New insights on the monitoring of solid-organ allografts based on immune cell signatures.

Attaining a fair long-term allograft survival remains a challenge for allogeneic transplantation worldwide. Although the emergence of immunosuppressants has caused noticeable progress in the management of immunologic rejection, proper application of these therapeutics and dose adjustments require delicate and real-time monitoring of recipients. Nevertheless, the majority of conventional allograft monitoring approaches are based on organ damage or functional tests that render them unable to predict the rejection events in early time points before the establishment of a functional alloimmune response. On the other hand, biopsy-based methods include invasive practices and are accompanied by serious complications. In recent years, there have been a myriad of attempts on the discovery of reliable and non-invasive approaches for the monitoring of allografts that regarding a close relationship between allografts and hosts' immune system, most of the attempts have been devoted to the studies on the immune response-associated biomarkers. The discovery of gene and protein expression patterns in immune cells along with their phenotypic characterization and secretome analysis as well as tracking the immune responses in allograft tissues and clinical specimens are among the notable attempts taken to discover the non-invasive predictive markers with a proper coincidence to the pathologic condition. Collectively, these studies suggest a list of candidate biomarkers with ideal potentials for early and non-invasive prediction of allograft rejection and shed light on the way towards developing more standardized and reproducible approaches for monitoring the allograft rejection. Copyright © 2021. Published by Elsevier B.V.

Citation

Alireza Mardomi, Seyed Bagher Naderi, Sepideh Zununi Vahed, Mohammadreza Ardalan. New insights on the monitoring of solid-organ allografts based on immune cell signatures. Transplant immunology. 2022 Feb;70:101509

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PMID: 34843937

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