Revisiting transplant immunology through the lens of single-cell technologies.
Revisiting transplant immunology through the lens of single-cell technologies.
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DOI：
10.1007/s00281-022-00958-0
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发表时间：
2023-01
影响因子：
9
通讯作者：
Emamaullee, Juliet
中科院分区：
文献类型：
--
作者：
Barbetta, Arianna;Rocque, Brittany;Sarode, Deepika;Bartlett, Johanna Ascher;Emamaullee, Juliet
实体器官移植（SOT）是终末期器官疾病的标准治疗方法。 SOT 最常见的并发症涉及同种异体移植排斥，这可能通过 T 细胞和/或抗体介导的机制发生。在临床环境中诊断排斥反应需要进行侵入性活检，因为目前没有可靠的生物标志物来检测排斥反应。同样，实际上不可能识别表现出操作耐受性并且可能是减少或完全撤除免疫抑制的候选者的患者。新兴的单细胞技术，包括飞行时间流式细胞仪 (CyTOF)、成像质谱流式细胞术和单细胞 RNA 测序，为深入表征临床样本中参与同种异体移植排斥和耐受的病原性免疫群体提供了新的机会。这些技术能够检查个体细胞表型和细胞间相互作用，最终为同种异体移植排斥的复杂病理生理学提供新的见解。然而，处理这些大型、高维数据集需要使用计算生物学技术进行高级数据处理和分析的专业知识。机器学习算法代表了使用这些复杂数据集分析和创建预测模型的最佳策略，并且可能对于基于单细胞数据的患者水平结果的未来临床应用至关重要。在此，我们回顾了 SOT 背景下有关单细胞技术的现有文献。在线版本包含可在 10.1007/s00281-022-00958-0 获取的补充材料。
Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell– and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT. The online version contains supplementary material available at 10.1007/s00281-022-00958-0.
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