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表型 T 细胞分化分析:原发性免疫缺陷研究中的诊断和预测工具。

Phenotypical T Cell Differentiation Analysis: A Diagnostic and Predictive Tool in the Study of Primary Immunodeficiencies.

机构信息

Unit of Immunology and Infectious Diseases, Academic Department of Pediatrics, Bambino Gesù Children's Hospital, Rome, Italy.

Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

出版信息

Front Immunol. 2019 Nov 29;10:2735. doi: 10.3389/fimmu.2019.02735. eCollection 2019.

Abstract

Multiparametric flow cytometry (MFC) represents a rapid, highly reproducible, and sensitive diagnostic technology for primary immunodeficiencies (PIDs), which are characterized by a wide range of T cell perturbations and a broad clinical and genetic heterogeneity. MFC data from CD4+ and CD8+ T cell subsets were examined in 100 patients referred for Primary Immunodeficiencies to our center. Naïve, central memory, effector memory, and terminal effector memory cell differentiation stages were defined by the combined expression CD45RA/CD27 for CD4 and CD45RA/CCR7 for CD8. Principal component analysis (PCA), a non-hypothesis driven statistical analysis, was applied to analyze MFC data in order to distinguish the diverse PIDs. Among severe lymphopenic patients, those affected by severe combined and combined immunodeficiency (SCID and CID) segregated in a specific area, reflecting a homogenous, and a more severe T cell impairment, compared to other lymphopenic PID, such as thymectomized and partial DiGeorge syndrome patients. PID patients with predominantly antibody defects were distributed in a heterogeneous pattern, but unexpectedly PCA was able to cluster some patients' resembling CID, hence warning for additional and more extensive diagnostic tests and a diverse clinical management. In conclusion, PCA applied to T cell MFC data might help the physician to estimate the severity of specific PID and to diversify the clinical and diagnostic approach of the patients.

摘要

多参数流式细胞术(MFC)是一种快速、高度可重复和敏感的原发性免疫缺陷(PID)诊断技术,其特征是 T 细胞广泛紊乱和广泛的临床和遗传异质性。我们中心对 100 名前来就诊的原发性免疫缺陷患者的 CD4+和 CD8+T 细胞亚群进行了 MFC 数据分析。幼稚、中央记忆、效应记忆和终末效应记忆细胞分化阶段通过 CD45RA/CD27 联合表达和 CD45RA/CCR7 联合表达来定义。主成分分析(PCA)是一种非假设驱动的统计分析方法,用于分析 MFC 数据,以区分不同的 PID。在严重淋巴细胞减少症患者中,那些患有严重联合免疫缺陷(SCID)和联合免疫缺陷(CID)的患者在特定区域聚集,反映出更均匀和更严重的 T 细胞损伤,与其他淋巴细胞减少症 PID 患者(如胸腺切除术和部分 DiGeorge 综合征患者)相比。主要抗体缺陷的 PID 患者呈异质性分布,但出人意料的是,PCA 能够聚类一些患者类似 CID,因此需要进行额外和更广泛的诊断测试和多样化的临床管理。总之,应用于 T 细胞 MFC 数据的 PCA 可能有助于医生评估特定 PID 的严重程度,并使患者的临床和诊断方法多样化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a566/6896983/2ac872fcd8dc/fimmu-10-02735-g0001.jpg

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