving CLAD. Enrichment analysis identified specific pathways that are either overactive or suppressed in CLAD, revealing potential molecular targets for therapeutic intervention. Our findings emphasize the crucial role of T cells in the pathophysiology of CLAD, coordinating the immune response and revealing an amplified immune cell network, potentially leading to maladaptive tissue responses. By integrating a comprehensive cellular and molecular portrait of the immune environment, our research not only deepens our understanding of the pathogenesis of CLAD but also lays a foundational approach for the development of targeted therapies.
Keywords: Convolutional neural network; Deep learning; Enrichment analysis; Lung; Single-cell.
Copyright © 2024. Published by Elsevier Ltd.