中国P站

Single-cell RNA sequencing; Transplant immunology; Allograft rejection; Immune profiling; Precision medicine; Organ transplantation; Graft survival; Cellular heterogeneity; Rejection biomarkers; Immune cell mapping

Introduction

Allograft rejection continues to pose a major barrier in organ transplantation, limiting long-term graft function and survival. Current diagnostic tools, including histopathology and conventional biomarkers, often fail to detect rejection at its earliest and most reversible stages. As the field seeks more precise, cell-level insights into immune responses, single-cell RNA sequencing (scRNA-seq) has emerged as a revolutionary technology [1-5]. This technique allows researchers to study gene expression profiles at the resolution of individual cells, revealing cellular diversity and dynamics that were previously inaccessible. By capturing the transcriptomic landscape of immune cells involved in rejection, scRNA-seq offers a powerful approach to dissect the complexity of the transplant immune response. Its use in recent transplant studies has unveiled new immune cell subtypes and molecular pathways that contribute to both acute and chronic allograft rejection [6-10].

Discussion

The ability of scRNA-seq to analyze thousands of individual cells from a graft or blood sample has opened new avenues for understanding immune-mediated injury. In transplant settings, this technology has helped identify distinct populations of T cells, macrophages, and dendritic cells with pro-inflammatory or regulatory roles. Notably, specific gene signatures associated with cytotoxic T lymphocytes and memory B cells have been linked with episodes of acute cellular rejection. This level of granularity allows for immune profiling that can distinguish between harmful and tolerant responses. Furthermore, longitudinal studies using scRNA-seq have enabled the tracking of cellular evolution and immune activation over time, providing insights into the mechanisms that drive graft failure or acceptance.

Incorporating scRNA-seq data with spatial transcriptomics enhances the contextual understanding of immune cell localization and interaction within the tissue microenvironment. This approach helps map immune infiltration zones and identify potential targets for localized immunosuppressive strategies. The identification of rejection-related biomarkers, such as chemokines, transcription factors, and surface molecules, has the potential to inform personalized immunosuppressive therapy and predict rejection risk before clinical symptoms arise. Importantly, scRNA-seq also plays a role in studying tolerance mechanisms, by identifying regulatory T cells and other immunomodulatory subsets that are associated with long-term graft acceptance. These findings pave the way for new therapeutic interventions focused on immune modulation.

Despite its immense potential, the clinical application of scRNA-seq still faces challenges. The high cost of sequencing, complexity of data analysis, and requirement for advanced computational infrastructure can limit its immediate clinical integration. However, as sequencing technologies become more affordable and user-friendly bioinformatics tools are developed, scRNA-seq is expected to become more feasible in routine transplant monitoring. Collaboration among transplant clinicians, immunologists, and computational scientists will be essential to validate results, standardize methodologies, and bridge the gap between bench and bedside.

Conclusion

Single-cell RNA sequencing is transforming the landscape of transplant immunology by providing a high-resolution view of the cellular players involved in allograft rejection. Its ability to dissect the immune response at the single-cell level allows for earlier diagnosis, improved risk stratification, and the identification of novel therapeutic targets. As the technology continues to evolve, its integration with clinical practice holds the promise of precision transplant medicine, where treatment decisions are guided by individualized immune profiles. Embracing scRNA-seq in both research and clinical environments could dramatically improve graft outcomes and patient survival. Ultimately, it represents a pivotal step forward in achieving more durable, rejection-free organ transplantation in the future.

References

1. Delgado JF, Reyne AG, Dios S, López-Medrano F, Jurado A, et al. (2015) Influence of cytomegalovirus infection in the development of cardiac allograft vasculopathy after heart transplantation. J Heart Lung Transplant 3:1112-1119.

   Indexed at, Google Scholar, Crossref

2. Raffa GM, Di Gesaro G, Sciacca S, Tuzzolino F, Turrisi M, et al. (2016) Heart transplant program at IRCCS-ISMETT: Impact of mechanical circulatory support on pre- and post -transplant survival. Int J Cardiol 219: 358-361.

   Indexed at, Google Scholar, Crossref

3. Zieli艅ska K, Kukulski L, Wróbel M, Przyby艂owski P, Rokicka D, et al. (2022) Carbohydrate Metabolism Disorders in Relation to Cardiac Allograft Vasculopathy (CAV) Intensification in Heart Transplant Patients According to the Grading Scheme Developed by the International Society for Heart and Lung Transplantation (ISHLT). Ann Transplant 27: 933420.

   Indexed at, Google Scholar, Crossref

4. Conway J, Manlhiot C, Kirk R, Edwards LB, McCrindle BW, et al. Mortality and morbidity after retransplantation after primary heart transplant in childhood: an analysis from the registry of the International Society for Heart and Lung Transplantation. J Heart Lung Transplant 33: 241-51.

   Indexed at, Google Scholar, Crossref

5. Vanderlaan RD, Manlhiot C, Edwards LB, Conway J, McCrindle BW, et al. (2015) Risk factors for specific causes of death following pediatric heart transplant: An analysis of the registry of the International Society of Heart and Lung Transplantation. Pediatr Transplant 19: 896-905.

   Indexed at, Google Scholar, Crossref

6. Kitamura S (2012) Heart transplantation in Japan: a critical appraisal for the results and future prospects. Gen Thorac Cardiovasc Surg 60: 639-644.

   Indexed at, Google Scholar, Crossref

7. Wever-Pinzon O, Edwards LB, Taylor DO, Kfoury AG, Drakos SG, et al. (2017) Association of recipient age and causes of heart transplant mortality: Implications for personalization of post-transplant management-An analysis of the International Society for Heart and Lung Transplantation Registry. J Heart Lung Transplant 36: 407-417.

   Indexed at, Google Scholar, Crossref

8. Saczkowski R, Dacey C, Bernier PL (2010) Does ABO-incompatible and ABO-compatible neonatal heart transplant have equivalent survival. Interact Cardiovasc Thorac Surg 10: 1026-1033.

   Indexed at, Google Scholar, Crossref

9. Jeewa A, Manlhiot C, Kantor PF, Mital S, McCrindle BW, et al. (2014) Risk factors for mortality or delisting of patients from the pediatric heart transplant waiting list. J Thorac Cardiovasc Surg 147: 462-468.

   Indexed at, Google Scholar, Crossref

10. Sivathasan C, Lim CP, Kerk KL, Sim DK, Mehra MR, et al. (2017) Mechanical circulatory support and heart transplantation in the Asia Pacific region. J Heart Lung Transplant 36: 13-18.

    Indexed at, Google Scholar, Crossref